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Found 7 papers, 5 papers with code
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions.
Distracting Downpour: Adversarial Weather Attacks for Motion Estimation
Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world.
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology.
Attacking Motion Estimation with Adversarial Snow
Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world.
M-FUSE: Multi-frame Fusion for Scene Flow Estimation
Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand.
Ranked #1 on
Scene Flow Estimation
on Spring
Blind Image Inpainting with Sparse Directional Filter Dictionaries for Lightweight CNNs
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time.
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected.
