349 citations · 1.3k across the 44 of their papers we have counts for
8 papers · 1 filter
Video Salient Object Detection via Contrastive Features and Attention Modules
Yi-Wen Chen, Xiaojie Jin, Xiaohui Shen +1
Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neu…
Conflict-Averse Gradient Descent for Multi-task Learning
Bo Liu, Xingchao Liu, Xiaojie Jin +2
The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learnin…
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Mingyu Ding, Xiaochen Lian, Linjie Yang +4
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored…
Refiner: Refining Self-attention for Vision Transformers
Daquan Zhou, Yujun Shi, Bingyi Kang +6
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most…
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
DeepViT: Towards Deeper Vision Transformer
Daquan Zhou, Bingyi Kang, Xiaojie Jin +5
Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs)that can be…
All Tokens Matter: Token Labeling for Training Better Vision Transformers
Zihang Jiang, Qibin Hou, Li Yuan +5
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs). Different from the standard training objective of ViT…
AutoSpace: Neural Architecture Search with Less Human Interference
Daquan Zhou, Xiaojie Jin, Xiaochen Lian +4
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automati…