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..
Self-Supervised Learning of Compressed Video Representations
Authors: Youngjae Yu, Sangho Lee, Gunhee Kim, Yale Song
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our approach achieves competitive performance on compressed video recognition both in supervised and self-supervised regimes. ... 3 EXPERIMENTS ... Table 1 summarizes the results. ... Table 3 summarizes the results. |
| Researcher Affiliation | Collaboration | Youngjae Yu , Sangho Lee , Gunhee Kim Seoul National University EMAIL, EMAIL Yale Song Microsoft Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Self-supervision label for Pyramidal Motion Statistics Prediction |
| Open Source Code | No | No explicit statement about the authors providing open-source code for their methodology or a link to a code repository was found. |
| Open Datasets | Yes | We pretrain our model on Kinetics-400 (Kay et al., 2017). For evaluation, we finetune the pretrained model for action recognition using UCF-101 (Soomro et al., 2012) and HMDB-51 (Kuehne et al., 2011). |
| Dataset Splits | Yes | We use the standard training and evaluation protocols for both UCF-101 (Soomro et al., 2012) and HMDB-51 (Kuehne et al., 2011). |
| Hardware Specification | Yes | We use 4 NVIDIA Tesla V100 GPUs and use a batch size of 100. ... Table 2 shows per-frame runtime speed (ms) and GFLOPs measured on an NVIDIA Tesla P100 GPU with Intel E5-2698 v4 CPUs |
| Software Dependencies | No | No specific software versions (e.g., Python, PyTorch, CUDA versions) were mentioned, only general software components like '3D Res Net' and 'SGD'. |
| Experiment Setup | Yes | We pretrain our model end-to-end from scratch for 20 epochs, including the initial warm-up period of 5 epochs. For downstream scenarios, we finetune our model for 500 epochs for UCF-101 and for 300 epochs for HMDB-51, including the warm-up period of 30 epochs. For both the pretraining and finetuning stages, we use SGD with momentum 0.9, weight decay 10 4, and half-period cosine learning rate schedule. We use 4 NVIDIA Tesla V100 GPUs and use a batch size of 100. |