Yong Ro, Taeheon Kim, Hak Gu, Youngjoon Yu, Sebin Shin
We lifted 4 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| ssbin0914/Causal-Mode-Multiplexer | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_imdb | Not yet run | ssbin0914/Causal-Mode-Multiplexer/lib/datasets/factory.py code served (permissive licence) · get_code("3be9228820b46ef3") |
| unique_boxes | Not yet run | ssbin0914/Causal-Mode-Multiplexer/lib/datasets/ds_utils.py code served (permissive licence) · get_code("8a015c012f507631") |
| xywh_to_xyxy | Not yet run | ssbin0914/Causal-Mode-Multiplexer/lib/datasets/ds_utils.py code served (permissive licence) · get_code("d3e019b11b709edb") |
| xyxy_to_xywh | Not yet run | ssbin0914/Causal-Mode-Multiplexer/lib/datasets/ds_utils.py code served (permissive licence) · get_code("a832e5017f504280") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically, datasets in multispectral pedestrian detection mainly distribute between ROTO 1 (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data. To address this problem, we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover, we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST, CVC-14, FLIR) and the new ROTX-MP. Our code and dataset are available at: https://github.com/ssbin0914/Causal-Mode-Multiplexer.git. * Equally contributed. † Corresponding author. 1 R⋆T⋆ refers to the visibility (O/X) in each modality. Generally, ROTO refers to daytime images, and RXTO refers to nighttime images. ROTX refers to daytime images in obscured situations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.01300")
get_code_for_paper("2403.01300")
have("2403.01300")
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