128 citations · 380 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
CiT: Curation in Training for Effective Vision-Language Data
Hu Xu, Saining Xie, Po-Yao Huang +5
Large vision-language models are generally applicable to many downstream tasks, but come at an exorbitant training cost that only large institutions can afford. This paper trades g…