2 citations · 2 across the 7 of their papers we have counts for
8 papers
Decoupled Complementary Spectral-Spatial Learning for Background Representation Enhancement in Hyperspectral Anomaly Detection
Wenping Jin, Li Zhu, Fei Guo
A recent class of hyperspectral anomaly detection methods can be trained once on background datasets and then deployed universally without per-scene retraining or parameter tuning,…
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…
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…
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
Fei Guo, Li Zhu, YiWang Wang +1
In the research field of few-shot learning, the main difference between image-based and video-based is the additional temporal dimension. In recent years, some works have used the…
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
Wenping Jin, Fei Guo, Li Zhu
In the realm of machine learning, the study of anomaly detection and localization within image data has gained substantial traction, particularly for practical applications such as…