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IEEE/ICACT20250226 Slide.10        [Big Slide]	Oral Presentation
	Deformable cross attention dynamically aligns and selectively focus on spatial regions. Instead of attending to all positions in the feature maps like standard cross-attention, deformable cross-attention uses learnable offsets to identify only the most relevant regions. This reduces computational complexity and mitigates spatial misalignment. With learnable positional encodings are added, radar BEV feature is transformed into queries and reference points, and Camera BEV features are used as keys and values in this cross-attention mechanism. And the same process is applied for the camera BEV feature as well.

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