1 citations · 1 across the 3 of their papers we have counts for
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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…
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…
Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning
Tian Liu, Huixin Zhang, Shubham Parashar +1
Few-shot recognition (FSR) aims to train a classification model with only a few labeled examples of each concept concerned by a downstream task, where data annotation cost can be p…
Roadside Monocular 3D Detection Prompted by 2D Detection
Yechi Ma, Yanan Li, Wei Hua +1
Roadside monocular 3D detection requires detecting objects of predefined classes in an RGB frame and predicting their 3D attributes, such as bird's-eye-view (BEV) locations. It has…
The Neglected Tails in Vision-Language Models
Shubham Parashar, Zhiqiu Lin, Tian Liu +5
Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive a…