37 citations · 92 across the 11 of their papers we have counts for
5 papers · 1 filter
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek, Florian Bordes, Pietro Astolfi +3
Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of available curated captions are far too short…
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer
Jacob Zhiyuan Fang, Skyler Zheng, Vasu Sharma +1
To build scalable models for challenging real-world tasks, it is important to learn from diverse, multi-modal data in various forms (e.g., videos, text, and images). Among the exis…
FLAP: Fast Language-Audio Pre-training
Ching-Feng Yeh, Po-Yao Huang, Vasu Sharma +2
We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, c…
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
Lili Yu, Bowen Shi, Ramakanth Pasunuru +24
We present CM3Leon (pronounced "Chameleon"), a retrieval-augmented, token-based, decoder-only multi-modal language model capable of generating and infilling both text and images. C…
Demystifying CLIP Data
Hu Xu, Saining Xie, Xiaoqing Ellen Tan +7
Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative mode…