4 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters
Kevin Y. Li, Sachin Goyal, Joao D. Semedo +1
Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks, driven by incorporating image representations into the…
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
Pratyush Maini, Sachin Goyal, Zachary C. Lipton +2
Large web-sourced multimodal datasets have powered a slew of new methods for learning general-purpose visual representations, advancing the state of the art in computer vision and…
Finetune like you pretrain: Improved finetuning of zero-shot vision models
Sachin Goyal, Ananya Kumar, Sankalp Garg +2
Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works like WiseFT (Wortsman et al., 2021) and LP-FT (Kuma…
PAL : Pretext-based Active Learning
Shubhang Bhatnagar, Sachin Goyal, Darshan Tank +1
The goal of pool-based active learning is to judiciously select a fixed-sized subset of unlabeled samples from a pool to query an oracle for their labels, in order to maximize the…