Leveraging Transformer Decoder for Automotive Radar Object Detection

arXiv:2601.13386 · cs.CV, eess.SP · Submitted 2026-01-19 · Read on arXiv

cs.CV, eess.SP

Submitted: 2026-01-19

Updated: 2026-01-19

Terminology

• Transformer — 43× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Pyramid Token Fusion — 4× in this paper
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Average Precision — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Temporal Correlations — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• D CNNs to Range-Azimuth-Doppler — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• YOLOv4: Optimal Speed and Accuracy of Object Detection

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models