arXiv AI
LE-GDA: Layer Ensemble Gaussian Discriminant Analysis for Training-Free Few-Shot CLIP Adaptation
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arXiv Computer Vision
Oct 16
LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation
LAMP (Layerwise Adaptive Mixing of Predictors) is a training‑free framework that fuses global and regional information from multiple encoder layers for few‑shot vision‑language adaptation. It uses a single forward pass per view to generate descriptors, then applies global and support‑calibrated regional kernel predictors that independently fit label residuals. Validation data select the optimal mixture in class‑score space, preserving native feature dimensions without learned projections, and an extended variant LAMP‑F further adapts text‑anchored class prototypes for improved accuracy across eleven datasets with ViT and ResNet backbones.
arXiv Computer Vision
Sep 16
HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning
arXiv:2604.15678v2 Announce Type: replace Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) met...
arXiv Machine Learning
2d ago
Exact Degeneracy Under Balanced k-Shot Sampling:Consequences for Small-Sample Discriminant Analysis on LLM Embeddings
arXiv:2609.09860v2 Announce Type: replace Abstract: Balanced k-shot sampling, drawing exactly k labeled examples per class, is the standard protocol in few-shot evaluation. We show that it induces an...
arXiv AI
Jun 8
GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection
arXiv:2606. 07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty modeling for few-shot classification and out-of-distribution (OOD) detection.
arXiv Computer Vision
Sep 1
GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
arXiv Machine Learning
Oct 23
Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains
The paper proposes treating few-shot classification as a series of multiple instance verification (MIV) tasks, enabling effective transfer across domains without fine-tuning the backbone. It introduces the MIV-head, a backbone‑agnostic, computationally efficient classification head that achieves competitive accuracy on cross‑domain few‑shot image classification using frozen, pre‑trained backbones. Experiments on an extended Meta‑Dataset benchmark show that the MIV-head outperforms state‑of‑the‑art adapter methods while incurring lower adaptation cost.