Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Cross-modal Active Complementary Learning with Self-refining Correspondence
Authors: Yang Qin, Yuan Sun, Dezhong Peng, Joey Tianyi Zhou, Xi Peng, Peng Hu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out extensive experiments on three image-text benchmarks, i.e., Flickr30K, MS-COCO, and CC152K, to verify the superior robustness of our CRCL against synthetic and real-world noisy correspondences. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science, Sichuan University, Chengdu, China. 2 Centre for Frontier AI Research (CFAR) and Institute of High Performance Computing (IHPC), A*STAR, Singapore. 3 Chengdu Ruibei Yingte Information Technology Co., Ltd, Chengdu, China. 4 Sichuan Zhiqian Technology Co., Ltd, Chengdu, China. |
| Pseudocode | Yes | Algorithm 1: The pseudo-code of CRCL |
| Open Source Code | Yes | Code is available at https://github.com/Qin Yang79/CRCL. |
| Open Datasets | Yes | For an extensive evaluation, we use three benchmark datasets (i.e., Flickr30K [34], MSCOCO [35] and CC152K [12]) in our experiments. |
| Dataset Splits | Yes | Following [36], 30,000 images are employed for training, 1,000 images for validation, and 1,000 images for testing in our experiments. MS-COCO is a large-scale image-text dataset, which has 123,287 images, and 5 captions are given to describe each image. We follow the split of [36, 8] to carry out our experiments, i.e., 5000 validation images, 5000 test images, and the rest for training. CC152K contains 150,000 image-text pairs for training, 1,000 pairs for validation, and 1,000 pairs for testing. |
| Hardware Specification | No | No: The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running experiments. |
| Software Dependencies | No | No: The paper mentions using 'BUTD features' and 'Bi-GRU' as textual backbone, but does not provide specific version numbers for any software libraries, frameworks, or dependencies. |
| Experiment Setup | Yes | Specifically, the shared hyper-parameters are set as the same as the original works [4, 9], e.g., the batch size is 128, the word embedding size is 300, and the joint embedding dimensionality is 1,024. More specific hyper-parameters and implementation details are given in our supplementary material due to the space limitation. |