Abstract Vision Transformers (ViTs) have demonstrated powerful representation ability in
various visual tasks thanks to their intrinsic data-hungry nature. However, we unexpectedly …

Estimating the depths of equirectangular (ie, 360) images (EIs) is challenging given the
distorted 180 x 360 field-of-view, which is hard to be addressed via convolutional neural …

In this work we propose a novel discriminative framework for dexterous grasp generation
named Dexterous Grasp TRansformer (DGTR) capable of predicting a diverse set of feasible …

We study the domain adaptation task for action recognition, namely domain adaptive action
recognition, which aims to effectively transfer action recognition power from a label-sufficient …

Abstract Vision Transformer (ViT) is widely used in the field of computer vision, in ViT, there
are four main steps, which are “four secrets”, such as patch division, token selection, position …

Task-conditional architecture offers advantage in parameter efficiency but falls short in
performance compared to state-of-the-art multi-decoder methods. How to trade off …

Transformer-based methods have proven to be ef-1 fective in achieving long-distance
modeling, cap-2 turing the spatial and spectral information, and 3 exhibiting strong inductive …

Transformers have astounding representational power but typically consume considerable
computation which is quadratic with image resolution. The prevailing Swin transformer …

Abstract Vision Transformer (ViT) faces obstacles in wide application due to its huge
computational cost. Almost all existing studies on compressing ViT adopt the manner of …

In the task of human behavior detection, video classification based on deep learning has
become a prevalent technique. The existing models are limited due to an inadequate …