199 citations · 1.1k across the 51 of their papers we have counts for
54 papers
Learning to Group and Order: Cross-Instance Self-Supervised RL for Vision-Centric MLLMs
Songming Yang, Hong-Tao Yu, Xiu-Shen Wei
Reinforcement learning (RL) post-training has become an important paradigm for improving multimodal large language models (MLLMs), especially with automatically verifiable rewards.…
Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation
Lik Hang Kenny Wong, Yiyao Ma, Xiu-Shen Wei +5
Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient policy a key issue. To addres…
Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook
Xuelin Zhu, Xiu-Shen Wei, Jiawei Ge +2
Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-worl…
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis
Hong-Tao Yu, Chen-Wei Xie, Yuxin Peng +2
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal perception and reasoning capabilities. While numerous benchmarks have evaluated…
Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies
Meipo Dai, Qiyuan Zhuang, He-Yang Xu +4
Generative robot policies typically begin action generation from an observation-independent standard Gaussian distribution, leaving the choice of source distribution underexplored.…
Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation
He-Yang Xu, Pengyuan Zhang, Zongyuan Ge +5
Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high-fidelity spatial…
Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
Jia-Wei Hai, Yijun Wang, Xiu-Shen Wei
Vision-Language Models (VLMs), such as CLIP, have achieved significant zero-shot performance on downstream tasks with various fine-tuning adaptation methods. However, recent studie…
Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu +11
Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation b…
Open-Loop Planning, Closed-Loop Verification: Speculative Verification for VLA
Zihua Wang, Zhitao Lin, Ruibo Li +4
Vision-Language-Action (VLA) models, as large foundation models for embodied control, have shown strong performance in manipulation tasks. However, their performance comes at high…
RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment
Qiyuan Zhuang, He-Yang Xu, Yijun Wang +3
Understanding object affordances is essential for enabling robots to perform purposeful and fine-grained interactions in diverse and unstructured environments. However, existing ap…
Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
Yijun Wang, Qiyuan Zhuang, Larysa Marchanka +1
Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and shifts abruptly around structural brea…
Efficient and Effective In-context Demonstration Selection with Coreset
Zihua Wang, Jiarui Wang, Haiyang Xu +6
In-context learning (ICL) has emerged as a powerful paradigm for Large Visual Language Models (LVLMs), enabling them to leverage a few examples directly from input contexts. Howeve…
Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization
Peng Wang, Yong Li, Lin Zhao +1
Fine-grained hashing has become a powerful solution for rapid and efficient image retrieval, particularly in scenarios requiring high discrimination between visually similar catego…
Object-level Correlation for Few-Shot Segmentation
Chunlin Wen, Yu Zhang, Jie Fan +5
Few-shot semantic segmentation (FSS) aims to segment objects of novel categories in the query images given only a few annotated support samples. Existing methods primarily build th…
Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
He-Yang Xu, Hongxiang Gao, Yuwen Li +2
The diagnostic value of electrocardiogram (ECG) lies in its dynamic characteristics, ranging from rhythm fluctuations to subtle waveform deformations that evolve across time and fr…
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
Hong-Tao Yu, Yuxin Peng, Serge Belongie +1
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal perception capabilities, garnering significant attention. While numerous evaluat…
Beyond Overfitting: Doubly Adaptive Dropout for Generalizable AU Detection
Yong Li, Yi Ren, Xuesong Niu +3
Facial Action Units (AUs) are essential for conveying psychological states and emotional expressions. While automatic AU detection systems leveraging deep learning have progressed,…
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
Yang Shen, Xiu-Shen Wei, Yifan Sun +6
Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established…
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction
Chen-Long Duan, Yong Li, Xiu-Shen Wei +1
Pre-training plays a vital role in various vision tasks, such as object recognition and detection. Commonly used pre-training methods, which typically rely on randomized approaches…
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
Jiabei He, Yang Shen, Xiu-Shen Wei +1
Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Inter…
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Xiu-Shen Wei, Yang Shen, Xuhao Sun +2
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on…
Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
Shu-Lin Xu, Yifan Sun, Faen Zhang +3
Learning fine-grained embeddings from coarse labels is a challenging task due to limited label granularity supervision, i.e., lacking the detailed distinctions required for fine-gr…
Watch out Venomous Snake Species: A Solution to SnakeCLEF2023
Feiran Hu, Peng Wang, Yangyang Li +6
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata. This paper p…
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Xiu-Shen Wei, Xuhao Sun, Yang Shen +3
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised dis…