90 accepted papers
In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit–explicit representation that fuses neural
We propose AdapTok, an adaptive temporal causal video tokenizer that can flexibly allocate tokens for different frames based on video content. AdapTok is equipped with a block-wise masking strategy that randomly drops tail tokens of each block during training, and a block causal scorer to predict th
Non-transferable learning (NTL) aims to enforce usage restrictions by limiting a model's generalization on target-domain data while maintaining its utility on the source domain. Current approaches face three major challenges: (1) low training efficiency due to retraining of the backbone network, (2)
Reinforcement Learning (RL) has become a cornerstone for improving the performance of Large Language Models (LLMs). However, its rollout phase constitutes a significant efficiency bottleneck, mainly arising from the long-tail bubbles across data parallel ranks, particularly in long-context scenarios…
Instruction-based image editing with diffusion models has achieved impressive results, yet existing methods struggle with fine-grained instructions specifying precise attributes such as colors, positions, and quantities. While recent approaches employ Group Relative Policy Optimization (GRPO) for al
Goal-conditioned Reinforcement Learning (RL) is a promising direction for training agents capable of tackling a variety of tasks. However, generalizing to new goals in different environments remains a central challenge for goal-conditioned RL agents. Existing methods often rely on state abstraction,
Model merging offers a promising paradigm for consolidating multiple expert models into a single multitask architecture. However, its effectiveness is often hindered by task interference, where conflicting parameter updates from different tasks degrade performance. While dynamic, Mixture-of-Experts
Evolutionary model merging provides a powerful framework for the automated, training-free composition of LLMs through parameter-space search. However, existing methods predominantly rely on stochastic, hand-crafted operators that overlook the underlying performance landscape of the coefficient space…
Offline Federated Deep Reinforcement Learning (FDRL) methods aggregate multiple client-side offline Deep Reinforcement Learning (DRL) models, each trained locally, to facilitate knowledge sharing while preserving privacy. Existing offline FDRL methods assign client weights during global aggregation …
Control in high-dimensional action spaces remains a fundamental challenge in reinforcement learning (RL), primarily due to inefficient exploration of the action space. While recent methods attempt to guide exploration, they often fall short of achieving the agility and coordination exhibited in biol
The increasing complexity of real-world deployment requires intelligent agents to effectively adapt to non-stationary data streams with stochastic increments under data scarcity. We formally define this challenge as the Few-Shot Hybrid Incremental Learning (FSHIL) paradigm, which reveals a critical
The probabilistic diffusion model (DM), generating content by inferencing through a recursive chain structure, has emerged as a powerful framework for visual generation. After pre-training on enormous data, the model needs to be properly aligned to meet requirements for downstream applications. How…
Humans learn by observing, interacting with environments, and internalizing physics and causality. We explore whether agents can similarly acquire human-like reasoning through interaction and experience. To study this, we introduce a Game-to-Unseen (G2U) benchmark of 1,000+ heterogeneous games that
With the advancement of Vision-Language Models (VLMs), employing VLM-as-a-Judge for visual evaluation has become a widely adopted metric in vision research. However, existing VLM-as-a-Judge approaches suffer from biased scoring outcomes with low discrimination and lack the capacity for unified multi
Reinforcement learning (RL) has achieved promising results in continuous control tasks, where efficient exploration of the state space is crucial for success. However, many recent RL approaches still struggle with sample inefficiency and insufficient exploration for long-horizon tasks, particularly
The rise of Vision Transformers (ViTs) as cornerstone models in safety-critical applications like autonomous driving and medical diagnosis has shifted the focus from pure accuracy to verifiable trustworthiness. However, the very mechanisms used to explain these models, their internal attention maps,
This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthesis and robust pose estimation under varying camera configurations. Existing 3DGS-based SLAM systems often suffer from aliasing artifacts and trajectory…
Preserving identity while precisely manipulating attributes is a central challenge in face editing for both images and videos. Existing methods often introduce visual artifacts or fail to maintain temporal consistency. We present **FlowGuide**, a unified framework that achieves fine-grained control…
Although Large Language Models (LLMs) excel at code generation, recent research reveals that they exhibit an insufficient grasp of core programming concepts, such as data flow and control flow. This limitation undermines their robustness when encountering variations in these concepts in practice; ho
Questionnaire data serve as a valuable resource across numerous scientific domains, offering insights into human behavior, health, and social trends. Traditional downsampling-based representation learning methods—such as standardization and one-hot encoding—reformat these data into tabular structure
Minimal parametrization of 3D lines plays a critical role in camera localization and structural mapping. Existing representations in robotics and computer vision predominantly handle independent lines, overlooking structural regularities such as sets of parallel lines that are pervasive in man-made
Large language models (LLMs) enable reasoning over biomolecular structures, yet existing methods remain modality-specific and typically compress structural inputs via sequence-based tokenization or fixed-length query connectors. Such architectures either omit geometric grounding required to mitigate…
Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and…
Prevailing LLM (Large Language Model) serving engines employ expert parallelism (EP) to implement multi-device inference of massive Mixture-of-Experts (MoE) models. However, the efficiency of expert parallel inference is largely bounded by inter-device communication, as EP embraces expensive all-to-…
Rectified-flow-based diffusion transformers, particularly FLUX, have demonstrated outstanding performance in high-quality image generation. However, achieving fast and accurate inversion—transforming images back to latent noise for faithful reconstruction and editing—remains a challenging bottleneck…
Prompting is fundamental to unlocking the full potential of large language models. To automate and enhance this process, automatic prompt optimization (APO) has been developed, demonstrating effectiveness primarily in text-only input scenarios. However, extending existing APO methods to multimodal t
Recent Vision-Language Models (VLMs) have made remarkable progress in multimodal understanding tasks, yet their evaluation on long video understanding remains unreliable. Due to limited frame inputs, key frames necessary for answering the question may be missing from the model's input. However, mode
A central problem in unsupervised domain adaptation is determining what to transfer from labeled source domains to an unlabeled target domain. To handle high-dimensional observations (e.g., images), a line of approaches use deep learning to learn latent representations of the observations, which fac…
Transformer-based Large Language Models (LLMs) struggle with inputs exceeding their training context window due to positional out-of-distribution (O.O.D.) issues that disrupt attention. Existing solutions, including fine-tuning and training-free methods, face challenges like inefficiency, redundant
Cross-modal alignment is crucial for multimodal representation fusion due to the inherent heterogeneity between modalities. While Transformer-based methods have shown promising results in modeling inter-modal relationships, their quadratic computational complexity limits their applicability to long-…
Ship detection using remote sensing imagery is a crucial research area with both military and civilian applications. However, it remains challenging due to limitations in current ship datasets, such as insufficient volume, incomplete annotations, and inaccuracies. Additionally, ships often exhibit a…
Efficient exploration of state spaces is critical for the success of deep reinforcement learning (RL). While many methods leverage exploration bonuses to encourage exploration instead of relying solely on extrinsic rewards, these bonus-based approaches often face challenges with learning efficiency
Causal Representation Learning (CRL) aims to uncover the data-generating process and identify the underlying causal variables and relations, whose evaluation remains inherently challenging due to the requirement of known ground-truth causal variables and causal structure. Existing evaluations often…
Transformer-based models have achieved remarkable success in various Natural Language Processing (NLP) tasks, yet their ability to handle long documents is constrained by computational limitations. Traditional approaches, such as truncating inputs, sparse self-attention, and chunking, attempt to mit…
Preference optimization for diffusion models aims to align them with human preferences for images. Previous methods typically use Vision-Language Models (VLMs) as pixel-level reward models to approximate human preferences. However, when used for step-level preference optimization, these models face…
Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that…
Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-di
Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrot
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance and poor generalizations.…
We address the problem of learning ordinary differential equations (ODEs) on manifolds. Existing machine learning methods, particularly those using neural networks, often struggle with high computational demands. To overcome this issue, we introduce a geometrically constrained operator-valued kernel…
Inertial tracking (IT), independent of the environment and external infrastructure, has long been the ideal solution for providing location services to humans. Despite significant strides in inertial tracking empowered by deep learning, prevailing neural inertial tracking predominantly utilizes conv…
Although Large Language Models (LLMs) can generate coherent text, they often struggle to recognise user intent behind queries. In contrast, Natural Language Understanding (NLU) models interpret the purpose and key information of user input for responsive interactions. Existing NLU models typically m…
YOLO-based models are widely used for personal protective equipment (PPE) compliance detection due to their excellent detection performance and efficiency. However, most YOLO models are not competent for detection tasks in complex industrial scenarios such as remote surveillance and extremely small…
Existing text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images guided by textual prompts. However, achieving multi-subject compositional synthesis with precise spatial control remains a significant challenge. In this work, we address the task of l…
Pansharpening technique is dedicated to enriching the spatial details of low-resolution multispectral images (LRMS) under the guidance of a panchromatic (PAN) image. With the guarantee of promising results, Transformer-based methods have enjoyed a high reputation in this field. However, to reduce co…
Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs) by encouraging step-by-step thinking. However, CoT-based methods depend on intermediate reasoning steps, which limits scalability and generalization. Recent work explores recursive reasoning, where L…
Vision-Language Models (VLMs) integrate visual knowledge with the analytical capabilities of Large Language Models (LLMs) through supervised visual instruction tuning, using image-question-answer triplets. However, the potential of VLMs trained without supervised instruction remains largely unexplor…
Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence, enabling automated extraction of key information from complex documents across domains such as medical, financial, and educational applications. However, form-like documents pose unique challenges d
Large Multimodal Models (LMMs) have achieved impressive progress in visual perception and reasoning. However, when confronted with visually ambiguous or non-semantic scene text, they often struggle to accurately spot and understand the content, frequently generating semantically plausible yet visual…
Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based tec…
In order to solve issues such as low accuracy, poor robustness and tracking loss in dynamic environments in traditional Simultaneous Localization and Mapping (SLAM) techniques, a semantic SLAM system named DFS-SLAM (Deep Fusion Semantics SLAM) is proposed. The algorithmic framework of DFS-SLAM is he
"We introduce , which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, offers several advantages. Firstly, trajectory-based is more user-friendly for interaction, when acquiring other guidance s…
Current formation assignment and optimization methods frequently overlook the influence of rotational dynamics, limiting their operational flexibility. Additionally, these methods typically neglect the impact of obstacles, which may also hinder their effectiveness in obstacle-rich environments. To a
Polyp segmentation plays a crucial role in the prevention of colon cancer. However, the diverse shapes of polyps and their similarity to normal areas in terms of color and texture make polyp segmentation a challenging task. Currently, most polyp segmentation methods solely focus on spatial domain fe…
Low-light image enhancement aims to reconstruct images with insufficient illumination into visually appealing representations with natural brightness. While most existing methods tend to focus on enhancing illumination, they often overlook the restoration of finer details in the enhanced image. More…
Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images they reduce the rich tapestry of human preference to a single overall score.…
E-visits have emerged as a pivotal tool for enhancing both patients' medical experience and the efficient utilization of medical resources. By modeling stylized queueing models, this letter explores the optimal online medical resource allocation decision on providing e-visits in a public hospital, a
"An increasingly massive number of remote-sensing images spurs the development of extensible object detectors that can detect objects beyond training categories without costly collecting new labeled data. In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial ima…
The proliferation of short video and live-streaming platforms has revolutionized how consumers engage in online shopping. Instead of browsing product pages, consumers are now turning to rich-content e-commerce, where they can purchase products through dynamic and interactive media like short videos…
Live commerce is the act of selling products online through livestreaming. The customer's diverse demands for online products introduces more challenges to Livestreaming Product Recognition. Previous works are either focus on fashion clothing data or subject to single-modal input, thus inconsistent…
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive to small changes in their environment. This presents a serious concern for the real world deployment of MARL algorithms,…
Inferring the causal structure of a set of random variables is a crucial problem in many disciplines of science. Over the past two decades, various approaches have been pro- posed for causal discovery from observational data. How- ever, most of the existing methods are designed for either purely dis…
Asymmetric text matching has becoming increasingly indispensable for many downstream tasks (e.g., IR and NLP). Here, asymmetry means that the documents involved for matching hold different amounts of information, e.g., a short query against a relatively longer document. The existing solutions mainly…
Embedding learning has found widespread applications in recommendation systems and natural language modeling, among other domains. To learn quality embeddings efficiently, adaptive learning rate algorithms have demonstrated superior empirical performance over SGD, largely accredited to their token-d…
Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we prop…
Most metric learning techniques typically focus on sample embedding learning, while implicitly assume a homogeneous local neighborhood around each sample, based on the metrics used in training ( e.g., hypersphere for Euclidean distance or unit hyperspherical crown for cosine distance). As real-world…
We investigate the role of noise in optimization algorithms for learning over-parameterized models. Specifically, we consider the recovery of a rank one matrix $Y^*\in R^{d\times d}$ from a noisy observation $Y$ using an over-parameterization model. Specifically, we parameterize the rank one matrix…
Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the res…
Deep metric learning aims to learn a deep embedding space, where similar objects are pushed towards together and different objects are repelled against. Existing approaches typically use inter-class characteristics, e.g. class-level information or instance-level similarity, to obtain semantic releva…
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems. The theory behind such empirical observations, however, is still largely unknown. This paper studies this fundamental problem through investigating the nonconvex rectangular matrix factorizatio…
Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the applications involving a large population of homogeneous agents, as it exploits the permutation invariance of agents and avoids the curse of many agents. Most existing results only focus on online settings, in which agents…
Weakly-supervised video object segmentation (WVOS) is an emerging video task that can track and segment the target given a simple bounding box label. However, existing WVOS methods are still unsatisfied in either speed or accuracy, since they only use the exemplar frame to guide the prediction while…
Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the ope…
Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous sta…
Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unexplored. In this paper, we provide new theoretical insights of gradient descent based adversarial training by studying it…
We present a novel end-to-end network, MANet, for light field depth estimation. MANet is a parameter-effective and effi-cient multi-scale aggregated network, which is about 3 times smaller and 3 times faster than the current top-performing method Epinet. The MANet architecture is performed for estim…
The key of image and sentence matching is to accurately measure the visual-semantic similarity between an image and a sentence. However, most existing methods make use of only the intra-modality relationship within each modality or the inter-modality relationship between image regions and sentence w…
Approaches toward ordinal speech emotion recognition (SER) tasks are commonly based on the categorical classification algorithms, where the rank-order emotions are arbitrarily treated as independent categories. To employ the ordinal information between emotional ranks, we propose to model the ordina…
Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) modu…
Automatic singing evaluation without reference melody has long been a difficult problem. This paper aims to pilot a novel data driven approach to tackle this artistic problem. We constructed a large scale dataset and designed an innovative Bi-Dense neural network which can address this task efficien…
Human can well recognize images of novel categories just after browsing few examples of these categories. One possible reason is that they have some external discriminative visual information about these categories from their prior knowledge. Inspired from this, we propose a novel Knowledge Transfer…
Attention-based sequence-to-sequence architectures have made great progress in the speech recognition task. The SpeechTransformer, a no-recurrence encoder-decoder architecture, has shown promising results on small-scale speech recognition data sets in previous works. In this paper, we focus on a lar…
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of deep neural networks. The theory behind, however, is still largely unknown. This paper studies this fundamental problem through training a simple two-layer convolutional neural net…
To recognize objects of the unseen classes, most existing Zero-Shot Learning (ZSL) methods first learn a compatible projection function between the common semantic space and the visual space based on the data of source seen classes, then directly apply it to the target unseen classes. However, in re…
Zero-shot learning (ZSL) aims to recognize unseen image categories by learning an embedding space between image and semantic representations. For years, among existing works, it has been the center task to learn the proper mapping matrices aligning the visual and semantic space, whilst the importanc…
We investigate a projection free optimization method, namely non-convex conditional gradient sliding (NCGS) for non-convex optimization problems on the batch, stochastic and finite-sum settings. Conditional gradient sliding (CGS) method, by integrating Nesterov’s accelerated gradient method with Fra…
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power. Our pro…
Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching. It also forms an interesting but challenging computer vision task, as the visual data to match, i.e., still image and video…
In this paper, we propose a hybrid FUZZY P + ID controller to stabilize the towing cable tension between a cable ship and a burying system. First, we develop the model of a winch system driven by valve-controlled hydraulic motors and evaluate the step responses yielded by the conventional PID and th…
We address the challenging large-scale content-based face image retrieval problem, intended as searching images based on the presence of specific subject, given one face image of him/her. To this end, one natural demand is a supervised binary code learning method. While the learned codes might be di…