2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Scaling Sequence-to-Sequence Generative Neural Rendering
Shikun Liu, Kam Woh Ng, Wonbong Jang +9
We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on the principle that 3D can be re…
Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training
Junlin Han, Shengbang Tong, David Fan +4
Large Language Models (LLMs), despite being trained on text alone, surprisingly develop rich visual priors. These priors allow latent visual capabilities to be unlocked for vision…
Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning
Tianyi Bai, Yuxuan Fan, Jiantao Qiu +7
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual differences, leading to hallucination…
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting
Yuyuan Liu, Yuanhong Chen, Chong Wang +6
Segment Anything Model 2 (SAM2) exhibits strong generalisation for promptable segmentation in video clips; however, its integration with the audio modality remains underexplored. E…