27 citations · 68 across the 13 of their papers we have counts for
14 papers
MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers
Jiarui Chen, Zeqiang Lai, Jiangshan Wang +5
Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation. H…
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
Bo Pang, Yifan Zhang, Yaoyi Li +2
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised l…
Semantic Segmentation by Early Region Proxy
Yifan Zhang, Bo Pang, Cewu Lu
Typical vision backbones manipulate structured features. As a compromise, semantic segmentation has long been modeled as per-point prediction on dense regular grids. In this work,…
PGT: A Progressive Method for Training Models on Long Videos
Bo Pang, Gao Peng, Yizhuo Li +1
Convolutional video models have an order of magnitude larger computational complexity than their counterpart image-level models. Constrained by computational resources, there is no…
TDAF: Top-Down Attention Framework for Vision Tasks
Bo Pang, Yizhuo Li, Jiefeng Li +3
Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-…
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation
Hanwen Cao, Yongyi Lu, Cewu Lu +3
Recent works of point clouds show that mulit-frame spatio-temporal modeling outperforms single-frame versions by utilizing cross-frame information. In this paper, we further improv…