4 citations · 13 across the 12 of their papers we have counts for
14 papers
On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
Rosie Zhao, Anshul Shah, Xiaoyu Zhu +5
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision-langua…
MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation
Ansh Shah, K Madhava Krishna
Recovering metric depth from a single image remains a fundamental challenge in computer vision, requiring both scene understanding and accurate scaling. While deep learning has adv…
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics
Aniket Roy, Maiterya Suin, Anshul Shah +3
Diffusion models have advanced generative AI significantly in terms of editing and creating naturalistic images. However, efficiently improving generated image quality is still of…
GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation
Yuxiang Guo, Anshul Shah, Jiang Liu +3
Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information. In recent years, this field has been dominated by lear…
Learning to Prompt Your Domain for Vision-Language Models
Guoyizhe Wei, Feng Wang, Anshul Shah +1
Prompt learning has recently become a very efficient transfer learning paradigm for Contrastive Language Image Pretraining (CLIP) models. Compared with fine-tuning the entire encod…
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
Anshul Shah, Aniket Roy, Ketul Shah +4
Supervised learning of skeleton sequence encoders for action recognition has received significant attention in recent times. However, learning such encoders without labels continue…
STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videos
Anshul Shah, Benjamin Lundell, Harpreet Sawhney +1
We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and per…
Cap2Aug: Caption guided Image to Image data Augmentation
Aniket Roy, Anshul Shah, Ketul Shah +2
Visual recognition in a low-data regime is challenging and often prone to overfitting. To mitigate this issue, several data augmentation strategies have been proposed. However, sta…
Unfolding a blurred image
Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during th…
Max-Margin Contrastive Learning
Anshul Shah, Suvrit Sra, Rama Chellappa +1
Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavio…
Object-Aware Cropping for Self-Supervised Learning
Shlok Mishra, Anshul Shah, Ankan Bansal +5
A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-sup…
Learning Visual Representations for Transfer Learning by Suppressing Texture
Shlok Mishra, Anshul Shah, Ankan Bansal +5
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information. In self-supervised learn…
Pose And Joint-Aware Action Recognition
Anshul Shah, Shlok Mishra, Ankan Bansal +3
Recent progress on action recognition has mainly focused on RGB and optical flow features. In this paper, we approach the problem of joint-based action recognition. Unlike other mo…
Bringing Alive Blurred Moments
Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during th…