From the 1 of 5 linked papers with an AI index.
5 papers
Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors
Tian Liu, Anwesha Basu, James Caverlee +1
The paper introduces a training-free post-hoc correction framework that uses large multimodal models to improve few-shot expert models for visual species recognition, boosting accu…
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
Few-Shot Recognition (FSR) tackles classification tasks by training with minimal task-specific labeled data. Prevailing methods adapt or finetune a pretrained Vision-Language Model…
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…