6 papers
LLaVA-FA: Learning Fourier Approximation for Compressing Large Multimodal Models
Pengcheng Zheng, Chaoning Zhang, Jiarong Mo +8
Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical de…
GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
Qigan Sun, Chaoning Zhang, Jianwei Zhang +8
In recent years, Multimodal Large Language Models (MLLMs) have made significant progress in visual question answering tasks. However, directly applying existing fine-tuning methods…
Fast SAM2 with Text-Driven Token Pruning
Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari +6
Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by…
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
Panqi Chen, Yifan Sun, Lei Cheng +6
Modeling and reconstructing multidimensional physical dynamics from sparse and off-grid observations presents a fundamental challenge in scientific research. Recently, diffusion-ba…
Exploring Kernel Transformations for Implicit Neural Representations
Sheng Zheng, Chaoning Zhang, Dongshen Han +4
Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant atte…
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution
Chenghao Li, Chaoning Zhang, Yi Lu +7
Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors. Ho…