arXiv AI
Precision at Scale: Domain-Specific Datasets On-Demand
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arXiv AI
Jul 2
MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts
arXiv:2607. 00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation.
arXiv Machine Learning
Jul 28
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.
arXiv AI
Aug 20
Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets proposes LSADA, a method that constructs a learning state for each sample based on its loss and loss‑decrease rate to determine a sample‑specific augmentation strength. LSADA also introduces a decoupled data augmentation and diffusion fusion strategy that applies strength‑controlled transformations to class‑relevant regions while generating diverse class‑irrelevant regions, progressively fusing them to enhance image diversity while preserving class semantics. Experiments on nine public datasets demonstrate that LSADA outperforms the current state‑of‑the‑art dynamic GDA method by an average of 4.5% on six natural image datasets and 2.5% on three medical image datasets.
arXiv Computer Vision
Sep 4
Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
The study evaluates self‑supervised learning (SSL) models pretrained on ImageNet‑1k and the Human Protein Atlas (HPA) Field‑of‑View (FOV) for protein localization in microscopy images. DINO‑based Vision Transformer backbones pretrained on either dataset transfer well to the OpenCell dataset, achieving strong performance even without fine‑tuning and improving further when fine‑tuned (0.704 ± 0.027 macro F1 on 17 classes). At the single‑cell level, the HPA‑pretrained model outperforms others in k‑nearest‑neighbor classification across all neighborhood sizes (macro F1 ≥ 0.515).
arXiv AI
Jun 2
GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).
arXiv Machine Learning
Aug 4
Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.