3 citations · 7 across the 5 of their papers we have counts for
5 papers
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
Harsh Rangwani, Pradipto Mondal, Mayank Mishra +2
Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT, we divide the input image into patch tokens and process them through a s…
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri +4
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain. However,…
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
Sravanti Addepalli, Ashish Ramayee Asokan, Lakshay Sharma +1
Vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs, resulting in remarkable generalization across several data distributions. However, in s…
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri +3
Conventional Domain Adaptation (DA) methods aim to learn domain-invariant feature representations to improve the target adaptation performance. However, we motivate that domain-spe…
Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors
Ashish Ramayee Asokan, Nidarshan Kumar, Anirudh Venkata Ragam +1
Multimodal Emotion Recognition refers to the classification of input video sequences into emotion labels based on multiple input modalities (usually video, audio and text). In rece…