We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| yliu-cs/cvla | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| format_dir | Ran | yliu-cs/cvla/tools/gather_result.py pointer only (licence: NONE) · get_code("b7ab36676e38309c") |
| format_result | Ran | yliu-cs/cvla/tools/gather_result.py pointer only (licence: NONE) · get_code("f0e0f2df4de793ac") |
| gather_result | Not yet run | yliu-cs/cvla/tools/gather_result.py pointer only (licence: NONE) · get_code("9ab5f4bea2e67ede") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The growing importance of multi-modal humor detection within affective computing correlates with the expanding influence of short-form video sharing on social media platforms. In this paper, we propose a novel two-branch hierarchical model for short-form video humor detection (SVHD), named Comment-aided Video-Language Alignment (CVLA) via data-augmented multi-modal contrastive pre-training. Notably, our CVLA not only operates on raw signals across various modal channels but also yields an appropriate multi-modal representation by aligning the video and language components within a consistent semantic space. The experimental results on two humor detection datasets, including DY11k and UR-FUNNY, demonstrate that CVLA dramatically outperforms state-of-the-art and several competitive baseline approaches. Our dataset, code and model release at https://github.com/yliu-cs/CVLA.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.09055")
get_code_for_paper("2402.09055")
have("2402.09055")
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