6 papers
From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images
Yiming Chen, Junlin Han, Tianyi Bai +3
While Multimodal Large Language Models (MLLMs) are adept at answering what is in an image-identifying objects and describing scenes-they often lack the ability to understand how an…
Chimera: Compositional Image Generation using Part-based Concepting
Shivam Singh, Yiming Chen, Agneet Chatterjee +4
Personalized image generative models are highly proficient at synthesizing images from text or a single image, yet they lack explicit control for composing objects from specific pa…
Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection
Jingyao Wang, Yiming Chen, Lingyu Si +1
Scene understanding is one of the core tasks in computer vision, aiming to extract semantic information from images to identify objects, scene categories, and their interrelationsh…
Clink! Chop! Thud! -- Learning Object Sounds from Real-World Interactions
Mengyu Yang, Yiming Chen, Haozheng Pei +3
Can a model distinguish between the sound of a spoon hitting a hardwood floor versus a carpeted one? Everyday object interactions produce sounds unique to the objects involved. We…
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks
Hongchao Jiang, Yiming Chen, Yushi Cao +2
Large Language Models (LLMs) have significantly advanced the state-of-the-art in various coding tasks. Beyond directly answering user queries, LLMs can also serve as judges, assess…
InterMamba: Efficient Human-Human Interaction Generation with Adaptive Spatio-Temporal Mamba
Zizhao Wu, Yingying Sun, Yiming Chen +3
Human-human interaction generation has garnered significant attention in motion synthesis due to its vital role in understanding humans as social beings. However, existing methods…