Reasoning with Heterogeneous Graph Alignment for Video Question Answering
Authors: Pin Jiang, Yahong Han11109-11116
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on three benchmark datasets and conduct extensive ablation study to the effectiveness of the network architecture. Experiments show the network to be superior in quality. |
| Researcher Affiliation | Academia | Pin Jiang, Yahong Han College of Intelligence and Computing Tianjin University, Tianjin, China {jpin, yahong}@tju.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code. |
| Open Datasets | Yes | TGIF-QA is a widely used large-scale benchmark dataset for Video QA (Jang et al. 2017)... MSVD-QA and MSRVTT-QA are two datasets generated from video descriptions through an automatic method (Xu et al. 2017). |
| Dataset Splits | Yes | These datasets have provided a standard partition of the training, validation and testing sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using pre-trained models like ResNet-152, C3D, VGG, and GloVe word embeddings, but it does not specify software components with version numbers (e.g., programming language versions, deep learning framework versions, or library versions). |
| Experiment Setup | Yes | In terms of training details, we set the number of the hidden units d to 512. Batch size is set to 64. We use Adam as an optimizer with initial learning rate 10 4. The dropout rate is set to 0.3. For better performance, we use some general training strategies, including early stop, learning rate warming up, and learning rate cosine annealing. |