1 citations · 1 across the 7 of their papers we have counts for
8 papers
ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
Shubhang Bhatnagar, Ishan Bhatnagar, Viraj Shah +1
Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images. Increasingly, these images come from a diffusion model rather tha…
Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
Shubhang Bhatnagar, Dheeraj Baiju, Narendra Ahuja
Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it toget…
Finding Distributed Object-Centric Properties in Self-Supervised Transformers
Samyak Rawlekar, Amitabh Swain, Yujun Cai +3
Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, typically observed in [CLS] token attention maps of the final layer. However, the…
LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models
Shubhang Bhatnagar, Andy Xu, Kar-Han Tan +1
Large Language Models (LLMs) with multimodal capabilities have revolutionized vision-language tasks, but their deployment often requires huge memory and computational resources. Po…
Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
Samyak Rawlekar, Shubhang Bhatnagar, Narendra Ahuja
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prom…
Potential Field Based Deep Metric Learning
Shubhang Bhatnagar, Narendra Ahuja
Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interact…
Improving Multi-label Recognition using Class Co-Occurrence Probabilities
Samyak Rawlekar, Shubhang Bhatnagar, Vishnuvardhan Pogunulu Srinivasulu +1
Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged inf…
Piecewise-Linear Manifolds for Deep Metric Learning
Shubhang Bhatnagar, Narendra Ahuja
Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the…