What Is Where: Inferring Containment Relations from Videos
Authors: Wei Liang, Yibiao Zhao, Yixin Zhu, Song-Chun Zhu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method on our dataset with 1326 video clips taken in 9 indoor scenes, including some challenging cases, such as heavy occlusions and diverse changes of containment relations. The experimental results demonstrate good performance on the dataset. |
| Researcher Affiliation | Academia | 1School of Computer Science, Beijing Institute of Technology (BIT), China 2Center for Vision, Cognition, Learning, & Autonomy, University of California, Los Angeles, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | No | The paper states: "We collected a RGB-D video dataset with diverse actions to evaluate the proposed method." It describes the dataset and its properties, but does not provide a specific link, DOI, repository name, or formal citation for public access to this collected dataset. |
| Dataset Splits | No | The paper mentions "800 clips are used to train our model and the remaining clips are for testing." It also refers to "cross-validation during the training phrase" for obtaining weights (λ1 and λ2). However, it does not provide specific numerical details (percentages or sample counts) for a distinct validation split. |
| Hardware Specification | No | The paper mentions that the dataset was "captured by a Kinect sensor" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments or training the models. |
| Software Dependencies | No | The paper does not explicitly list any software dependencies with specific version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x). |
| Experiment Setup | Yes | Energy of containment relations is defined as: φ(Gt, Vt) = λ1 φIN + λ2 φON + φAFF, where λ1 and λ2 are the weights of the energy terms, obtained through cross-validation during the training phrase. ... The window sizes and sliding steps are both multi-scale. |