1 code implementation • 3 May 2023 • James Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang
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.
no code implementations • 28 Apr 2023 • Zhiyuan Cheng, Hongjun Choi, James Liang, Shiwei Feng, Guanhong Tao, Dongfang Liu, Michael Zuzak, Xiangyu Zhang
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.
1 code implementation • 31 Jan 2023 • Zhiyuan Cheng, James Liang, Guanhong Tao, Dongfang Liu, Xiangyu Zhang
We improve adversarial robustness against physical-world attacks using L0-norm-bounded perturbation in training.
1 code implementation • 3 Oct 2022 • Wenguan Wang, James Liang, Dongfang Liu
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.
1 code implementation • 11 Jul 2022 • Zhiyuan Cheng, James Liang, Hongjun Choi, Guanhong Tao, Zhiwen Cao, Dongfang Liu, Xiangyu Zhang
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.