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..
Video Decomposition Prior: Editing Videos Layer by Layer
Authors: Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on standard video datasets like DAVIS, REVIDE, & SDSD and show qualitative results on a diverse array of internet videos. |
| Researcher Affiliation | Academia | Gaurav Shrivastava University of Maryland, College Park EMAIL Ser-Nam Lim University of Central Florida EMAIL Abhinav Shrivastava University of Maryland, College Park EMAIL |
| Pseudocode | No | The paper describes network architectures and optimization steps in text and tables but does not provide a formal pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our approach on standard video datasets like DAVIS, REVIDE, & SDSD and show qualitative results on a diverse array of internet videos. |
| Dataset Splits | No | The paper describes an inference-time optimization framework that optimizes directly on the test sequence itself, rather than training on a dataset with traditional train/validation splits. |
| Hardware Specification | Yes | To optimize our model, we use a single Nvidia A6000 GPU with 48G memory to process a single video at a time of resolution 856x480. |
| Software Dependencies | No | The paper states: 'We utilize Pytorch for our implementation.' However, it does not provide a specific version number for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | We optimize the module (f RGB( ) and fα( )) weights using the entire test sequence with the Adam optimizer at a learning rate in the range of [0.00002, 0.002]... We use 100 epochs for a 60frame sequence in VOS and 60 epochs for dehazing and relighting... For getting a good performance on the UVOS task we utilize the following weights for the different losses; λrec = 1, λFsim = 0.001, λlayer = 1, λwarp = 0.01 and λMask = 0.01. |