199 citations · 1.1k across the 51 of their papers we have counts for
43 papers · 1 filter
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.…
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…
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…
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…
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…
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…
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
Yang Shen, Xuhao Sun, Xiu-Shen Wei +2
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-g…
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Xiu-Shen Wei, He-Yang Xu, Faen Zhang +2
Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated metho…
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification
Jiabao Wang, Yang Li, Xiu-Shen Wei +3
Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). Ho…
Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei +2
Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority s…
Fine-Grained Image Analysis with Deep Learning: A Survey
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha +5
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The…
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Zeren Sun, Yazhou Yao, Xiu-Shen Wei +5
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distingui…
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Peng Wang, Kai Han, Xiu-Shen Wei +2
Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the prom…
Distilling Virtual Examples for Long-tailed Recognition
Yin-Yin He, Jianxin Wu, Xiu-Shen Wei
We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method. Specifically, by treatin…
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Huafeng Liu, Chuanyi Zhang, Yazhou Yao +4
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images…