We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| timbroed/cafuser | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| logit | Ran | timbroed/cafuser/cafuser/modeling/feature_adapter/mlp_learnable_ratio.py code served (permissive licence) · get_code("efc14f938a363004") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Leveraging multiple sensors is crucial for robust semantic perception in autonomous driving, as each sensor type has complementary strengths and weaknesses. However, existing sensor fusion methods often treat sensors uniformly across all conditions, leading to suboptimal performance. By contrast, we propose a novel, condition-aware multimodal fusion approach for robust semantic perception of driving scenes. Our method, CAFuser, uses an RGB camera input to classify environmental conditions and generate a Condition Token that guides the fusion of multiple sensor modalities. We further newly introduce modality-specific feature adapters to align diverse sensor inputs into a shared latent space, enabling efficient integration with a single and shared pre-trained backbone. By dynamically adapting sensor fusion based on the actual condition, our model significantly improves robustness and accuracy, especially in adverse-condition scenarios. CAFuser ranks first on the public MUSES benchmarks, achieving 59.7 PQ for multimodal panoptic and 78.2 mIoU for semantic segmentation, and also sets the new state of the art on DeLiVER. The source code is publicly available at: https://github.com/timbroed/CAFuser.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.10791")
get_code_for_paper("2410.10791")
have("2410.10791")
Connect an agent — have() is free.