2 citations · 3 across the 7 of their papers we have counts for
7 papers
RefDiT: Local Attribute Guidance in Reference-Based Image Generation
Rameshwar Mishra, Srikrishna Karanam, A V Subramanyam
Personalization models generate new images guided by a few subject references, while style transfer methods aim to produce images aligned with a global style derived from a referen…
Do Prompts Guarantee Safety? Mitigating Toxicity from LLM Generations through Subspace Intervention
Himanshu Singh, Ziwei Xu, A. V. Subramanyam +1
Large Language Models (LLMs) are powerful text generators, yet they can produce toxic or harmful content even when given seemingly harmless prompts. This presents a serious safety…
Nearest Neighbor Projection Removal Adversarial Training
Himanshu Singh, A. V. Subramanyam, Shivank Rajput +1
Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adversarial training enhances robus…
Novel View Synthesis using DDIM Inversion
Sehajdeep Singh, A V Subramanyam, Aditya Gupta +1
Single-image novel view synthesis requires viewpoint-dependent structural changes while retaining source-scene information and inferring unobserved content. We formulate this task…
Conditional Consistency Guided Image Translation and Enhancement
Amil Bhagat, Milind Jain, A. V. Subramanyam
Consistency models have emerged as a promising alternative to diffusion models, offering high-quality generative capabilities through single-step sample generation. However, their…
Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs
Rameshwar Mishra, A V Subramanyam
Advancements in generative models have sparked significant interest in generating images while adhering to specific structural guidelines. Scene graph to image generation is one su…
Language Guided Adversarial Purification
Himanshu Singh, A V Subramanyam
Adversarial purification using generative models demonstrates strong adversarial defense performance. These methods are classifier and attack-agnostic, making them versatile but of…