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..
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Authors: Zesen Cheng, Peng Jin, Hao Li, Kehan Li, Siheng Li, Xiangyang Ji, Chang Liu, Jie Chen
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With our Wi Co, several prominent top-down and bottom-up combinations achieve remarkable improvements on three common datasets with reasonable extra costs, which justifies effectiveness and generality of our method. 4 Experiments Our model is evaluated on three standard referring image segmentation datasets: Ref COCO [Yu et al., 2016], Ref COCO+ [Yu et al., 2016] and Ref COCOg [Mao et al., 2016]. |
| Researcher Affiliation | Academia | 1 School of Electronic and Computer Engineering, Peking University, Shenzhen, China 2 AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China 3 Peng Cheng Laboratory, Shenzhen, China 4 Tsinghua University, Beijing, China EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | None found. |
| Open Source Code | No | None found. |
| Open Datasets | Yes | Our model is evaluated on three standard referring image segmentation datasets: Ref COCO [Yu et al., 2016], Ref COCO+ [Yu et al., 2016] and Ref COCOg [Mao et al., 2016]. |
| Dataset Splits | No | Our model is evaluated on three standard referring image segmentation datasets: Ref COCO [Yu et al., 2016], Ref COCO+ [Yu et al., 2016] and Ref COCOg [Mao et al., 2016]. The data preprocessing operations are in line with the original implementation of those selected methods. |
| Hardware Specification | Yes | We train our models for 5,000 iterations on an NVIDIA V100 with a batch size of 24. |
| Software Dependencies | No | Adam W [Loshchilov and Hutter, 2017] is adopted as our optimizer, and the learning rate and weight decay are set to 1e-5 and 5e-2. |
| Experiment Setup | Yes | Adam W [Loshchilov and Hutter, 2017] is adopted as our optimizer, and the learning rate and weight decay are set to 1e-5 and 5e-2. We train our models for 5,000 iterations on an NVIDIA V100 with a batch size of 24. To binarize the probability map and get segmentation results, the threshold τ is set to 0.35 to calibrate previous works [Ding et al., 2021]. |