Jiwen Lu, Jie Zhou, Yansong Tang, Sujia Wang, Wenxun Dai, Shiyi Zhang, Xiangwei Shen
We lifted 4 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 |
|---|---|---|
| shiyi-zh0408/logo | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_conv_params | Ran | shiyi-zh0408/logo/CoRe-GOAT/models/i3d.py pointer only (licence: NONE) · get_code("0b19df9669841d40") |
| get_padding_shape | Ran | shiyi-zh0408/logo/CoRe-GOAT/models/i3d.py pointer only (licence: NONE) · get_code("45cc344e60f640a1") |
| simplify_padding | Ran | shiyi-zh0408/logo/CoRe-GOAT/models/i3d.py pointer only (licence: NONE) · get_code("81ec17604dc709cc") |
| temporal_position_encoding | Ran | shiyi-zh0408/logo/CoRe-GOAT/models/group_aware_attention.py pointer only (licence: NONE) · get_code("e67dff688b7495e3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. An overview of the LOGO dataset. LOGO is a multi-person long-form video dataset with frame-wise annotations on both action procedures (as shown in the second line) and formations (as shown in the third line, which reflects relations among actors) based on artistic swimming scenarios. It provides a potential for constructing an action quality assessment approach with the ability of modeling group information among actors. Longer video durations also challenge the ability of the method to aggregate long-term temporal information.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.05029")
get_code_for_paper("2404.05029")
have("2404.05029")
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