223 citations · 374 across the 8 of their papers we have counts for
10 papers
CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery
Yuanpei Liu, Zhenqi He, Jialu Tang +1
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize k…
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
Zhenqi He, Ziqi Jiang, Yuanpei Liu +3
Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image by editing a reference image with a natural-language instruction, without relying on domain-specific ann…
SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
Zhenqi He, Yuanpei Liu, Kai Han
This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether…
Category Discovery: An Open-World Perspective
Zhenqi He, Yuanpei Liu, Kai Han
Category discovery (CD) is an emerging open-world learning task, which aims at automatically categorizing unlabelled data containing instances from unseen classes, given some label…
Hyperbolic Category Discovery
Yuanpei Liu, Zhenqi He, Kai Han
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images,…
DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery
Yuanpei Liu, Kai Han
In this paper, we tackle the problem of Generalized Category Discovery (GCD). Given a dataset containing both labelled and unlabelled images, the objective is to categorize all ima…
ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval
Guanqi Zhan, Yuanpei Liu, Kai Han +2
The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-tr…
UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework
Silin Cheng, Yuanpei Liu, Kai Han
We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the…
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Junde Wu, Wei Ji, Yuanpei Liu +4
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-b…
Distilled Siamese Networks for Visual Tracking
Jianbing Shen, Yuanpei Liu, Xingping Dong +3
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from…