16 citations · 30 across the 27 of their papers we have counts for
18 papers · 1 filter
From Visual Widgets to UI Code: Efficient Tool-Grounded Generation
Houston H. Zhang, Tao Zhang, Li Gu +5
Existing screenshot-to-code systems face a trade-off between flexibility and controllability. Direct multimodal generation can hallucinate visible details, whereas structured pipel…
DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation
Siobhan Reid, Zhixiang Chi, Li Gu +3
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This setting is more realistic than typical…
No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
Linlian Jiang, Wentao Ju, Sadman Rakib Pinon +4
LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations for retraining is impractical. Test-time…
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…