4 citations · 8 across the 8 of their papers we have counts for
9 papers
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…
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…
LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Gare, Jinjin Tian +6
We tackle the challenge of predicting models' Out-of-Distribution (OOD) performance using in-distribution (ID) measurements without requiring OOD data. Existing evaluations with "E…
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…
Revisiting Few-Shot Object Detection with Vision-Language Models
Anish Madan, Neehar Peri, Shu Kong +1
The era of vision-language models (VLMs) trained on web-scale datasets challenges conventional formulations of "open-world" perception. In this work, we revisit the task of few-sho…
Long-Tailed 3D Detection via Multi-Modal Fusion
Yechi Ma, Neehar Peri, Achal Dave +3
Contemporary autonomous vehicle (AV) benchmarks have significantly advanced multimodal (LiDAR+RGB) 3D detection. However, despite the naturally long-tailed distribution of object c…