Author

C. Fookes

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Jul 2026

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation

TOLiD is proposed, a self-supervised pretraining method for LiDAR representation learning that addresses the gap between dense ViT token representations and sparse 3D encoders by coupling a LiDAR backbone with a student Vision Transformer initialized from a frozen VFM teacher and applying supervision over compatible pa...

Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy et al. · arXiv.org · 1 citation

Preprint Aug 2026

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

This paper introduces the first attack that directly optimizes an encoder-attention objective under an imperceptible, bounded, bounded perturbation, and argues that encoder attention concentrates the model's spatial reasoning, so corrupting it propagates through the detection pipeline more disruptively than perturbing...

Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando et al. · 0 citations