Search Results for author:
Found 8 papers, 5 papers with code
Object Localization under Single Coarse Point Supervision
In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.
P2P-Loc: Point to Point Tiny Person Localization
As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way.
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Inspired by that, a simple yet effective approach is proposed, known as group sampling, which gathers groups of samples from the same class into a mini-batch.
Ranked #1 on
Unsupervised Person Re-Identification
on MSMT17
Representation Learning
Unsupervised Person Re-Identification
SM+: Refined Scale Match for Tiny Person Detection
Detecting tiny objects ( e. g., less than 20 x 20 pixels) in large-scale images is an important yet open problem.
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
Effective Fusion Factor in FPN for Tiny Object Detection
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Scale Match for Tiny Person Detection
In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising directionfor tiny object detection in a long distance and with mas-sive backgrounds.
