From the 1 of 10 linked papers with an AI index.
10 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…
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…
Long-Tailed 3D Detection via Multi-Modal Fusion
Yechi Ma, Neehar Peri, Achal Dave +3
Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors. While class labels naturally follow a long-tailed distribution in the real world…
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…