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Found 7 papers, 2 papers with code
MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection
Specifically, we generate text descriptions of the pedestrian in each RGB and thermal modality and design a Multispectral Chain-of-Thought (MSCoT) prompting, which models a step-by-step process to facilitate cross-modal reasoning at the semantic level and perform accurate detection.
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection
As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data.
Advancing Adversarial Training by Injecting Booster Signal
Different from the previous approaches, in this paper, we propose a new approach to improve the adversarial robustness by using an external signal rather than model parameters.
Meta Input: How to Leverage Off-the-Shelf Deep Neural Networks
The proposed meta input can be optimized with a small number of testing data only by considering the relation between testing input data and its output prediction.
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision.
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense.
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data.
