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arXiv Machine Learning
Sep 11

Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions

The paper critically evaluates common few‑shot learning protocols that rely on pre‑training a model on a large auxiliary set with classes disjoint from the target but drawn from the same visual domain. By comparing no pre‑training, class‑disjoint in‑domain pre‑training, supervised out‑of‑domain pre‑training, and label‑free out‑of‑domain pre‑training across eight datasets and three architectures, the authors find that in‑domain pre‑training yields a 33.41‑point average improvement, while out‑of‑domain pre‑training offers a 23.75‑point gain, revealing a 9.66‑point optimistic bias due to domain overlap. They also demonstrate that a label‑free augmentation strategy can match supervised out‑of‑domain performance and propose a descriptor‑based source‑selection method that closely approximates oracle selection, underscoring the need to move beyond in‑domain pre‑training as the default evaluation protocol.

By Alejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier Gallego
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arXiv Computer Vision
Sep 22

Training-Free Spectral Transductive Refinement for Cross-Domain Few-Shot Classification

The paper introduces Spectral Transductive Refinement (STR), a training‑free method that refines class prototypes at test time using the geometry of a joint k‑nearest‑neighbour graph and a normalized‑Laplacian spectral coordinate system. STR operates solely on frozen visual embeddings, iteratively updating pseudo‑labelled queries to improve one‑shot and few‑shot classification under domain shift. Experiments on ResNet‑18 and ResNet‑10 backbones show STR outperforms single‑prototype baselines and rivals meta‑trained cross‑domain few‑shot methods, achieving the best 1‑shot average across eight target domains.

By Fahim Rahman, S. M. Tanjeeb Meheran Rohan, Md. Taimum Ibne Sayed, Asaduzzaman Herok, Md. Bakhtiar Hasan
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arXiv Computer Vision
Sep 14

Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap

ReVisIT is a train‑free framework that turns retrieved image‑label pairs into units of visual thought, combining structured class definitions, multimodal retrieval, and alternating user/assistant injection before joint decoding. On several benchmarks—including Fast Open MiniImageNet, Bongard‑OpenWorld, and the newly released MAAC‑Bench—ReVisIT achieves performance comparable to or surpassing large, trained models while using far fewer parameters. The approach demonstrates that high‑quality retrieval and a simple turns layer can provide a universal performance boost across diverse multimodal tasks.

By Bingchen Huang, Zhiling Wang, Yifu Chen, Yuanchao Du
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arXiv Computer Vision
4d ago

Query-Conditioned Prototype Adaptation for Cross-Domain Few-Shot Learning: Single-Query Inference, Controlled Comparisons, and Failure Modes

The paper introduces the Within-Instance Prototypical Transformer (WIPT), a method that performs single-query test-time prototype adaptation by jointly transforming an unlabelled query with labelled support embeddings to form query-specific class means. Using a frozen ViT-S/16 encoder trained on miniImageNet and evaluated on CUB, EuroSAT, and ISIC, WIPT improves 1‑shot performance on CUB and EuroSAT but not on ISIC, while in 5‑shot settings it outperforms a support‑only Transformer on ISIC but remains weaker than ProtoNet overall. The study also explores multi‑query processing, memory and latency trade‑offs, and analyzes how WIPT alters uncertain versus confident predictions, concluding that the method offers a streaming‑compatible test‑time adaptation that can enhance low‑shot cross‑domain decisions without target‑time optimization.

By Rushab Rasik Karania, Tomas Maul
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