297 citations · 390 across the 13 of their papers we have counts for
6 papers · 1 filter
OneFormer: One Transformer to Rule Universal Image Segmentation
Jitesh Jain, Jiachen Li, MangTik Chiu +3
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, n…
Efficient Image Generation with Variadic Attention Heads
Steven Walton, Ali Hassani, Xingqian Xu +2
While the integration of transformers in vision models have yielded significant improvements on vision tasks they still require significant amounts of computation for both training…
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Yulin Wang, Yang Yue, Xinhong Xu +6
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e.g., allocating the majority of c…
Dilated Neighborhood Attention Transformer
Ali Hassani, Humphrey Shi
Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain…
DiSparse: Disentangled Sparsification for Multitask Model Compression
Xinglong Sun, Ali Hassani, Zhangyang Wang +2
Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglem…
Neighborhood Attention Transformer
Ali Hassani, Steven Walton, Jiachen Li +2
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision. NA is a pixel-wise operation, localizing self attention (SA)…