198 citations · 886 across the 20 of their papers we have counts for
20 papers
Probabilistic computation and uncertainty quantification with emerging covariance
Hengyuan Ma, Yang Qi, Li Zhang +2
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities. However…
QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression
Kedeng Tong, Yaojun Wu, Yue Li +3
Learned image compression has exhibited promising compression performance, but variable bitrates over a wide range remain a challenge. State-of-the-art variable rate methods compro…
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
Xiao Han, Xiatian Zhu, Licheng Yu +3
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and imag…
UIGR: Unified Interactive Garment Retrieval
Xiao Han, Sen He, Li Zhang +2
Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment.…
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao +8
Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution a…
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Xiangtai Li, Xia Li, Li Zhang +5
Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by mu…
XingGAN for Person Image Generation
Hao Tang, Song Bai, Li Zhang +2
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The pro…
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Li Zhang, Yuan Yao +1
Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these models require an avalanche of expensive human labe…
Self-supervised Video Object Segmentation
Fangrui Zhu, Li Zhang, Yanwei Fu +2
The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking). We make the foll…
Style Normalization and Restitution for Generalizable Person Re-identification
Xin Jin, Cuiling Lan, Wenjun Zeng +2
Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in…
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
Qibin Hou, Li Zhang, Ming-Ming Cheng +1
Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conven…
Few-shot Action Recognition with Permutation-invariant Attention
Hongguang Zhang, Li Zhang, Xiaojuan Qi +3
Many few-shot learning models focus on recognising images. In contrast, we tackle a challenging task of few-shot action recognition from videos. We build on a C3D encoder for spati…
Global Aggregation then Local Distribution in Fully Convolutional Networks
Xiangtai Li, Li Zhang, Ansheng You +3
It has been widely proven that modelling long-range dependencies in fully convolutional networks (FCNs) via global aggregation modules is critical for complex scene understanding t…
Dual Graph Convolutional Network for Semantic Segmentation
Li Zhang, Xiangtai Li, Anurag Arnab +3
Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation. In contrast to previous work that uses multi-scale feature fusion…
Dynamic Graph Message Passing Networks
Li Zhang, Dan Xu, Anurag Arnab +1
Modelling long-range dependencies is critical for scene understanding tasks in computer vision. Although CNNs have excelled in many vision tasks, they are still limited in capturin…
Fast Online Object Tracking and Segmentation: A Unifying Approach
Qiang Wang, Li Zhang, Luca Bertinetto +2
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbe…
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Flood Sung, Li Zhang, Tao Xiang +2
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can…
Actor-Critic Sequence Training for Image Captioning
Li Zhang, Flood Sung, Feng Liu +4
Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users…
Learning a Deep Embedding Model for Zero-Shot Learning
Li Zhang, Tao Xiang, Shaogang Gong
Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be…
Learning a Discriminative Null Space for Person Re-identification
Li Zhang, Tao Xiang, Shaogang Gong
Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using feat…