activity
20222024
most citedOn the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning

6 citations · 8 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Dynamic Token Reduction during Generation for Vision Language Models

Xiaoyu Liang, Chaofeng Guan, Jiaying Lu +3

Vision-Language Models (VLMs) have achieved notable success in multimodal tasks but face practical limitations due to the quadratic complexity of decoder attention mechanisms and a…

cs.CV2024

ST: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming

Jiedong Zhuang, Lu Lu, Ming Dai +4

Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual token…

cs.CV2024

FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance

Jiedong Zhuang, Jiaqi Hu, Lianrui Mu +4

CLIP has achieved impressive zero-shot performance after pre-training on a large-scale dataset consisting of paired image-text data. Previous works have utilized CLIP by incorporat…

cs.CV20242 cited

UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing

Jianhong Bai, Tianyu He, Yuchi Wang +4

Recent advances in text-guided video editing have showcased promising results in appearance editing (e.g., stylization). However, video motion editing in the temporal dimension (e.…

cs.CV20236 cited

On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning

Jianhong Bai, Zuozhu Liu, Hualiang Wang +4

Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the ma…

cs.CV2022

Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning

Hualiang Wang, Siming Fu, Xiaoxuan He +3

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little at…