4 papers · 1 filter
Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
Tian Liu, Anwesha Basu, James Caverlee +1
Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-spec…
Enabling Validation for Robust Few-Shot Recognition
Hanxin Wang, Tian Liu, Shu Kong
A key challenge in Few-Shot Recognition (FSR) is the scarcity of labeled task-specific data for training. Contemporary methods mitigate this challenge by exploiting pretrained Visi…
CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Pardis Taghavi, Tian Liu, Renjie Li +2
Instance segmentation demands costly per-pixel annotations and computationally expensive models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that c…
UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark
Hasnat Md Abdullah, Tian Liu, Kangda Wei +2
Localizing unusual activities, such as human errors or surveillance incidents, in videos holds practical significance. However, current video understanding models struggle with loc…