Large-scale Vision-Language Models (VLMs) have achieved notable progress in aligning visual inputs with text. However, their ability to deeply understand the unique physical properties of non-RGB vision sensor images remains limited. In this paper, we revisit and analyze these limitations and introduce a novel, cost-efficient paradigm that significantly advances sensor image understanding-without requiring extensive training data or any modifications to the existing VLM architectures. Specifically, we propose Sensor-Aware Attributes Fine-Tuning (SAFT) with the Diverse Negative Attributes (DNA) optimization, which leverages minimal sensor-specific data to enable robust learning of non-RGB characteristics and overcome RGB-centric biases inherent in current VLMs. In addition, we present VS-TDX-the first comprehensive, public benchmark designed to rigorously evaluate VLMs' sensor-specific understanding across diverse and realistic scenarios. Through extensive experiments on VLMs and various sensor modalities, we validate that our method consistently delivers superior performance and generalization under resource-constrained and architecture-invariant settings. Our approach provides a practical advance towards scalable deployment of VLMs in increasingly sensor-diverse real-world environments.

Multispectral pedestrian detection is attractive for around-the-clock applications due to the complementary information between RGB and thermal modalities. However, current models often fail to detect pedestrians in certain cases (e.g., thermal-obscured pedestrians), particularly due to the modality bias learned from statistically biased datasets. In this paper, we investigate how to mitigate modality bias in multispectral pedestrian detection using a Large Language Model (LLM). Accordingly, we design a Multispectral Chain-of-Thought (MSCoT) prompting strategy, which prompts the LLM to perform multispectral pedestrian detection. Moreover, we propose a novel Multispectral Chain-of-Thought Detection (MSCoTDet) framework that integrates MSCoT prompting into multispectral pedestrian detection. To this end, we design a Language-driven Multi-modal Fusion (LMF) strategy that enables fusing the outputs of MSCoT prompting with the detection results of vision-based multispectral pedestrian detection models. Extensive experiments validate that MSCoTDet effectively mitigates modality biases and improves multispectral pedestrian detection.

This article introduces a novel approach, called causal prompting network (CPNet), to enhance the causal intervention in the context of image captioning. By leveraging visual prompt engineering in the feature space, this method aims to achieve superior performance in causal intervention tasks. Since CPNet is highly flexible and adaptable, it can be incorporated into any existing causal intervention-based image captioning framework. Specifically, two types of visual prompts-causal region of interest (RoI) prompt (CRP) and causal matching prompt (CMP)-are employed to refine the feature representations effectively. CRP is utilized on the RoI feature of the object feature to enhance RoI features with deconfounded causal features. Meanwhile, CMP is used to strengthen the contextual representation of confounders linked to image captioning tasks. To evaluate the proposed CPNet's effectiveness, an extensive range of experiments are conducted on the popular microsoft common objects in context dataset (MS-COCO) and Flickr30k datasets, and the results are validated using the Karpathy split. Experimental results demonstrate that the proposed CPNet surpasses the performance of other state-of-the-art (SOTA) image captioning methods.

RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically, datasets in multispectral pedestrian detection mainly distribute between ROTO11R⋆T⋆ refers to the visibility (O/X) in each modality. Generally, ROTO refers to daytime images, and RXTO refers to nighttime images. ROTX refers to daytime images in obscured situations. (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data. To address this problem, we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover, we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST, CVC-14, FLIR) and the new ROTX-MP. Our code and dataset are available at: https://github.com/ssbin0914/Causal-Mode-Multiplexer.git.

Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have been proposed, among which adversarial training (AT) has been demonstrated to be the most effective strategy. However, it has been known that AT sometimes hurts natural accuracy. Then, many works focus on optimizing model parameters to handle the problem. Different from the previous approaches, in this article, we propose a new approach to improve the adversarial robustness using an external signal rather than model parameters. In the proposed method, a well-optimized universal external signal called a booster signal is injected into the outside of the image which does not overlap with the original content. Then, it boosts both adversarial robustness and natural accuracy. The booster signal is optimized in parallel to model parameters step by step collaboratively. Experimental results show that the booster signal can improve both the natural and robust accuracies over the recent state-of-the-art AT methods. Also, optimizing the booster signal is general and flexible enough to be adopted on any existing AT methods.

Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its security against physical attacks is severely understudied. We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. Utilizing transparent Low-e films, we realize a laminated visible-thermal physical attack by attaching Low-e films over a visible attack printing. Moreover, we apply our physical method to manufacture a Multispectral Invisible Suit that hides persons from the multiple view angles of Multispectral detectors. To simulate our attack under various surveillance scenes, we constructed a large-scale multispectral pedestrian dataset which we will release in public. Extensive experiments show that our proposed method effectively attacks the state-of-the-art multispectral detector both in the digital space and the physical world.

Object detection plays an important role in security-critical systems such as autonomous vehicles but has shown to be vulnerable to adversarial patch attacks. Existing defense methods are restricted to localized noise patches by removing noisy regions in the input image. However, adversarial patches have developed into natural-looking patterns which evade existing defenses. To address this issue, we propose a defense method based on a novel concept "Adversarial Patch- Feature Energy" (APE) which exploits common deep feature characteristics of an adversarial patch. Our proposed defense consists of APE-masking and APE-refinement which can be employed to defend against any adversarial patch on literature. Extensive experiments demonstrate that APE-based defense achieves impressive robustness against adversarial patches both in the digital space and the physical world.

Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks.

The success of multi-sensor data fusions in deep learning appears to be attributed to the use of complementary information among multiple sensor datasets. Compared to their predictive performance, relatively less attention has been devoted to the adversarial robustness of multi-sensor data fusion models. To achieve adversarial robust multi-sensor data fusion networks, we propose here a novel robust training scheme called Multi-Sensor Cumulative Learning (MSCL). The motivation behind the MSCL method is based on the way human beings learn new skills. The MSCL allows the multi-sensor fusion network to learn robust features from individual sensors, and then learn complex joint features from multiple sensors just as people learn to walk before they run. The step wise framework of MSCL enables the network to incorporate pre-trained knowledge of robustness with new joint information from multiple sensors. Extensive experimental evidence validated that the MSCL outperforms other multi-sensor fusion training in defending against adversarial examples.

Many studies on reducing the adversarial vulnerability of deep neural networks have been published in the field of machine learning. To evaluate the actual robustness of networks, various adversarial attacks have been proposed. Most previous works have focused on white-box settings which assume that the adversary can have full access to the target models. Since they are not practical in real-world situations, recent studies on black-box attacks have received a lot of attention. However, existing black-box attacks have critical limitations, such as yielding a low attack success rate or relying too much on gradient estimation and decision boundaries. Those attacks are ineffective against weak defenses using gradient obfuscation. In this paper, we propose a novel gradient-free decision-based black-box attack using random search optimization. The proposed method only needs a hard-label (decision-based) and is effective against defenses using gradient obfuscation. Experimental results validate its query-efficiency and improved L2distance.