66 citations · 67 across the 6 of their papers we have counts for
6 papers · 1 filter
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
Nilaksh, Saurav Jha, Artem Zholus +1
World model-based policy evaluation is a practical proxy for testing real-world robot control by rolling out candidate actions in action-conditioned video diffusion models. As thes…
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha, Shiqi Yang, Masato Ishii +7
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However,…
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Saurav Jha, Dong Gong, Lina Yao
Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned. Owing to their powerful generalizability, pre-trained vision-la…
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Yun Li, Zhe Liu, Saurav Jha +2
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world…
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
Saurav Jha, Nikhil Agarwal, Suneeta Agarwal
Given the recent deep learning advancements in face detection and recognition techniques for human faces, this paper answers the question "how well would they work for cartoons'?"…