14 citations · 42 across the 12 of their papers we have counts for
13 papers · 1 filter
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Xin Wang, Jie Li, Zejia Weng +9
Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their…
GenRec: Unifying Video Generation and Recognition with Diffusion Models
Zejia Weng, Xitong Yang, Zhen Xing +2
Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether suc…
AID: Adapting Image2Video Diffusion Models for Instruction-guided Video Prediction
Zhen Xing, Qi Dai, Zejia Weng +2
Text-guided video prediction (TVP) involves predicting the motion of future frames from the initial frame according to an instruction, which has wide applications in virtual realit…
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Junke Wang, Lingchen Meng, Zejia Weng +3
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performanc…
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-…
BMB: Balanced Memory Bank for Imbalanced Semi-supervised Learning
Wujian Peng, Zejia Weng, Hengduo Li +1
Exploring a substantial amount of unlabeled data, semi-supervised learning (SSL) boosts the recognition performance when only a limited number of labels are provided. However, trad…
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Zejia Weng, Xitong Yang, Ang Li +2
Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive zero-shot learning abilities for image understanding, yet limited effort has been made to investigate CLIP…
Semi-Supervised Vision Transformers
Zejia Weng, Xitong Yang, Ang Li +2
We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised l…
A Multimodal Framework for Video Ads Understanding
Zejia Weng, Lingchen Meng, Rui Wang +2
There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements e…
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…
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…
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Xingjun Ma, Linxi Jiang, Hanxun Huang +3
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research. Obfuscated gradients have previously been found to exist in many defense meth…