Search Results for author:
Found 10 papers, 6 papers with code
D$^{\bf{3}}$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos
In this paper, the duplicate detection is newly and precisely defined as occlusion misreporting on the same athlete by multiple detection boxes in one frame.
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Self-training has shown great potential in semi-supervised learning.
SparseTT: Visual Tracking with Sparse Transformers
In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking.
Segmentation-Reconstruction-Guided Facial Image De-occlusion
The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module.
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70. 8% to 73. 6%, achieving new state-of-the-art.
Ranked #3 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Pixel Sampling for Style Preserving Face Pose Editing
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Missing textures in the incomplete UV map are further full-filled by the UV generator.
Visual Grounding with Transformers
In this paper, we propose a transformer based approach for visual grounding.
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes.
Ranked #1 on
Visual Object Tracking
on OTB-2015
ARM: A Confidence-Based Adversarial Reweighting Module for Coarse Semantic Segmentation
Experiments on standard datasets shows our ARM can bring consistent improvements for both coarse annotations and fine annotations.
