10 citations · 19 across the 4 of their papers we have counts for
4 papers
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +3
Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visua…
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +4
Recently, large-scale pre-trained Vision and Language (VL) models have set a new state-of-the-art (SOTA) in zero-shot visual classification enabling open-vocabulary recognition of…
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
Wei Lin, Leonid Karlinsky, Nina Shvetsova +6
Large scale Vision-Language (VL) models have shown tremendous success in aligning representations between visual and text modalities. This enables remarkable progress in zero-shot…
Whale: Efficient Giant Model Training over Heterogeneous GPUs
Xianyan Jia, Le Jiang, Ang Wang +9
The scaling up of deep neural networks has been demonstrated to be effective in improving model quality, but also encompasses several training challenges in terms of training effic…