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..
Weakly-Supervised Audio-Visual Segmentation
Authors: Shentong Mo, Bhiksha Raj
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on AVSBench demonstrate the effectiveness of our WS-AVS in the weakly-supervised audio-visual segmentation of single-source and multi-source scenarios. |
| Researcher Affiliation | Academia | Shentong Mo1,2 Bhiksha Raj1,2 1CMU, 2MBZUAI |
| Pseudocode | No | The paper describes its method using text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | AVSBench [1] contains 4,932 videos with 10,852 total frames from 23 categories including animals, humans, instruments, etc. Following prior work [1], we use the split of 3,452/740/740 videos for train/val/test in single source segmentation. |
| Dataset Splits | Yes | Following prior work [1], we use the split of 3,452/740/740 videos for train/val/test in single source segmentation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The model is trained with the Adam optimizer with default hyper-parameters β1 = 0.9, β2 = 0.999, and a learning rate of 1e-4. The model is trained for 20 epochs with a batch size of 64. |