Categories: FAANG

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7× Faster Pre-training on Web-scale Image-Text Data

Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings. However, pairwise similarity computation in contrastive loss between image and text pairs poses computational challenges. This paper presents a novel weakly supervised pre-training of vision models on web-scale image-text data. The proposed method reframes pre-training on image-text data as a classification task. Consequently, it eliminates the need for pairwise similarity computations in contrastive loss, achieving a remarkable 2.7…
AI Generated Robotic Content

Recent Posts

Are there any open source alternatives to this?

I know there are models available that can fill in or edit parts, but I'm…

21 hours ago

The future of engineering belongs to those who build with AI, not without it

As we look ahead, the relationship between engineers and AI systems will likely evolve from…

22 hours ago

The 8 Best Handheld Vacuums, Tested and Reviewed (2025)

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

22 hours ago

I really miss the SD 1.5 days

submitted by /u/Dwanvea [link] [comments]

2 days ago

Latent Bridge Matching: Jasper’s Game-Changing Approach to Image Translation

Discover how latent bridge matching, pioneered by the Jasper research team, transforms image-to-image translation with…

2 days ago

A Gentle Introduction to SHAP for Tree-Based Models

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost…

2 days ago