53 citations · 195 across the 17 of their papers we have counts for
18 papers
Cascade-DETR: Delving into High-Quality Universal Object Detection
Mingqiao Ye, Lei Ke, Siyuan Li +4
Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not comp…
OVTrack: Open-Vocabulary Multiple Object Tracking
Siyuan Li, Tobias Fischer, Lei Ke +3
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional m…
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
Siyuan Li, Li Sun, Qingli Li
Pre-trained vision-language models like CLIP have recently shown superior performances on various downstream tasks, including image classification and segmentation. However, in fin…
Tracking Every Thing in the Wild
Siyuan Li, Martin Danelljan, Henghui Ding +2
Current multi-category Multiple Object Tracking (MOT) metrics use class labels to group tracking results for per-class evaluation. Similarly, MOT methods typically only associate o…
Style Transformer for Image Inversion and Editing
Xueqi Hu, Qiusheng Huang, Zhengyi Shi +4
Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion…
Unsupervised Deep Manifold Attributed Graph Embedding
Zelin Zang, Siyuan Li, Di Wu +3
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing meth…
Invertible Manifold Learning for Dimension Reduction
Siyuan Li, Haitao Lin, Zelin Zang +3
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning,…
Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
Siyuan Li, Semih Günel, Mirela Ostrek +3
Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose est…
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards
Siyuan Li, Rui Wang, Minxue Tang +1
Hierarchical Reinforcement Learning (HRL) is a promising approach to solving long-horizon problems with sparse and delayed rewards. Many existing HRL algorithms either use pre-trai…
Single Image Deraining: A Comprehensive Benchmark Analysis
Siyuan Li, Iago Breno Araujo, Wenqi Ren +7
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy i…
PFLD: A Practical Facial Landmark Detector
Xiaojie Guo, Siyuan Li, Jinke Yu +5
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use. To simultaneously consider the three concerns, this paper investigates a neat m…
Context-Aware Policy Reuse
Siyuan Li, Fangda Gu, Guangxiang Zhu +1
Transfer learning can greatly speed up reinforcement learning for a new task by leveraging policies of relevant tasks. Existing works of policy reuse either focus on only selecting…
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
Siyuan LI, Wenqi Ren, Jiawan Zhang +2
Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attractin…
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
Xinqing Guo, Zhang Chen, Siyuan Li +2
Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Siyuan Li, Chongjie Zhang
Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source…
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
Zhang Chen, Xinqing Guo, Siyuan Li +2
Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes. The techniques are essentially complementary: DfD can robustly handle repetitive te…
Visual Data Deblocking using Structural Layer Priors
Xiaojie Guo
The blocking artifact frequently appears in compressed real-world images or video sequences, especially coded at low bit rates, which is visually annoying and likely hurts the perf…