Search Results for author:
Found 4 papers, 1 papers with code
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
To achieve high accuracy on both clean and adversarial data, we propose building a spatial-temporal continuous representation using the semantic text guidance of the object of interest.
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory
Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs).
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions
Then, we propose a novel core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture.
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
