915 citations · 1.9k across the 40 of their papers we have counts for
9 papers · 1 filter
AiluRus: A Scalable ViT Framework for Dense Prediction
Jin Li, Yaoming Wang, Xiaopeng Zhang +6
Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences,…
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
Dongsheng Jiang, Yuchen Liu, Songlin Liu +6
Multi-modal Large Language Models (MLLMs) have made significant strides in expanding the capabilities of Large Language Models (LLMs) through the incorporation of visual perception…
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Shuangrui Ding, Peisen Zhao, Xiaopeng Zhang +3
Transformers have become the primary backbone of the computer vision community due to their impressive performance. However, the unfriendly computation cost impedes their potential…
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
Bowen Shi, Xiaopeng Zhang, Yaoming Wang +5
Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their…
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
Lingxi Xie, Longhui Wei, Xiaopeng Zhang +4
The AI community has been pursuing algorithms known as artificial general intelligence (AGI) that apply to any kind of real-world problem. Recently, chat systems powered by large l…
ControlVideo: Training-free Controllable Text-to-Video Generation
Yabo Zhang, Yuxiang Wei, Dongsheng Jiang +3
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temp…
Segment Anything in 3D with Radiance Fields
Jiazhong Cen, Jiemin Fang, Zanwei Zhou +5
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results. This paper aims to generalize SAM to segment 3D obj…
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
Ning Liao, Xiaopeng Zhang, Min Cao +1
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown cl…
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
Ning Liao, Bowen Shi, Xiaopeng Zhang +3
Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as th…