83 citations · 213 across the 11 of their papers we have counts for
9 papers · 1 filter
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Shengkun Tang, Yaqing Wang, Zhenglun Kong +6
Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with incre…
Visual Relation Grounding in Videos
Junbin Xiao, Xindi Shang, Xun Yang +2
In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV). The task aims at spatio-temporally localizing the given relations in the form of subject-pr…
The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation
Tao Wang, Yu Li, Bingyi Kang +5
Most existing object instance detection and segmentation models only work well on fairly balanced benchmarks where per-category training sample numbers are comparable, such as COCO…
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Yu Li, Tao Wang, Bingyi Kang +4
Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored.In this work, we provide th…
Asymmetric GAN for Unpaired Image-to-image Translation
Yu Li, Sheng Tang, Rui Zhang +3
Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN pro…
Consensus Feature Network for Scene Parsing
Tianyi Wu, Sheng Tang, Rui Zhang +2
Scene parsing is challenging as it aims to assign one of the semantic categories to each pixel in scene images. Thus, pixel-level features are desired for scene parsing. However, c…
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Tianyi Wu, Sheng Tang, Rui Zhang +2
Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect partial information. To tackle this issue, we firstly p…
Style Separation and Synthesis via Generative Adversarial Networks
Rui Zhang, Sheng Tang, Yu Li +4
Style synthesis attracts great interests recently, while few works focus on its dual problem "style separation". In this paper, we propose the Style Separation and Synthesis Genera…
CGNet: A Light-weight Context Guided Network for Semantic Segmentation
Tianyi Wu, Sheng Tang, Rui Zhang +1
The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuita…