84 citations · 226 across the 39 of their papers we have counts for
41 papers
Mutually Causal Semantic Distillation Network for Zero-Shot Learning
Shiming Chen, Shuhuang Chen, Guo-Sen Xie +1
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic…
UniComp: Rethinking Video Compression Through Informational Uniqueness
Chao Yuan, Shimin Chen, Minliang Lin +3
Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the info…
Prototype-Guided Curriculum Learning for Zero-Shot Learning
Lei Wang, Shiming Chen, Guo-Sen Xie +4
In Zero-Shot Learning (ZSL), embedding-based methods enable knowledge transfer from seen to unseen classes by learning a visual-semantic mapping from seen-class images to class-lev…
Few-Shot Object Detection via Spatial-Channel State Space Model
Zhimeng Xin, Tianxu Wu, Yixiong Zou +3
Due to the limited training samples in few-shot object detection (FSOD), we observe that current methods may struggle to accurately extract effective features from each channel. Sp…
Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Model
Shiming Chen, Bowen Duan, Salman Khan +1
Large-scale vision-language models (VLMs), such as CLIP, have achieved remarkable success in zero-shot learning (ZSL) by leveraging large-scale visual-text pair datasets. However,…
GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
Shiming Chen, Dingjie Fu, Salman Khan +1
Remarkable progress in zero-shot learning (ZSL) has been achieved using generative models. However, existing generative ZSL methods merely generate (imagine) the visual features fr…
Interpretable Zero-shot Learning with Infinite Class Concepts
Zihan Ye, Shreyank N Gowda, Shiming Chen +3
Zero-shot learning (ZSL) aims to recognize unseen classes by aligning images with intermediate class semantics, like human-annotated concepts or class definitions. An emerging alte…
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
Zhimeng Xin, Tianxu Wu, Shiming Chen +5
Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours…
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
Improved Feature Generating Framework for Transductive Zero-shot Learning
Zihan Ye, Xinyuan Ru, Shiming Chen +3
Feature Generative Adversarial Networks have emerged as powerful generative models in producing high-quality representations of unseen classes within the scope of Zero-shot Learnin…
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan +2
Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video…
Visual-Semantic Graph Matching Net for Zero-Shot Learning
Bowen Duan, Shiming Chen, Yufei Guo +3
Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often lea…
Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization
Shimin Chen, Wei Li, Jianyang Gu +2
In the task of temporal action localization of ActivityNet-1.3 datasets, we propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. W…
Technical Report for Soccernet 2023 -- Dense Video Captioning
Zheng Ruan, Ruixuan Liu, Shimin Chen +5
In the task of dense video captioning of Soccernet dataset, we propose to generate a video caption of each soccer action and locate the timestamp of the caption. Firstly, we apply…
Technical Report for SoccerNet Challenge 2022 -- Replay Grounding Task
Shimin Chen, Wei Li, Jiaming Chu +3
In order to make full use of video information, we transform the replay grounding problem into a video action location problem. We apply a unified network Faster-TAD proposed by us…
ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning
Wenjin Hou, Dingjie Fu, Kun Li +3
Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic knowledge from seen classes to unseen ones, guided by semantic information. To this end, existing…
Fewer Tokens and Fewer Videos: Extending Video Understanding Abilities in Large Vision-Language Models
Shimin Chen, Yitian Yuan, Shaoxiang Chen +2
Amidst the advancements in image-based Large Vision-Language Models (image-LVLM), the transition to video-based models (video-LVLM) is hindered by the limited availability of quali…
ZeroDiff: Solidified Visual-Semantic Correlation in Zero-Shot Learning
Zihan Ye, Shreyank N. Gowda, Xiaowei Huang +4
Zero-shot Learning (ZSL) aims to enable classifiers to identify unseen classes. This is typically achieved by generating visual features for unseen classes based on learned visual-…
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions. Despite this, domai…
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Wenjin Hou, Shiming Chen, Shuhuang Chen +6
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative metho…
Few-Shot Object Detection: Research Advances and Challenges
Zhimeng Xin, Shiming Chen, Tianxu Wu +3
Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such m…
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
Shiming Chen, Wenjin Hou, Salman Khan +1
Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by sema…
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
Yunwei Bai, Ying Kiat Tan, Shiming Chen +2
Few-shot learning (FSL) commonly requires a model to identify images (queries) that belong to classes unseen during training, based on a few labelled samples of the new classes (su…
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining…