1k citations · 3.1k across the 56 of their papers we have counts for
10 papers · 1 filter
Towards Panoptic 3D Parsing for Single Image in the Wild
Sainan Liu, Vincent Nguyen, Yuan Gao +2
Performing single image holistic understanding and 3D reconstruction is a central task in computer vision. This paper presents an integrated system that performs dense scene labeli…
ViTGAN: Training GANs with Vision Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang +3
Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if…
Long Short-Term Transformer for Online Action Detection
Mingze Xu, Yuanjun Xiong, Hao Chen +4
We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged s…
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models
Tyler A. Chang, Yifan Xu, Weijian Xu +1
In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are…
Compatibility-aware Heterogeneous Visual Search
Rahul Duggal, Hao Zhou, Shuo Yang +4
We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery i…
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries
Qi Dong, Zhuowen Tu, Haofu Liao +3
Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both th…
Pose Recognition with Cascade Transformers
Ke Li, Shijie Wang, Xiang Zhang +3
In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them in…
Co-Scale Conv-Attentional Image Transformers
Weijian Xu, Yifan Xu, Tyler Chang +1
In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, t…
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
Zhaowei Cai, Avinash Ravichandran, Subhransu Maji +3
We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher bas…
Line Segment Detection Using Transformers without Edges
Yifan Xu, Weijian Xu, David Cheung +1
In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junctio…