4 citations · 7 across the 8 of their papers we have counts for
8 papers
Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation
Chao Li, Tianhong Li, Sai Vidyaranya Nuthalapati +9
Unifying text-image contrastive learning and text-to-image (T2I) generation in a single end-to-end model is challenging because the two objectives demand opposing masking regimes:…
Single-Teacher View Augmentation: Boosting Knowledge Distillation via Angular Diversity
Seonghoon Yu, Dongjun Nam, Dina Katabi +1
Knowledge Distillation (KD) aims to train a lightweight student model by transferring knowledge from a large, high-capacity teacher. Recent studies have shown that leveraging diver…
Learning Vision from Models Rivals Learning Vision from Data
Yonglong Tian, Lijie Fan, Kaifeng Chen +3
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dat…
Scaling Laws of Synthetic Images for Model Training ... for Now
Lijie Fan, Kaifeng Chen, Dilip Krishnan +3
Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting cu…
Return of Unconditional Generation: A Self-supervised Representation Generation Method
Tianhong Li, Dina Katabi, Kaiming He
Unconditional generation -- the problem of modeling data distribution without relying on human-annotated labels -- is a long-standing and fundamental challenge in generative models…
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian +6
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically…
Unsupervised Object Localization with Representer Point Selection
Yeonghwan Song, Seokwoo Jang, Dina Katabi +1
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional…
Unsupervised Domain Generalization by Learning a Bridge Across Domains
Sivan Harary, Eli Schwartz, Assaf Arbelle +11
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental cap…