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Found 8 papers, 5 papers with code
Learning Vision from Models Rivals Learning Vision from Data
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data.
Scaling Laws of Synthetic Images for Model Training ... for Now
Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e. g., fewer than 0. 5 million images in ImageNet), (2) when the evaluation dataset diverges significantly from the training data, indicating the out-of-distribution scenario, or (3) when synthetic data is used in conjunction with real images, as demonstrated in the training of CLIP models.
Improve Supervised Representation Learning with Masked Image Modeling
On K-Nearest-Neighbor image retrieval evaluation with ImageNet-1k, the same model outperforms the baseline by 1. 32%.
MatFormer: Nested Transformer for Elastic Inference
Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval.
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains.
Global Features are All You Need for Image Retrieval and Reranking
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking.
Ranked #1 on
Image Retrieval
on RParis (Hard)
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs.
Matryoshka Representation Learning
The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.
Ranked #25 on
Image Classification
on ObjectNet
(using extra training data)
