7 papers
BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Rocktim Jyoti Das, Harsh Singh, Diana Turmakhan +5
Scaling data and models has played a pivotal role in the remarkable progress of computer vision and language. Inspired by these domains, recent efforts in robotics have similarly f…
Towards Reliable Identification of Diffusion-based Image Manipulations
Alex Costanzino, Woody Bayliss, Juil Sock +5
Changing facial expressions, gestures, or background details may dramatically alter the meaning conveyed by an image. Notably, recent advances in diffusion models greatly improve t…
PSyDUCK: Training-Free Steganography for Latent Diffusion
Aqib Mahfuz, Georgia Channing, Mark van der Wilk +3
Recent advances in generative AI have opened promising avenues for steganography, which can securely protect sensitive information for individuals operating in hostile environments…
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
Runtao Liu, I Chieh Chen, Jindong Gu +6
Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures a…
Video Motion Transfer with Diffusion Transformers
Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov +2
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process t…
MALT: Improving Reasoning with Multi-Agent LLM Training
Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das +6
Large Language Models (LLMs) often produce answers with a single chain-of-thought, which restricts their ability to explore reasoning paths or self-correct flawed outputs in comple…