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Found 5 papers, 4 papers with code
CLUSTSEG: Clustering for Universal Segmentation
We present CLUSTSEG, a general, transformer-based framework that tackles different image segmentation tasks (i. e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme.
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
We argue that the weakest link of fusion models depends on their most vulnerable modality, and propose an attack framework that targets advanced camera-LiDAR fusion-based 3D object detection models through camera-only adversarial attacks.
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
We improve adversarial robustness against physical-world attacks using L0-norm-bounded perturbation in training.
Learning Equivariant Segmentation with Instance-Unique Querying
Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in which instance masks are derived by querying the image feature using a set of instance-aware embeddings.
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
Experimental results show that our method can generate stealthy, effective, and robust adversarial patches for different target objects and models and achieves more than 6 meters mean depth estimation error and 93% attack success rate (ASR) in object detection with a patch of 1/9 of the vehicle's rear area.
