207 citations · 786 across the 32 of their papers we have counts for
21 papers · 1 filter
Multi-Task Learning with Multi-Query Transformer for Dense Prediction
Yangyang Xu, Xiangtai Li, Haobo Yuan +2
Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each tas…
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
Xiangtai Li, Wenwei Zhang, Jiangmiao Pang +4
This paper presents Video K-Net, a simple, strong, and unified framework for fully end-to-end video panoptic segmentation. The method is built upon K-Net, a method that unifies ima…
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
Xiangtai Li, Shilin Xu, Yibo Yang +3
Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part segmentation into one task. Previous work mainly utilizes separated approaches to handle thing, stuff,…
Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition
Shilin Xu, Xiangtai Li, Jingbo Wang +3
Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation a…
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
Qianyu Zhou, Xiangtai Li, Lu He +5
Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance…
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
Haobo Yuan, Xiangtai Li, Yibo Yang +5
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously. The previous…
Global Aggregation then Local Distribution for Scene Parsing
Xiangtai Li, Li Zhang, Guangliang Cheng +4
Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherentl…
Improving Video Instance Segmentation via Temporal Pyramid Routing
Xiangtai Li, Hao He, Yibo Yang +5
Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are ma…
Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation
Chen Shi, Xiangtai Li, Yanran Wu +2
Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic contex…
Fast and Accurate Scene Parsing via Bi-direction Alignment Networks
Yanran Wu, Xiangtai Li, Chen Shi +5
In this paper, we propose an effective method for fast and accurate scene parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one representative work BiSeNet~\…
BoundarySqueeze: Image Segmentation as Boundary Squeezing
Hao He, Xiangtai Li, Yibo Yang +5
This paper proposes a novel method for high-quality image segmentation of both objects and scenes. Inspired by the dilation and erosion operations in morphological image processing…
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Xiangtai Li, Hao He, Xia Li +6
Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two…
Enhanced Boundary Learning for Glass-like Object Segmentation
Hao He, Xiangtai Li, Guangliang Cheng +5
Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. Howev…
Towards Efficient Scene Understanding via Squeeze Reasoning
Xiangtai Li, Xia Li, Ansheng You +5
Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, i…
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…
Boundary Content Graph Neural Network for Temporal Action Proposal Generation
Yueran Bai, Yingying Wang, Yunhai Tong +3
Temporal action proposal generation plays an important role in video action understanding, which requires localizing high-quality action content precisely. However, generating temp…
Semantic Flow for Fast and Accurate Scene Parsing
Xiangtai Li, Ansheng You, Zhen Zhu +4
In this paper, we focus on designing effective method for fast and accurate scene parsing. A common practice to improve the performance is to attain high resolution feature maps wi…
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…
Reprojection R-CNN: A Fast and Accurate Object Detector for 360° Images
Pengyu Zhao, Ansheng You, Yuanxing Zhang +3
360° images are usually represented in either equirectangular projection (ERP) or multiple perspective projections. Different from the flat 2D images, the detection task is challen…
GFF: Gated Fully Fusion for Semantic Segmentation
Xiangtai Li, Houlong Zhao, Lei Han +2
Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Net…