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Found 3 papers, 1 papers with code
DiffPrompter: Differentiable Implicit Visual Prompts for Semantic-Segmentation in Adverse Conditions
Furthermore, we investigate the advantages of jointly training visual and latent prompts, demonstrating that this combined approach significantly enhances performance in out-of-distribution scenarios.
GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions
We present a Gated Differentiable Image Processing (GDIP) block, a domain-agnostic network architecture, which can be plugged into existing object detection networks (e. g., Yolo) and trained end-to-end with adverse condition images such as those captured under fog and low lighting.
Contextual road lane and symbol generation for autonomous driving
In this paper we present a novel approach for lane detection and segmentation using generative models.
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