21 citations · 57 across the 10 of their papers we have counts for
22 papers · 1 filter
Comparison Visual Instruction Tuning
Wei Lin, Muhammad Jehanzeb Mirza, Sivan Doveh +4
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation. It is e…
Towards Multimodal In-Context Learning for Vision & Language Models
Sivan Doveh, Shaked Perek, M. Jehanzeb Mirza +5
State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality primarily via projecting the vision tokens from the encoder to language-like tokens, whic…
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +5
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language M…
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…
On the Transferability of Visual Features in Generalized Zero-Shot Learning
Paola Cascante-Bonilla, Leonid Karlinsky, James Seale Smith +2
Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features…