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..
Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization
Authors: Jun-Tae Lee, Mihir Jain, Hyoungwoo Park, Sungrack Yun
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two publicly available video-datasets (AVE and Activity Net1.2) show that the proposed method effectively fuses audio and visual modalities, and achieves the state-of-the-art results for weakly-supervised action localization. |
| Researcher Affiliation | Industry | Juntae Lee, Mihir Jain, Hyoungwoo Park & Sungrack Yun Qualcomm AI Research . EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include a statement about releasing its source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Extensive experiments are conducted on two video datasets for localizing audio-visual events (AVE1) and actions (Activity Net1.22). 1https://github.com/Yapeng Tian/AVE-ECCV18 2http://activity-net.org/download.html |
| Dataset Splits | Yes | Activity Net1.2 is a temporal action localization dataset with 4,819 train and 2,383 validation videos, which in the literature is used for evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using specific networks like I3D, ResNet152, and VGG-like network for feature extraction, but it does not specify versions for software libraries or dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set dx to 1,024, and the Leaky Relu and hyperbolic tangent functions are respectively used for the activation of modality-specific layers and cross-attention modules. In training, the parameters are initialized with Xavier method (Glorot & Bengio, 2010) and updated by Adam optimizer (Kingma & Ba, 2015) with the learning rate of 10 4 and the batch size of 30. Also, the dropout with a ratio of 0.7 is applied for the final attended audio-visual features. In the loss, the hyper parameters are set as B = 4, α = 0.8, β = 0.8 and γ = 1. |