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..
Segment Anything without Supervision
Authors: XuDong Wang, Jingfeng Yang, Trevor Darrell
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated across seven popular datasets, Un SAM achieves competitive results with the supervised counterpart SAM, and surpasses the previous state-of-the-art in unsupervised segmentation by 11% in terms of AR. |
| Researcher Affiliation | Academia | Xu Dong Wang Jingfeng Yang Trevor Darrell UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Divide and Conquer |
| Open Source Code | Yes | code: https://github.com/frank-xwang/Un SAM |
| Open Datasets | Yes | MSCOCO [24], LVIS [15], SA-1B [21], ADE [48], Entity [29], Part Image Net [16] and PACO [30]. |
| Dataset Splits | No | The paper mentions training on a percentage of SA-1B and evaluating on COCO Val2017 and SA-1B test set, but does not explicitly state validation dataset splits for its training process, apart from using standard evaluation sets which often serve as de-facto validation sets for benchmarking. |
| Hardware Specification | Yes | All model training in this paper was conducted using either 4 A100 GPUs or 8 RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions software components and models like DINO, Mask2Former, and Semantic-SAM, but does not provide specific version numbers for them or other dependencies. |
| Experiment Setup | Yes | In the divide stage, we set the confidence threshold ฯ=0.3; in the conquer stage, we choose threshold ฮธmerge = [0.6, 0.5, 0.4, 0.3, 0.2, 0.1]. ... The default learning rate is 5e-5 with a batch size of 16 and a weight decay of 5e-2. We train the model for 8 epochs. ... The default learning rate is 1e-4 with a batch size of 8. The learning rate decreases by a factor of 10 at 90% and 95% of the training iterations. We train the model for 4 epochs. |