Few-Shot Image Classification

Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot image classification remains challenging. To bridge this gap, we prese…

Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling

2026-06-26 · Zhaoning Shi, Bo Ma, Hao Xu, Zepeng Yang 외 arxiv

This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framework based on biological hippocampal memory modeling. This framework in…

Few-Shot Image Classification Novel Object Detection Image Restoration

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models

2026-05-25 · Kaixiang Chen, Pengfei Fang, Hui Xue arxiv

Adapting large vision-language models (VLMs) such as CLIP to downstream tasks remains challenging, as full fine-tuning is computationally prohibitive and prone to overfitting in low-data regimes. Parameter-efficient fine…

parameter-efficient fine-tuning Few-Shot Image Classification Computational Efficiency

A$_3$B$_2$: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning

2026-05-13 · Yiyun Zhou, Zhonghua Jiang, Wenkang Han, Kunxi Li 외 arxiv

Efficient transfer learning methods for large-scale vision-language models ($e.g.$, CLIP) enable strong few-shot transfer, yet existing adaptation methods follow a fixed fine-tuning paradigm that implicitly assumes a uni…

Few-Shot Image Classification Few-Shot Learning Transfer Learning

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

2026-05-13 · Giries Abu Ayoub, Morad Tukan, Loay Mualem arxiv

Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are independent of contextual cues. In real-world settings, however, exampl…

Few-Shot Image Classification Few-Shot Audio Classification

Cross-Modal Prototype Alignment and Mixing for Training-Free Few-Shot Classification

2026-03-25 · Dipam Goswami, Simone Magistri, Gido M. van de Ven, Bartłomiej Twardowski 외 arxiv

Vision-language models (VLMs) like CLIP are trained with the objective of aligning text and image pairs. To improve CLIP-based few-shot image classification, recent works have observed that, along with text embeddings, i…

Few-Shot Image Classification

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