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Found 8 papers, 4 papers with code
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image.
FACET: Fairness in Computer Vision Evaluation Benchmark
We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation.
The effectiveness of MAE pre-pretraining for billion-scale pretraining
While MAE has only been shown to scale with the size of models, we find that it scales with the size of the training dataset as well.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 10-shot
(using extra training data)
Learning to Substitute Ingredients in Recipes
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary exploration in everyone's kitchen.
A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Our main insight is that existing joint-embedding based SSL methods can be repurposed for knowledge distillation from a large self-supervised teacher to a small student model.
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations.
The Hidden Uniform Cluster Prior in Self-Supervised Learning
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e. g., SimCLR, VICReg, SwAV, MSN).
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
Ranked #1 on
Copy Detection
on Copydays strong subset
(using extra training data)
