6 papers
Graph Feedback Bandits on Similar Arms: With and Without Graph Structures
Han Qi, Fei Guo, Li Zhu +1
In this paper, we study the stochastic multi-armed bandit problem with graph feedback. Motivated by applications in clinical trials and recommendation systems, we assume that two a…
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition
Fei Guo, YiKang Wang, Han Qi +2
Few-shot action recognition is an emerging field in computer vision, primarily focused on meta-learning within the same domain. However, challenges arise in real-world scenario dep…
Graph Feedback Bandits with Similar Arms
Han Qi, Guo Fei, Li Zhu
In this paper, we study the stochastic multi-armed bandit problem with graph feedback. Motivated by the clinical trials and recommendation problem, we assume that two arms are conn…
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-DF)
Fei Guo, YiKang Wang, Han Qi +2
In recent years, few-shot action recognition has attracted increasing attention. It generally adopts the paradigm of meta-learning. In this field, overcoming the overlapping distri…
Forced Exploration in Bandit Problems
Han Qi, Fei Guo, Li Zhu
The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have d…
Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CPC)
Fei Guo, Li Zhu, YiKang Wang +1
Recently, few-shot action recognition has significantly progressed by learning the feature discriminability and designing suitable comparison methods. Still, there are the followin…