41 citations · 359 across the 102 of their papers we have counts for
14 papers · 1 filter
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation
Tianyi Liu, Zuxuan Wu, Wenhan Xiong +2
Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like objectives (masked language modeling and image-text matching) during pretraining.…
Cross-Modal Transferable Adversarial Attacks from Images to Videos
Zhipeng Wei, Jingjing Chen, Zuxuan Wu +1
Recent studies have shown that adversarial examples hand-crafted on one white-box model can be used to attack other black-box models. Such cross-model transferability makes it feas…
Two-stage Visual Cues Enhancement Network for Referring Image Segmentation
Yang Jiao, Zequn Jie, Weixin Luo +4
Referring Image Segmentation (RIS) aims at segmenting the target object from an image referred by one given natural language expression. The diverse and flexible expressions as wel…
Attacking Video Recognition Models with Bullet-Screen Comments
Kai Chen, Zhipeng Wei, Jingjing Chen +2
Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless…
BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval
Ning Han, Jingjing Chen, Chuhao Shi +3
The task of text-video retrieval aims to understand the correspondence between language and vision, has gained increasing attention in recent years. Previous studies either adopt o…
Boosting the Transferability of Video Adversarial Examples via Temporal Translation
Zhipeng Wei, Jingjing Chen, Zuxuan Wu +1
Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible…
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…
Towards Transferable Adversarial Attacks on Vision Transformers
Zhipeng Wei, Jingjing Chen, Micah Goldblum +3
Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. % crafted in a similar fa…
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…
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Rui Wang, Zuxuan Wu, Zejia Weng +3
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn d…
VideoLT: Large-scale Long-tailed Video Recognition
Xing Zhang, Zuxuan Wu, Zejia Weng +4
Label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively…
M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
Junke Wang, Zuxuan Wu, Wenhao Ouyang +4
The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulati…
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…
WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
Bojia Zi, Minghao Chang, Jingjing Chen +2
In recent years, the abuse of a face swap technique called deepfake has raised enormous public concerns. So far, a large number of deepfake videos (known as "deepfakes") have been…