Kurt Keutzer, Sicheng Zhao, Pengfei Xu, Ma, Hua Chai, Jufeng Yang, Yang Gu, Tengfei Xing, Runbo Hu
We lifted 7 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| maysonma/VAANet | — | 3 of 6 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| Bottleneck | Ran | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("d476b566a0250682") |
| ResNet | Ran | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("c2b6d13ee2ae9766") |
| get_fine_tuning_parameters | Ran | this paper's copy was not recorded; identical code first harvested from tianhai123/3D-ResNets pointer only · get_code("93801ecf3026ab06") |
| resnet101 | Ran | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("a6d391f0f1cb1769") |
| VAANet | Not yet run | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("063e586c19fb6c79") |
| VisualStream | Not yet run | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("bafadcd84c9c9aca") |
| pretrained_resnet101 | Not yet run | maysonma/VAANet/models/vaanet.py pointer only (licence: NONE) · get_code("3f3c849ccdbc385e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.00832")
get_code_for_paper("2003.00832")
have("2003.00832")
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