Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Authors: Feihong Shen, Chao Li, Yifeng Geng, Yongjian Deng, Hao Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the public Retarget Me benchmark and demonstrate through objective experimental results and subjective user studies that our method outperforms previous approaches in terms of preserving semantics and aesthetics, as well as better generalization across diverse aspect ratios. |
| Researcher Affiliation | Collaboration | Feihong Shen1,2,4 feihongshen@seu.edu.cn Chao Li2 lllcho.lc@alibaba-inc.com Yifeng Geng2 cangyu.gyf@alibaba-inc.com Yongjian Deng3 yjdeng@bjut.edu.cn Hao Chen 1,4 haochen303@seu.edu.cn 1Southeast University 2Alibaba Group 3Beijing University of Technology 4Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes will be available at https://github.com/fhshen2022/Prune Repaint. |
| Open Datasets | Yes | We evaluate the proposed method on the public image retargeting datasets, Retarget Me [27], which contains 80 images from various scenes. [27] M. Rubinstein, D. Gutierrez, O. Sorkine-Hornung, and A. Shamir. A comparative study of image retargeting. ACMSIGGRAPH, 2010. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | Our method is implemented using Pytorch on a RTX 3090. |
| Software Dependencies | Yes | For the image-to-image repainting model in AR, we employ a composition of SD1.5 , Control Net-Inpainting and IP-Adapter [36]. |
| Experiment Setup | Yes | The length of the sliding window in Section 3.3.1 is set to l = 25, and the threshold is set to η = 15. |