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..
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Authors: Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho, Junmo Kim, Joon Son Chung
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive ablation studies and qualitative results verify the effectiveness of our method. Equi AV outperforms previous works across various audiovisual benchmarks. |
| Researcher Affiliation | Academia | 1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Correspondence to: Jongsuk Kim <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Equi AV |
| Open Source Code | Yes | The code is available on https://github.com/Jong Suk1/Equi AV |
| Open Datasets | Yes | We utilize two prominent audio-visual datasets for our experiments: Audio Set (Gemmeke et al., 2017) and VGGSound (Chen et al., 2020a). |
| Dataset Splits | No | The paper does not explicitly provide validation dataset splits with percentages or counts for reproduction. While it mentions "evaluation" clips for Audio Set and train/test splits for VGGSound, a distinct "validation" split is not specified. |
| Hardware Specification | Yes | GPUs 8 A6000 (Pre-training), 8 A5000 (Fine-tuning) |
| Software Dependencies | No | The paper mentions software components and techniques like "Adam W Optimizer", "half-cycle cosine annealing (Loshchilov & Hutter, 2017)", "Vision Transformer", "MAE", and "Spec Augment", but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | The hyperparameter settings used in this paper are listed in Table D. (e.g., Optimizer Adam W Optimizer momentum β1=0.9, β2=0.95 Weight decay 1e-5 Learning rate scheduler half-cycle cosine annealing (Loshchilov & Hutter, 2017) Initial learning rate 1e-6 Peak learning rate 1e-4 Warm-up epochs 2 Epochs 20 Batch size 256) |