Compact Generalized Non-local Network
Authors: Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results illustrate the clear-cut improvements and practical applicability of the generalized non-local module on both fine-grained object recognition and video classification. |
| Researcher Affiliation | Collaboration | Kaiyu Yue , Ming Sun Yuchen Yuan Feng Zhou Errui Ding Fuxin Xu Baidu VIS Baidu Research Central South University {yuekaiyu, sunming05, yuanyuchen02, zhoufeng09, dingerrui}@baidu.com fxxu@csu.edu.cn |
| Pseudocode | No | The paper provides workflow diagrams and formulas in figures (Fig 2, 3, 4), but these are not structured pseudocode or algorithm blocks. No section is explicitly labeled 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | Code is available at: https://github.com/Kaiyu Yue/cgnl-network.pytorch. |
| Open Datasets | Yes | We evaluate the CGNL network on multiple tasks, including fine-grained classification and action recognition. For fine-grained classification, we experiment on the Birds-200-2011 (CUB) dataset [25]... For action recognition, we experiment on two challenging datasets, Mini-Kinetics [30] and UCF101 [22]. ... We use the models pretrained on Image Net [20] to initialize the weights. |
| Dataset Splits | Yes | The Mini-Kinetics dataset... we use 78265 videos for training and 4986 videos for validation. ... Image Net [20] dataset, which has 1.2 million training images and 50000 images for validation in 1000 object categories. |
| Hardware Specification | No | The paper does not mention any specific hardware components (e.g., GPU models, CPU types, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions various neural network architectures and components like 'Res Net', 'Mask R-CNN', 'Batch Normalization', and 'dropout', but it does not specify any software names with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.x'). |
| Experiment Setup | Yes | We use a weight decay of 0.0001 and momentum of 0.9 in default. The strategy of gradual warmup is used in the first ten epochs. The dropout [23] with ratio 0.5 is inserted between average pooling layer and last fully-connected layer. To keep same with [27], we use zero to initialize the weight and bias of the Batch Norm (BN) layer in both CGNL and NL blocks [6]. To train the networks on CUB dataset, we follow the same training strategy above but the final crop size of 448. |