6 papers
From Generated Human Videos to Physically Plausible Robot Trajectories
James Ni, Zekai Wang, Wei Lin +5
Video generation models are rapidly improving in their ability to synthesize human actions in novel contexts, holding the potential to serve as high-level planners for contextual r…
Learning to Hear by Seeing: It's Time for Vision Language Models to Understand Artistic Emotion from Sight and Sound
Dengming Zhang, Weitao You, Jingxiong Li +6
Emotion understanding is critical for making Large Language Models (LLMs) more general, reliable, and aligned with humans. Art conveys emotion through the joint design of visual an…
MapReduce LoRA: Advancing the Pareto Front in Multi-Preference Optimization for Generative Models
Chieh-Yun Chen, Zhonghao Wang, Qi Chen +10
Reinforcement learning from human feedback (RLHF) with reward models has advanced alignment of generative models to human aesthetic and perceptual preferences. However, jointly opt…
SpotEdit: Evaluating Visually-Guided Image Editing Methods
Sara Ghazanfari, Wei-An Lin, Haitong Tian +1
Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content genera…
pLSTM: parallelizable Linear Source Transition Mark networks
Korbinian Pöppel, Richard Freinschlag, Thomas Schmied +2
Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to s…
Plug-and-Play Diffusion Distillation
Yi-Ting Hsiao, Siavash Khodadadeh, Kevin Duarte +4
Diffusion models have shown tremendous results in image generation. However, due to the iterative nature of the diffusion process and its reliance on classifier-free guidance, infe…