1 code implementation • 28 Dec 2023 • Yonglong Tian, Lijie Fan, KaiFeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data.
1 code implementation • 7 Dec 2023 • Lijie Fan, KaiFeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian
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.
no code implementations • 1 Dec 2023 • KaiFeng Chen, Daniel Salz, Huiwen Chang, Kihyuk Sohn, Dilip Krishnan, Mojtaba Seyedhosseini
On K-Nearest-Neighbor image retrieval evaluation with ImageNet-1k, the same model outperforms the baseline by 1. 32%.
2 code implementations • 11 Oct 2023 • Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, KaiFeng Chen, Inderjit Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham Kakade, Ali Farhadi, Prateek Jain
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.
no code implementations • ICCV 2023 • Nikolaos-Antonios Ypsilantis, KaiFeng Chen, Bingyi Cao, Mário Lipovský, Pelin Dogan-Schönberger, Grzegorz Makosa, Boris Bluntschli, Mojtaba Seyedhosseini, Ondřej Chum, André Araujo
In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains.
2 code implementations • ICCV 2023 • Shihao Shao, KaiFeng Chen, Arjun Karpur, Qinghua Cui, Andre Araujo, Bingyi Cao
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)
no code implementations • 26 May 2023 • Yunhao Ge, Jie Ren, Jiaping Zhao, KaiFeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan
Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs.
4 code implementations • 26 May 2022 • Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, KaiFeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi
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)