Decomposing Motion and Content for Natural Video Sequence Prediction
Authors: Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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. |