1 citations · 1 across the 7 of their papers we have counts for
7 papers
LV-OSD: Language-Vision-Complementary Open-Set Object Detection
Yupeng Zhang, Ruize Han, Wei Feng +2
Object detection is an important task in computer vision, which aims to detect the objects of interest. through the given category list or query images. In this work, we propose a…
VFMSDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
Yupeng Zhang, Ruize Han, Ningnan Guo +3
Real-world weather, illumination, and imaging variations often induce severe domain shifts, degrading single-source detectors in unseen environments. Existing single-domain general…
Generalist Graph Anomaly Detection via Prototype-Based Distillation
Yiming Xu, Zihan Chen, Zhen Peng +4
Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs,…
The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Kai Liu, Haoyang Yue, Zeli Lin +65
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high…
OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking
Zekun Qian, Ruize Han, Wei Feng +3
We study a novel yet practical problem of open-corpus multi-object tracking (OCMOT), which extends the MOT into localizing, associating, and recognizing generic-category objects of…
Unveiling the Power of Self-supervision for Multi-view Multi-human Association and Tracking
Wei Feng, Feifan Wang, Ruize Han +2
Multi-view multi-human association and tracking (MvMHAT), is a new but important problem for multi-person scene video surveillance, aiming to track a group of people over time in e…
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
Yuewei Lin, Jing Chen, Yu Cao +4
This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all…