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..
Spider: A Unified Framework for Context-dependent Concept Segmentation
Authors: Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, Huchuan Lu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Spider significantly outperforms the state-of-the-art specialized models in 8 different context-dependent segmentation tasks, including 4 natural scenes (salient, camouflaged, and transparent objects and shadow) and 4 medical lesions (COVID-19, polyp, breast, and skin lesion with color colonoscopy, CT, ultrasound, and dermoscopy modalities). |
| Researcher Affiliation | Collaboration | 1Dalian University of Technology, China 2X3000 Inspection Co., Ltd, China 3Yale University, America. |
| Pseudocode | Yes | Algorithm 1 Training and Inference |
| Open Source Code | No | The source code will be publicly available at Spider-Uni CDSeg. |
| Open Datasets | Yes | The dataset information is shown in Table 1. We follow the training settings of recent state-of-the-art methods in these tasks and merge all training samples together as our training set. Table 1 lists datasets such as DUTS (Wang et al., 2017), COD10K (Fan et al., 2020a), and others, indicating widely used public datasets with citations. |
| Dataset Splits | No | Table 1 lists '#Train' and '#Test' datasets but does not provide specific information about a distinct validation split for the datasets used in the experiments. |
| Hardware Specification | Yes | All the experiments are implemented on the 8 Tesla A100 GPU for training 50 epochs. |
| Software Dependencies | No | The paper mentions using specific optimizers like Adam and backbones like ViT, Swin, and ConvNeXt, but does not provide specific version numbers for these software components or other libraries/frameworks (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The input resolutions of images are resized to 384 384. For each task, the mini-batch sizes of the input and prompt are set to 4 and 12, respectively. We adopt some basic image augmentation techniques to avoid overfitting, including random flipping, rotating and border clipping. The Adam (Kingma & Ba, 2015) optimizer scheduled by step with initial learning rate of 0.0001, decay size of 30 and decay rate of 0.9 is introduced to update model parameters. |