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20232026
most citedInterpreting and Improving Attention From the Perspective of Large Kernel Convolution

1 citations · 1 across the 34 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

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…

cs.AI2025

Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis

Chenghao Li, Chaoning Zhang, Yi Lu +10

With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-sol…

cs.LG2025

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…

cs.CV2025

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…

cs.CL2025

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…