This work introduces ViTAMINS, a method that integrates synthetic hard negatives into unsupervised vision transformer pretraining to improve representation quality and motivate reconsidering contrastive learning as a simpler yet powerful alternative to dominant generative and self-distillation approaches.
Abstract
We introduce ViTAMINS, a method that integrates synthetic hard negatives into unsupervised vision transformer pretraining to improve representation quality. Our approach is thoroughly benchmarked on ImageNet and transfer learning, image retrieval, copy detection, and image, video segmentation tasks. Notably, our proposed negatives give rise to emergent properties, where learned representations contain explicit information about the semantic content of an image and serve as excellent classifiers (up to +11.3% over baselines). ViTAMINS achieves these benefits through simple modifications to existing contrastive frameworks and outperforms competing methods while being more resource efficient, e.g., our ViT-B surpasses V-JEPA with ViT-L. Our findings motivate reconsidering contrastive learning as a simpler yet powerful alternative to dominant generative and self-distillation approaches.
After running thorough benchmark image recognition trials, XViT-Net proved to be more accurate, resilient, and data efficient than the fully supervised Vision Transformer baselines and to be a trustworthy, interpretable, and data-efficient foundation for future image recognition systems.
Thilagavathi P, Praveen Kumar, Prince Kumar et al.· ITM Web of Conferences· 0 citations
Aether is introduced, a simple plug-in method that applies diffusion-style random perturbations in the embedding space via controlled alpha-mixing, specifically designed to provide isotropic regularization that remains semantically consistent.
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Medical image classification often suffers from the scarcity of labeled data, which limits the performance of deep learning models. We propose a self-supervised contrastive learning framework that integrates Vision Transformers to address this data efficiency challenge. The method first applies rigorous image preproces...
Arijit Gandhi· Journal of Machine Learning...· 0 citations
This narrative review examines deep learning for static visual scene classification across indoor, outdoor, and aerial imagery and concludes that useful progress should be assessed through reproducible gains under matched protocols and credible transfer to new environments, rather than isolated accuracy values.
Sandeep Kumar Aazad, Kritika Chaudhary, Taniya Saini et al.· Journal of Computing and Dat...· 0 citations
This study introduces ProbeGen, a benchmark for zero-shot generative perception that casts monocular depth estimation, referring/reasoning segmentation, and object counting as conditional generation tasks specified through text prompts, and compares 20 models in total.
Vision Transformer (ViT) architectures have emerged as powerful alternatives to conventional convolutional neural networks for image classification because they model long-range visual dependencies through self-attention. This paper presents a software-based image classification framework that uses a pre-trained ViT-Ba...
Sadeqa and Dr. Bitla Prabhakar· International Journal of Adv...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 29, 2026
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
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