66 accepted papers
Visual perception in modern Vision-Language Models (VLM) is constrained by a fundamental perceptual bandwidth bottleneck: a broad field-of-view inevitably sacrifices the fine-grained details necessary for complex reasoning. Inspired by the classical paradigms of active vision and information foragin…
Large vision foundation models, such as SAM2, have achieved remarkable performance in video object segmentation and tracking (VOST). However, their effectiveness is hindered by significant computational overhead. While model pruning is a widely used strategy to address this issue, traditional static
Active 3D reconstruction relies on active view selection to maximize reconstruction fidelity under limited capture budgets. However, most existing methods rely on surrogate signals—such as parameter uncertainty or geometric heuristics—which are often misaligned with the ultimate goal: the fidelity o…
GLOM, an innovative departure from standard deep learning architectures, has been proposed and gained special concern recently due to its good interpretability in representing part-whole relationships in computer vision. However, GLOM faces challenges in achieving agreement and is usually computatio…
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require extensive exploration and face challenges in generalizing across diverse environments due to their limited grasp of the u…
Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with contact-rich tasks that require fine-grained control involving force, especially under visual occlusion or dynamic uncert…
Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detecting surface defects in pre-defined or controlled imaging environments. However, accurately detecting workpiece defects i
Existing deep embedding learning based speaker verification (SV) methods suffer from performance degradation under domain shift conditions. This can be alleviated through unsupervised domain adaptation (UDA) techniques. While UDA improves global statistical consistency across domains, discriminative…
A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs. Semi-supervised algorithms rely on labeled data to learn from unlabeled data, and the performance is constrained by the…
Recent research on Reasoning of Large Language Models (LLMs) has sought to further enhance their performance by integrating meta-thinking—enabling models to monitor, evaluate, and control their reasoning processes for more adaptive and effective problem-solving. However, current single-agent work la…
In practice, reinforcement learning (RL) agents are often trained with a possibly imperfect proxy reward function, which may lead to a human-agent alignment issue (i.e., the learned policy either converges to non-optimal performance with low cumulative rewards, or achieves high cumulative rewards bu…
Evaluating large language models (LLMs) poses significant challenges, particularly due to issues of data contamination and the leakage of correct answers. To address these challenges, we introduce ThinkBench, a novel evaluation framework designed to robustly evaluate the reasoning capability of LLMs…
Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as Assistants of LLMs), a framework that combines Lar…
Between 2021 and 2023, AI-Olympics---a series of online AI competitions, was hosted by the online evaluation platform Jidi in collaboration with the IJCAI committee. In these competitions, an agent is required to accomplish diverse sports tasks in a two-dimensional continuous world, while competing…
Given a sentence and a particular aspect term, aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards this aspect term, which provides fine-grained analysis on sentiment understanding and it has attracted much attention in recent years. In order to achieve a good perfo…
Radiology report generation (RRG) aims to automatically generate a free-text description from a specific clinical radiograph, e.g., chest X-Ray images. Existing approaches tend to perform RRG with specific models trained on the public yet limited data from scratch, where they often lead to inferior…
Recent progress in large language models (LLMs) has marked a notable milestone in the field of artificial intelligence. The conventional evaluation of LLMs primarily relies on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs…
Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure. With big data, especially texts, forming the foundation of medical services, there is an exigent need for effective natural language processing (NLP) solutions tailored to t…
Dialogue summarization is an important task that requires to generate highlights for a conversation from different aspects (e.g., content of various speakers). While several studies successfully employ large language models (LLMs) and achieve satisfying results, they are limited by using one model a…
Radiology report generation (RRG) aims to automatically provide observations and insight into a patient’s condition based on radiology images, which is able to greatly reduce the workload of physicians on the premise of ensuring the quality of medical treatment. Existing works leverage the Transform…
The development of large language models (LLMs) brings significant changes to the field of natural language processing (NLP), enabling remarkable performance in various high-level tasks, such as machine translation, question-answering, dialogue generation, etc., under end-to-end settings without req…
In many practical scenarios, contents from different modalities are not semantically aligned; for instance, visual and textual information may conflict with each other, resulting in non-compositional expression effects such as irony or humor. Effective modeling and smooth integration of multimodal i…
This paper presents a meta representation learning method for robust speaker verification (SV) in unseen domains. It is known that the existing embedding learning based SV systems may suffer from domain mismatch issues. To address this, we propose an episodic training procedure to compensate domain…
"Facial Expression Analysis remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition…
Given several documents, multi-hop question generation (MQG) is a task aims to generate complicated questions that require reasoning over multiple pieces of these documents to find the answer. To perform this task, existing studies focus on designing advanced architectures to locate essential keywor…
On social media platforms, users’ emotions are triggered when they encounter particular content from other users,where such emotions are different from those that spontaneously emerged, owing to the “responsive” nature. Analyzing the aforementioned responsive emotions from user interactions is a tas…
In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED. Pretrained AST models have recently shown promise on DCASE2022 challenge task4 where t…
In this paper, we propose an effective anomalous sound detection (ASD) method based on representation learning with simulated anomalies. Recently, ASD systems have used Outlier Exposure (OE) strategy to achieve promising performance in DCASE challenges. These exploit deep Convolutional Neural Networ…
End-to-end Aspect-based Sentiment Analysis (EASA) is a natural language processing (NLP) task that involves extracting aspect terms and identifying the sentiments for them, which provides a fine-grained level of text analysis and thus requires a deep understanding of the running text. Many previous…
Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly. Although promising res…
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE) equipped with self-attention as a generative model to perform frame-level prediction. The output of the PAE together wi…
Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sampl…
Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly rewarded past experiences to improve sample efficiency of DRL algorithms. However, previous episodic control-based approaches fail to…
Automatic speaker verification (ASV) faces domain shift caused by the mismatch of intrinsic and extrinsic factors, such as recording device and speaking style, in real-world applications, which leads to severe performance degradation. Since single-speaker multi-condition (SSMC) data is difficult to…
Text style transfer (TST) is an important task in natural language generation, which aims to alter the stylistic attributes (e.g., sentiment) of a sentence and keep its semantic meaning unchanged. Most existing studies mainly focus on the transformation between styles, yet ignore that this transform…
Chinese couplet generation aims to generate a pair of clauses (usually generating a subsequent clause given an antecedent one) with certain rules (e.g., morphological and syntactical symmetry) adhered and has long been a challenging task with cultural background. To generate high-quality couplet (an…
Relation extraction (RE) is an important task in natural language processing which aims to annotate the relation between two given entities, which requires a deep understanding of the running text. To import model performance, existing approaches leverage syntactic information to facilitate the rela…
Composing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given. To automatically generate a well-formed Ci, text generation systems should strictly take into account pre-defined rigid formats (e.g., len…
This paper presents a domain robust deep embedding learning method for speaker verification (SV) tasks. Most recent methods utilize deep neural networks (DNN) to learn compact and discriminative speaker embeddings from large-scale labeled datasets such as VoxCeleb and the NIST SRE corpus. Despite th…
Dependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words. To improve dependency parsing, leveraging existing dependency parsers and extra data (e.g., through semi-…
Speech emotion recognition (SER) with limited size dataset is a challenging task, since a spoken utterance contains various disturbing attributes besides emotion, including speaker, content, and language. However, due to a close relationship between speaker and emotion attributes, simply fine-tuning…
Transliteration is an important task in natural language processing (NLP) which aims to convert a name in the source language to the target language without changing its pronunciation. Particularly, transliteration from English to Arabic is highly needed in many applications, especially in countries…
Relation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual information is essential to achieve an outstanding model performance. Among different types of contextual information, the aut…
Medical images are widely used in clinical decision-making, where writing radiology reports is a potential application that can be enhanced by automatic solutions to alleviate physicians’ workload. In general, radiology report generation is an image-text task, where cross-modal mappings between imag…
In this paper, a self-supervised representation learning method is proposed for anomalous sound detection (ASD). ASD has received much research attention in recent DCASE challenges. It aims to identify whether a sound emitted from a machine is anomalous or not, given only normal sound data. This is…
In this paper, we present an effective end-to-end deep embedding learning method based on Dense-Residual networks, which combine the advantages of a densely connected convolutional network (DenseNet) and a residual network (ResNet), for speaker verification (SV). Unlike a model ensemble strategy whi…
This paper presents an improved mean teacher (MT) based method for large-scale weakly labeled semi-supervised sound event detection (SED), by focusing on learning a better student model. Two main improvements are proposed based on the authors’ previous perturbation based MT method. Firstly, an event…
It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words. However, most of these studies only…
Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and sig…
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency t…
Arabic diacritization is a fundamental task for Arabic language processing. Previous studies have demonstrated that automatically generated knowledge can be helpful to this task. However, these studies regard the auto-generated knowledge instances as gold references, which limits their effectiveness…
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding of the contextual information, especially the part…
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especial…
Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further impr…
Despite the significant progress of deep learning based speech separation methods, it remains challenging to extract and track the speech from target speakers, especially in a single-channel multiple speaker situation. Previously, the authors proposed a source-aware context network to exploit the te…
End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis pro…
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks for Chinese language processing. Previous studies have demonstrated that jointly performing them can be an effective one-step solution to both tasks and this joint task can benefit from a good modeling of cont…
Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact…
Summarization is an important natural language processing (NLP) task in identifying key information from text. For conversations, the summarization systems need to extract salient contents from spontaneous utterances by multiple speakers. In a special task-oriented scenario, namely medical conversat…
Weakly labeled semi-supervised learning methods have recently drawn increasing attention from the research community for sound event detection tasks. Due to the weakness of the labelling, neural networks are often designed to perform sound event detection (SED) and audio tagging (AT) at the same tim…
Recently, an attention based convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) has achieved state-of-the-art performance for audio tagging (AT) and sound event detection (SED) tasks in the Detection and Classification of Acoustic Scenes and Events (DCASE) challen…
Topic detection for conversational telephone speech (CTS) is addressed in this paper. The low accuracy of automatic speech recognition (ASR) will cause severe performance deterioration for topic detection. To make up for this, we adopt two ASR systems, HMM-BiLSTM and CTC systems, to provide compleme…
Deep learning based approaches have achieved promising performance in speaker-dependent single-channel multispeaker speech separation. However, partly due to the label permutation problem, they may encounter difficulties in speaker-independent conditions. Recent methods address this problem by some…
Transfer learning methods have demonstrated state-of-the-art performance on various small-scale image classification tasks. This is generally achieved by exploiting the information from an ImageNet convolution neural network (ImageNet CNN). However, the transferred CNN model is generally with high c…
Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still f…
Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to…