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..
Decomposing Motion and Content for Natural Video Sequence Prediction
Authors: Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed network architecture on human activity videos using KTH, Weizmann action, and UCF-101 datasets. We show state-of-the-art performance in comparison to recent approaches. To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos. |
| Researcher Affiliation | Collaboration | Ruben Villegas1 Jimei Yang2 Seunghoon Hong3, Xunyu Lin4,* Honglak Lee1,5 1University of Michigan, Ann Arbor, USA 2Adobe Research, San Jose, CA 95110 3POSTECH, Pohang, Korea 4Beihang University, Beijing, China 5Google Brain, Mountain View, CA 94043 |
| Pseudocode | No | The paper describes the model architecture and algorithm steps in detail with text and diagrams, but does not provide formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper mentions a project website ('https://sites.google. com/a/umich.edu/rubenevillegas/iclr2017') for qualitative comparisons and videos, but does not explicitly state that source code for the methodology is available there or elsewhere. |
| Open Datasets | Yes | We evaluate the proposed network architecture on human activity videos using KTH (Schuldt et al., 2004), Weizmann action (Gorelick et al., 2007), and UCF-101 (Soomro et al., 2012) datasets. ... all networks were trained on Sports-1M (Karpathy et al., 2014) dataset and tested on UCF-101 unless otherwise stated. |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., 'person 1-16 for training and 17-25 for testing' for KTH, or 'trained on Sports-1M... and tested on UCF-101'), but does not explicitly mention a distinct validation set or how it was used. |
| Hardware Specification | Yes | We also thank NVIDIA for donating K40c and TITAN X GPUs. |
| Software Dependencies | No | The paper mentions using CNNs and LSTMs but does not specify any software libraries, frameworks, or their version numbers (e.g., TensorFlow, PyTorch, Python version). |
| Experiment Setup | Yes | For all our experiments, we use α = 1, λ = 1, and p = 2 in the loss functions. ... We set β = 0.02 for training. ... We set β = 0.001 for training. |