122 accepted papers
Assessing Large Language Models (LLMs)' underlying value differences enables comprehensive comparison of their misalignment, cultural adaptability, and biases. Nevertheless, current value measurement methods face the informativeness challenge: with often outdated, contaminated, or generic test quest…
Deploying GRPO on Flow Matching models has proven effective for text-to-image generation. However, existing paradigms typically propagate an outcome-based reward to all preceding denoising steps without distinguishing the local effect of each step. Moreover, current group-wise ranking mainly compare…
With the widespread deployment of large language models (LLMs) in human-computer interaction, dark patterns have extended from traditional visual interfaces to conversational AI systems. While existing research has confirmed the prevalence of dark patterns in LLMs, current evaluation benchmarks face
Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neur
With the widespread deployment of large language models (LLMs), aligning model outputs with pluralistic human values has become an important research problem. Recent approaches that train task-specific experts and merge them through parameter aggregation have shown promise for pluralistic alignment.…
Machine learning methods have been increasingly applied to solve Vehicle Routing Problems (VRPs). A high-efficiency approach is to learn solution construction using deep neural networks. However, their tendency toward premature convergence is a critical barrier, severely hindering generalization acr
Recent unified models have demonstrated that the reasoning capacity of Multimodal Large Language Models (MLLMs) can be leveraged to facilitate diffusion-based image generation with impressive flexibility and performance. However, approaches that rely heavily on MLLMs for high-level semantic encoding
Medical vision-language pre-training (VLP) offers significant potential for advancing medical image understanding by leveraging paired image-report data. However, existing methods are limited by False Negatives (FaNe) induced by semantically similar texts and insufficient fine-grained cross-modal al
Supervised Graph Outlier Detection has long been constrained by severe class imbalance, and although recent diffusion-based augmentation methods have improved sample quality, their practical utility is hindered by the high computational costs of multi-step iterative sampling and the stochasticity of…
Complex activities in real-world audio unfold over extended durations and exhibit hierarchical structure, yet most prior work focuses on short clips and isolated events. To bridge this gap, we introduce MultiAct, a new dataset and benchmark for multi-level structured understanding of human activitie…
Trained on various human-authored corpora, Large Language Models (LLMs) have demonstrated a certain capability of reflecting specific human-like traits (e.g., personality or values) by prompting, benefiting applications like personalized LLMs and social simulations. However, existing methods suffer
The paradigm of Multimodal Large Language Models (MLLMs) offers a promising blueprint for advancing the electromagnetic (EM) domain. However, prevailing approaches often deviate from the native MLLM paradigm, instead using task-specific or pipelined architectures that lead to fundamental limitations
Quantization significantly accelerates inference in large language models (LLMs) by replacing original high-precision matrices with low-precision counterparts. Recent advances in weight-activation quantization have primarily focused on mapping both weights and activations to the INT4 format. Althoug…
Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs
We study experimental designs that balance nonlinear functions of covariates, extending classical methods that primarily target linear balance. Building on the Gram-Schmidt Walk (GSW) framework of Harshaw et al (2024) for linear covariate balancing, we introduce a design that directly controls imbal…
Recent medical MLLMs have made significant progress in generating step by step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate dynamic and iterative focusing on fine-grained visual regions. To close this gap, we introduce Ophiuchus, a versatile, to…
Video diffusion generation suffers from critical sampling efficiency bottlenecks, particularly for large-scale models and long contexts. Existing video acceleration methods, adapted from image-based techniques, lack a single-step distillation ability for large-scale video models and task generalizat
Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment models often sacrifice granular control for expressiveness, while methods designed for control may struggle with fidelity and robust disentanglement. Ins
Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene
Domain generalization remains a critical challenge for deploying neural networks, particularly in out-of-distribution object detection. The distributional discrepancy between training (e.g., daytime-sunny) and the realistic condition (e.g., night-rainy) inevitably produces imprecise localization and
An essential component in Large Language Models (LLMs) is Rotary Position Encoding (RoPE) , which efficiently manages positional dependencies in long-context modeling. However, when the number of input tokens surpasses the pretrained capacity of LLMs, their ability to process and generate text is ma…
We explore Generalizable Tumor Segmentation, aiming to train a single model for zero-shot tumor segmentation across diverse anatomical regions. Existing methods face limitations related to segmentation quality, scalability, and the range of applicable imaging modalities. In this paper, we uncover th…
Aerodynamic coefficient prediction is pivotal in aircraft and vehicles' design, performance evaluation, and motion control. Integrating artificial neural networks into aerodynamic coefficient prediction offers a promising alternative to traditional numerical methods burdened by extensive computation…
Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To…
Recently, spatial and temporal inconsistencies have been shown to effectively enhance the generalization performance of face forgery detection, as common forgery strategies create inconsistencies among face regions and across frames. However, current methods often focus on either spatial or temporal…
Scene Text Retrieval (STR) seeks to identify all images containing a given query string. Existing methods typically rely on an explicit Optical Character Recognition (OCR) process of text spotting or localization, which is susceptible to complex pipelines and accumulated errors. To settle this, we r…
Anomaly detection in continuous-time event sequences is a crucial task in safety-critical applications. While existing methods primarily focus on developing a superior test statistic, they fail to provide guarantees regarding the false positive rate (FPR), which undermines their reliability in pract…
Idioms condense complex semantics into fixed phrases, and their meaning is often not directly connected to the literal meaning of their constituent words, making idiom comprehension a test of metaphor competence. Metaphor, as a cognitive process in human beings, has not yet found an effective evalua…
In this paper, we propose a novel diffusion-based multi-condition controllable framework for video head swapping, which seamlessly transplant a human head from a static image into a dynamic video, while preserving the original body and background of target video, and further allowing to tweak head e…
Open-set counting is garnering increasing attention due to its capability to enumerate objects of arbitrary category. It can be generally categorized into two methodologies: text-guided zero-shot counting methods and exemplar-guided few-shot counting methods. Previous text-guided zero-shot methods o…
Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual instruction tuning (CIT) for LLMs may bring about the catastrophic forgetting (CF) problem, where previously learned abili…
Precisely evaluating semantic alignment between text prompts and generated videos remains a challenge in Text-to-Video (T2V) Generation. Existing text-to-video alignment metrics like CLIPScore only generate coarse-grained scores without fine-grained alignment details, failing to align with human pre…
Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an important class of real-world games from the fields of robotics and security, requires exponential time to be accurately sol…
This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arran…
Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interest to match candidate news. Recent efforts have primarily focused on extracting local subgraph information in a global…
Scattering characteristics of synthetic aperture radar (SAR) targets are typically related to observed azimuth and depression angles. However, in practice, it is difficult to obtain adequate training samples at all observation angles, which probably leads to poor robustness of deep networks. In thi…
Existing unsupervised graph anomaly detection (GAD) methods can be categorized into reconstruction based methods and contrastive learning based methods. The principle of reconstruction methods is to capture anomalous nodes based on data reconstruction errors. However, existing reconstruction methods…
Personalized federated learning (PFL) on graphs is an emerging field focusing on the collaborative development of architectures across multiple clients, each with distinct graph data distributions while adhering to strict privacy standards. This area often requires extensive expert intervention in m…
Latest trajectory prediction models in real-world autonomous driving systems often rely on online High-Definition (HD) maps to understand the road environment.However, online HD maps suffer from perception errors and feature redundancy, which hinder the performance of HD map-based trajectory predict…
Underwater exploration offers critical insights into our planet and attracts increasing attention for its broader applications in resource exploration, national security, etc. We study the underwater scene understanding methods, which aim to achieve automated underwater exploration. The underwater s…
Heterogeneous graphs are increasingly used to represent complex real-world scenarios with diverse entities and interactions by meta structures. Recently, the search of meta structures is combined with graph neural architecture search to automatically extract the semantic knowledge for various tasks…
Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI systems. But is it enough to only have physical reasoning capabilities to operate in a real physical environment? In the real
Although significant progress has been made in audio-driven talking head generation, text-driven methods remain underexplored. In this work, we present OmniTalker, a unified framework that jointly generates synchronized talking audio-video content from input text while emulating the target identity'…
We investigate experimental design for randomized controlled trials (RCTs) with both equal and unequal treatment-control assignment probabilities. Our work makes progress on the connection between the distributional discrepancy minimization (DDM) problem introduced by Harshaw et al. (2024) and the d…
The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as “logically perfect rumors”. This paper aims to unify
An efficient and precise diagnosis of retinal diseases is a fundamental goal for auxiliary diagnostic systems in ophthalmology. Inspired by the importance of scattered subtle lesions in manual retinal disease diagnosis, recent research has achieved state-of-the-art performance by mining information…
Document image retrieval (DIR) aims to retrieve document images from a gallery according to a given query. Existing DIR methods are primarily based on image queries that retrieve documents within the same coarse semantic category, e.g., newspapers or receipts. However, these methods struggle to effe…
Image Copy Detection (ICD) aims to identify manipulated content between image pairs through robust feature representation learning. While self-supervised learning (SSL) has advanced ICD systems, existing view-level contrastive methods struggle with sophisticated edits due to insufficient fine-graine…
Robotic breast ultrasound (RBUS) aims to standardize breast ultrasonography, reduce the workload of sonographers, and provide high-quality ultrasound (US) images for subsequent diagnosis. In the process of RBUS screening, adjusting the US probe correctly and efficiently to acquire high-quality US im
Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typi…
Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm for text retrieval, consequently becoming an essential component in various natural language processing systems. These…
Engineering design methods aim to generate new designs that meet desired performance requirements. Past work has directly introduced conditional Generative Adversarial Networks (cGANs) into this field and achieved promising results in single-point design problems(one performance requirement under on…
Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a m…
Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection. In this paper, from the perspective of human cognitive subjectivity, we introduce the mining of individual latent intentions and propose a novel multi-task learning framework, the…
Audio-visual saliency prediction can draw support from diverse modality complements but further performance enhancement is still challenged by customized architectures as well as task-specific loss functions. In recent studies denoising diffusion models have shown more promising in unifying task fra…
Continual Few-Shot Relation Learning (CFRL) aims to learn an increasing number of new relational patterns from a data stream. However, due to the limited number of samples and the continual training mode, this method frequently encounters the catastrophic forgetting issues. The research on causal in…
Percutaneous puncture is a widely used procedure in the diagnosis and therapy of cancer such as biopsy and ablation operations, while the organs in the thoracic and abdominal cavities are significantly affected by patients' respiratory movement. In this study, a robotic puncture system with respirat
The few-shot tasks require the model to have the ability to generalize from a few samples. However, due to the lack of cognitive ability, the current works cannot fully utilize limited samples to expand the sample space and still suffer from overfitting issues. To address the problems, we propose a…
Recently, path inference-based knowledge graph reasoning (KGR) methods have attracted great attention due to their good performance and interpretability. However, as the number of hops increases, the search space grows exponentially, making the reward sparse and the process of reasoning difficult. T…
Recently link weight prediction has attracted an increasing research interest due to its merits in quantifying the strength between nodes within a graph. Nonetheless, current link weight prediction methods focus solely on graph topology, disregarding node feature information embedded in graphs. In r…
Music recommendation for videos attracts growing interest in multi-modal research. However existing systems focus primarily on content compatibility often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisf…
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduced and gained increasing attention. Nevertheless, existing methods heavily rely on high-quality positive-negative trainin…
We present a novel perspective on temporal point processes (TPPs) by reformulating their intensity processes as solutions to stochastic differential equations (SDEs). In particular, we first prove the equivalent SDE formulations of several classical TPPs, including Poisson processes, Hawkes processe…
Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-efficient computation of PCs is highly interesting for edge computing applications.…
Big models have achieved revolutionary breakthroughs in the field of AI, but they also pose potential ethical and societal risks to humans. Addressing such problems, alignment technologies were introduced to make these models conform to human preferences and values. Despite the considerable advancem…
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a standard diffusion one, PuLID introduces both contrastive alignment loss and accurate ID loss, minimizing disruption to the…
Implicit neural representations have emerged as a powerful paradigm to represent signals such as images and sounds. This approach aims to utilize neural networks to parameterize the implicit function of the signal. However, when representing implicit functions, traditional neural networks such as Re…
Quantum machine learning aims to leverage quantum computing to enhance the computing capabilities and storage efficiency of classical machine learning. Among them, quantum generative models, as a type of unsupervised machine learning, have been proven to learn distributions that are outside of class…
In recent years, researchers have developed novel Quantum-Inspired Neural Network (QINN) frameworks for the Natural Language Processing (NLP) tasks, inspired by the theoretical investigations of quantum cognition. However, we have found that the training efficiency of QINNs is significantly lower th…
Trustworthiness is an essential prerequisite for the real-world application of large language models. In this paper, we focus on the trustworthiness of language models with respect to retrieval augmentation. Despite being supported with external evidence, retrieval-augmented generation still suffers…
"Urban waterlogging poses a major risk to public safety and infrastructure. Conventional methods using water-level sensors need high-maintenance to hardly achieve full coverage. Recent advances employ surveillance camera imagery and deep learning for detection, yet these struggle amidst scarce data…
With the advent of large language models (LLMs), Community Question Answering (CQA) forums offer well-curated questions and answers that can be utilized for instruction-tuning, effectively training LLMs to be aligned with human intents. However, the issue of duplicate questions arises as the volume…
The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly knowledge distillation. However, current knowledge distillation methods rely on the model’s intermediate layer features…
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalitie…
The end-to-end task-oriented dialogue system has achieved great success in recent years. Most of these dialogue systems need to accommodate multi-domain dialogue in real-world scenarios. However, due to the high cost of dialogue data annotation and the scarcity of labeled dialogue data, existing met…
Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world indu…
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model as good as GPT-3 (davinci) and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we fac…
Previous face forgery detection methods mainly focus on appearance features, which may be easily attacked by sophisticated manipulation. Considering the majority of current face manipulation methods generate fake faces based on a single frame, which do not take frame consistency and coordination int…
Transformer has become an important technique for natural language processing tasks with great success. However, it usually requires huge storage space and computational cost, making it difficult to be deployed on resource-constrained edge devices. To compress and accelerate Transformer, we propose…
The StyleGAN family succeed in high-fidelity image generation and allow for flexible and plausible editing of generated images by manipulating the semantic-rich latent style space. However, projecting a real image into its latent space encounters an inherent trade-off between inversion quality and e…
In the era of widespread dissemination through social media, the task of rumor detection plays a pivotal role in establishing a trustworthy and reliable information environment. Nonetheless, existing research on rumor detection confronts several challenges: the limited expressive power of text encod…
Vision Transformers (ViTs) have continuously achieved new milestones in object detection. However, the considerable computation and memory burden compromise their efficiency and generalization of deployment on resource-constraint devices. Besides, efficient transformer-based detectors designed by ex…
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into t…
High-quality datasets are significant to the development of dialogue models. However, most existing datasets for open-domain dialogue modeling are limited to a single language. The absence of multilingual open-domain dialog datasets not only limits the research on multilingual or cross-lingual trans…
One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning. Since in different hops, one attends to different parts of question, it is important to dynamically represent the question semantics for each hop. Existing methods, however, (i) infer the dynamic questi…
Augmenting pre-trained language models (PLMs) with knowledge graphs (KGs) has demonstrated superior performance on commonsense reasoning. Given a commonsense based QA context (question and multiple choices), existing approaches usually estimate the plausibility of candidate choices separately based…
Recently, a lot of research has been carried out to improve the efficiency of Transformer. Among them, the sparse pattern-based method is an important branch of efficient Transformers. However, some existing sparse methods usually use fixed patterns to select words, without considering similarities…
Hand, the bearer of human productivity and intelligence, is receiving much attention due to the recent fever of digital twins. Among different hand morphable models, MANO has been widely used in vision and graphics community. However, MANO disregards textures and accessories, which largely limits it…
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain. The previous sequence-labeling based method focuses on promoting model parameter sharing among domains. However, such a paradigm essentially ignores the domai…
Transformer has been demonstrated effective in Neural Machine Translation (NMT). However, it is memory-consuming and time-consuming in edge devices, resulting in some difficulties for real-time feedback. To compress and accelerate Transformer, we propose a Hybrid Tensor-Train (HTT) decomposition, wh…
Unsupervised cross-domain NER task aims to solve the issues when data in a new domain are fully-unlabeled. It leverages labeled data from source domain to predict entities in unlabeled target domain. Since training models on large domain corpus is time-consuming, in this paper, we consider an altern…
Although previous CNN based face anti-spoofing methods have achieved promising performance under intra-dataset testing, they suffer from poor generalization under cross-dataset testing. The main reason is that they learn the network with only binary supervision, which may learn arbitrary cues overfi…
Autonomous ultrasound scanning robots have attracted the attention of researchers, and the real-time quality assessment of ultrasound images is the key technology of them. Existing robot systems usually use pixel-level feature statistical methods such as grayscale, confidence map, etc. However, in c
In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capabilit…
Achieving sustainability is currently an important development direction for the manufacturing industry. With the consideration of all pillars of sustainability, valid evaluation of sustainability during scheduling optimization has become an emerging critical issue in the production scheduling loops
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interact…
Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially th…
Fact verification is an essential tool to mitigate the spread of false information online, which has gained a widespread attention recently. However, a fact verification in the question-answering dialogue is still underexplored. In this paper, we propose a neural network based approach called questi…
Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage. Most previous works learn entity-specific embeddings of entities, which cannot handle unseen entities. Recent several methods utilize enclosing subgraph to obtai…
Semantic parsing is a mainstream method of knowledge base question answering task that first generates a set of logical forms according to question and knowledge base (KB), and then selects the most matching one to get answers. However, existing selection methods are usually based on word-level matc…
Due to the increasing demand in films and games, synthesizing 3D avatar animation has attracted much attention recently. In this work, we present a production-ready text/speech-driven full-body animation synthesis system. Given the text and corresponding speech, our system synthesizes face and body…
In order to be trusted by humans, Artificial Intelligence agents should be able to describe rationales behind their decisions. One such application is human action recognition in critical or sensitive scenarios, where trustworthy and explainable action recognizers are expected. For example, reliable…
Stacked self-attention models receive widespread attention, due to its ability of capturing global dependency among words. However, the stacking of many layers and components generates huge parameters, leading to low parameter efficiency. In response to this issue, we propose a lightweight architect…
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention. Most existing methods assume that all timestamps and corresponding graphs are available during training, which makes it difficult to predict future events. To address this issue, recent works learn to infer…
Natural orifice transluminal endoscopic surgery (NOTES) has received significant attentions due to its minimal incision trauma compared with traditional multi-port robot assisted surgery. Continuum robot can be used in NOTES due to its high flexibility which can adapt to circuitous paths. However, t
Cross-domain slot filling is a challenging task in spoken language understanding due to the differences in text genre across domains. In this paper, we attempt to solve this task by exploiting the syntactic structures of user utterances, because these syntactic structures are actually accessible and…
This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based…
The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have bee…
Phishing is an increasingly serious cybercrime. Phishers create phishing websites by mimicking legitimate websites to confuse users and steal their personal information. The proliferation of phishing websites and more advanced camouflage techniques are problems faced by most existing methods. In thi…
Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers. In this survey, we review representative methods at the intersection of NLP and quantum phy…
In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes voiceprint, lip movement, facial expression, and spatial information. However, no one cares for the cue of sound onset,…
Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. H…
Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedenc…
Graph neural networks (GNNs) emerged recently as a powerful tool for analyzing non-Euclidean data such as social network data. Despite their success, the design of graph neural networks requires heavy manual work and domain knowledge. In this paper, we present a graph neural architecture search meth…
Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial…
Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g., what color is the banana?) based on the high-frequency answers (e.g., yellow) ignoring image contents. Existing approa…
Clothes category classification and attribute recognition have achieved distinguished success with the development of deep learning. People have found that landmark detection plays a positive role in these tasks. However, little research is committed to analyzing these tasks from the perspective of…
Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, has led to breakthroughs in Natural Language Processing (NLP) tasks. However, the multi-head attention mechanism, as a ke…
A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements. This study tackles speaker-dependent tongue motion prediction problem in unlabeled ultrasound videos with convolutional long short-term memory (ConvLSTM) networks. The mo…
Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-l…
The complex compositional structure of language makes problems at the intersection of vision and language challenging. But language also provides a strong prior that can result in good superficial performance, without the underlying models truly understanding the visual content. This can hinder pro…
Image quality assessment (IQA) tries to estimate human perception based image visual quality in an objective manner. Existing approaches target this problem with or without reference images. For no-reference image quality assessment, there is no given reference image or any knowledge of the distor…