406 citations · 853 across the 27 of their papers we have counts for
21 papers · 1 filter
Event-Aware Instructed Assistant for Referring Video Segmentation
Jinyu Liu, Henghui Ding, Shuting He +1
Existing referring video segmentation methods often treat a video as a single event consisting of multiple images, overlooking the fact that a video typically contains multiple dis…
FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
Kun Zhai, Siheng Chen, Xingjun Ma +1
Federated Prompt Tuning (FPT) is an efficient method for cross-client collaborative fine-tuning of large Vision-Language Models (VLMs). However, models tuned using FPT are vulnerab…
BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos
Jiahao Lin, Weixuan Peng, Bojia Zi +4
Recent advances in deep generative models have led to significant progress in video generation, yet the fidelity of AI-generated videos remains limited. Synthesized content often e…
Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization
Pengkun Jiao, Na Zhao, Jingjing Chen +1
Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains with both domain shifts an…
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process
Yang Luo, Yiheng Zhang, Zhaofan Qiu +4
The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of…
Decoder Pre-Training with only Text for Scene Text Recognition
Shuai Zhao, Yongkun Du, Zhineng Chen +1
Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. However, the domain gap between synthetic and real ima…
PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer
Qian Feng, Hanbin Zhao, Chao Zhang +4
Incremental Learning (IL) aims to learn deep models on sequential tasks continually, where each new task includes a batch of new classes and deep models have no access to task-ID i…
White-box Multimodal Jailbreaks Against Large Vision-Language Models
Ruofan Wang, Xingjun Ma, Hanxu Zhou +3
Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been…
From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios
Guoshan Liu, Yang Jiao, Jingjing Chen +2
The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks,…
Instruction-Guided Scene Text Recognition
Yongkun Du, Zhineng Chen, Yuchen Su +2
Multi-modal models have shown appealing performance in visual recognition tasks, as free-form text-guided training evokes the ability to understand fine-grained visual content. How…
FoodLMM: A Versatile Food Assistant using Large Multi-modal Model
Yuehao Yin, Huiyan Qi, Bin Zhu +3
Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from sat…
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
Zuxuan Wu, Zejia Weng, Wujian Peng +4
Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-…
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Hui Zhang, Zheng Wang, Dan Zeng +2
Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models…
Vocabulary-informed Zero-shot and Open-set Learning
Yanwei Fu, Xiaomei Wang, Hanze Dong +4
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recog…
Self-supervised Learning for Semi-supervised Temporal Language Grounding
Fan Luo, Shaoxiang Chen, Jingjing Chen +2
Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video. TLG is…
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Yuqian Fu, Yanwei Fu, Yu-Gang Jiang
A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motiva…
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition
Zejia Weng, Zuxuan Wu, Hengduo Li +2
Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally exp…
Multi-level Semantic Feature Augmentation for One-shot Learning
Zitian Chen, Yanwei Fu, Yinda Zhang +3
The ability to quickly recognize and learn new visual concepts from limited samples enables humans to swiftly adapt to new environments. This ability is enabled by semantic associa…
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Jinhui Tang, Xiangbo Shu, Zechao Li +2
Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, us…
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
Baohan Xu, Yanwei Fu, Yu-Gang Jiang +2
Emotion is a key element in user-generated videos. However, it is difficult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-genera…
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
Yu-Gang Jiang, Zuxuan Wu, Jun Wang +2
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Alth…