[HTML][HTML] Mask Mixup Model: Enhanced Contrastive Learning for Few-Shot Learning
Few-shot image classification aims to improve the performance of traditional image
classification when faced with limited data. Its main challenge lies in effectively utilizing …
classification when faced with limited data. Its main challenge lies in effectively utilizing …
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection
This paper presents a novel approach to one-class classifier fusion through locally adaptive
learning with dynamic $\ell $ p-norm constraints. We introduce a framework that dynamically …
learning with dynamic $\ell $ p-norm constraints. We introduce a framework that dynamically …
IDEA: Image Description Enhanced CLIP-Adapter
Z Ye, F Jiang, Q Wang, K Huang, J Huang - arxiv preprint arxiv …, 2025 - arxiv.org
CLIP (Contrastive Language-Image Pre-training) has attained great success in pattern
recognition and computer vision. Transferring CLIP to downstream tasks (eg zero-or few …
recognition and computer vision. Transferring CLIP to downstream tasks (eg zero-or few …
Adaptive Feature Representation Based On Contrastive Learning For Few-Shot Classification
Few-shot image classification is a challenging task aim to classific unseen images in
scenarios with limited samples. Recent work demonstrate that local discriminative features …
scenarios with limited samples. Recent work demonstrate that local discriminative features …
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class
Few-shot image classification aims to classify novel classes with few labeled samples.
Recent research indicates that deep local descriptors have better representational …
Recent research indicates that deep local descriptors have better representational …
DeepTTS: Enhanced Transformer-Based Text Spotter via Deep Interaction Between Detection and Recognition Tasks
Transformer architectures have sparked significant interest in the field of end-to-end text
detection and recognition, also known as text spotting. Given that most existing methods rely …
detection and recognition, also known as text spotting. Given that most existing methods rely …
Few-Shot Image Classification\\Set-Based Metric\\Self-Adapting Weighting
Y Chen, Z Xu, J Wang, ZX Yang - Available at SSRN 5076860 - papers.ssrn.com
Few-shot image classification aims to learn a classifier from limited labeled data. Though the
existing methods have achieved significant improvement, they are still challenging to …
existing methods have achieved significant improvement, they are still challenging to …