3 citations · 4 across the 11 of their papers we have counts for
8 papers · 1 filter
PACO: Proxy-Task Alignment and Online Calibration for On-the-Fly Category Discovery
Weidong Tang, Bohan Zhang, Zhixiang Chi +3
On-the-Fly Category Discovery (OCD) requires a model, trained on an offline support set, to recognize known classes while discovering new ones from an online streaming sequence. Ex…
Learning through Creation: A Hash-Free Framework for On-the-Fly Category Discovery
Bohan Zhang, Weidong Tang, Zhixiang Chi +4
On-the-Fly Category Discovery (OCD) aims to recognize known classes while simultaneously discovering emerging novel categories during inference, using supervision only from known c…
TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery
Yanan Wu, Yuhan Yan, Tailai Chen +5
On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream, using a model trained only on la…
Generalization in Online Reinforcement Learning for Mobile Agents
Li Gu, Zihuan Jiang, Zhixiang Chi +5
Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions and interacting with the screen. While rec…
Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation
Zhixiang Chi, Yanan Wu, Li Gu +5
CLIP exhibits strong visual-textual alignment but struggle with open-vocabulary segmentation due to poor localization. Prior methods enhance spatial coherence by modifying intermed…
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation
Wenbo Xu, Yanan Wu, Haoran Jiang +3
Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated…
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Yanan Wu, Zhixiang Chi, Yang Wang +2
Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and…
MetaGCD: Learning to Continually Learn in Generalized Category Discovery
Yanan Wu, Zhixiang Chi, Yang Wang +1
In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel class…