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..
TEINet: Towards an Efficient Architecture for Video Recognition
Authors: Zhaoyang Liu, Donghao Luo, Yabiao Wang, Limin Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Tong Lu11669-11676
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to verify the effectiveness of TEINet on several benchmarks (e.g., Something-Something V1&V2, Kinetics, UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high ef๏ฌciency. |
| Researcher Affiliation | Collaboration | 1State Key Lab for Novel Software Technology, Nanjing University, China 2Youtu Lab, Tencent |
| Pseudocode | No | The paper describes its method in text and figures, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Something-Something V1&V2. (Goyal et al. 2017) is a large collection of video clips containing daily actions interacting with common objects. ... Kinetics-400. (Kay et al. 2017) is a large-scale dataset in action recognition... UCF101 (Soomro, Zamir, and Shah 2012) and HMDB51 (Kuehne et al. 2011). |
| Dataset Splits | No | The paper does not provide explicit details on how the training, validation, and test splits were created for Something-Something or Kinetics, only mentioning 'three splits' for UCF101/HMDB51 without further definition. |
| Hardware Specification | Yes | For all of our experiments, we utilize SGD with momentum 0.9 and weight decay of 1e-4 to train our TEINet on Tesla M40 GPUs using a mini batch size of 64. ... by using a single NVIDIA Tesla P100 GPU to measure the latency and throughput. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | On the Kinetics dataset, we train our models for 100 epochs in total, starting with a learning rate of 0.01 and reducing to its 1/10 at 50, 75, 90 epochs. For all of our experiments, we utilize SGD with momentum 0.9 and weight decay of 1e-4 to train our TEINet on Tesla M40 GPUs using a mini batch size of 64. |