arXiv subjects

Zhuhao Zhang

Publications and source records attributed to Zhuhao Zhang.

3 recordsLinked to original sources

arXiv2025

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results

This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.

cs.CV↗

arXiv2025

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs

Parameter Efficient Fine-Tuning (PEFT) methods have emerged as effective and promising approaches for fine-tuning pre-trained language models. Compared with Full parameter Fine-Tuning (FFT), PEFT achieved comparable task performance with a substantial reduction of trainable parameters, which largely saved the training and storage costs. However, using the PEFT method requires considering a vast design space, such as the type of PEFT modules and their insertion layers. Inadequate configurations can lead to sub-optimal results. Conventional solutions such as architectural search techniques, while effective, tend to introduce substantial additional overhead. In this paper, we propose a novel approach, PrunePEFT, which formulates the PEFT strategy search as a pruning problem and introduces a hybrid pruning strategy that capitalizes on the sensitivity of pruning methods to different PEFT modules. This method extends traditional pruning techniques by iteratively removing redundant or conflicting PEFT modules, thereby optimizing the fine-tuned configuration. By efficiently identifying the most relevant modules, our approach significantly reduces the computational burden typically associated with architectural search processes, making it a more scalable and efficient solution for fine-tuning large pre-trained models.

cs.LG↗

arXiv2023

View Adaptive Light Field Deblurring Networks with Depth Perception

The Light Field (LF) deblurring task is a challenging problem as the blur images are caused by different reasons like the camera shake and the object motion. The single image deblurring method is a possible way to solve this problem. However, since it deals with each view independently and cannot effectively utilize and maintain the LF structure, the restoration effect is usually not ideal. Besides, the LF blur is more complex because the degree is affected by the views and depth. Therefore, we carefully designed a novel LF deblurring network based on the LF blur characteristics. On one hand, since the blur degree varies a lot in different views, we design a novel view adaptive spatial convolution to deblur blurred LFs, which calculates the exclusive convolution kernel for each view. On the other hand, because the blur degree also varies with the depth of the object, a depth perception view attention is designed to deblur different depth areas by selectively integrating information from different views. Besides, we introduce an angular position embedding to maintain the LF structure better, which ensures the model correctly restores the view information. Quantitative and qualitative experimental results on synthetic and real images show that the deblurring effect of our method is better than other state-of-the-art methods.

cs.CV↗