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 [1].
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]. |