69 citations · 157 across the 34 of their papers we have counts for
6 papers · 1 filter
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Chen Zhou, Peng Cheng, Junfeng Fang +6
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of fe…
CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning
Yuan Xun, Siyuan Liang, Xiaojun Jia +2
Pre-trained large models for multimodal contrastive learning, such as CLIP, have been widely recognized in the industry as highly susceptible to data-poisoned backdoor attacks. Thi…
Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning
Xinwei Liu, Xiaojun Jia, Yuan Xun +2
Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs crawled from the Internet. However,…
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds
Tianrui Lou, Xiaojun Jia, Jindong Gu +4
Adversarial attack methods based on point manipulation for 3D point cloud classification have revealed the fragility of 3D models, yet the adversarial examples they produce are eas…
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory
Sensen Gao, Xiaojun Jia, Xuhong Ren +2
Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs…
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
Jiawei Liang, Siyuan Liang, Aishan Liu +3
The proliferation of face forgery techniques has raised significant concerns within society, thereby motivating the development of face forgery detection methods. These methods aim…