Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused on improving detection accuracy, the robustness of event-based visual models against physical adversarial attacks has received limited attention. For example, adversarial physical objects, such as specific clothing patterns or accessories, can exploit inherent vulnerabilities in these systems, leading to misdetections or misclassifications. This study is the first to explore physical adversarial attacks on event-driven pedestrian detectors, specifically investigating whether certain clothing patterns worn by pedestrians can cause these detectors to fail, effectively rendering them unable to detect the person. To address this, we developed an end-to-end adversarial framework in the digital domain, framing the design of adversarial clothing textures as a 2D texture optimization problem. By crafting an effective adversarial loss function, the framework iteratively generates optimal textures through backpropagation. Our results demonstrate that the textures identified in the digital domain possess strong adversarial properties. Furthermore, we translated these digitally optimized textures into physical clothing and tested them in real-world scenarios, successfully demonstrating that the designed textures significantly degrade the performance of event-based pedestrian detection models. This work highlights the vulnerability of such models to physical adversarial attacks.

Spatio-temporal compression of videos, utilizing networks such as Variational Autoencoders (VAE), plays a crucial role in OpenAI's SORA and numerous other video generative models. For instance, many LLM-like video models learn the distribution of discrete tokens derived from 3D VAEs within the VQVAE framework, while most diffusion-based video models capture the distribution of continuous latent extracted by 2D VAEs without quantization. The temporal compression is simply realized by uniform frame sampling which results in unsmooth motion between consecutive frames. Currently, there lacks of a commonly used continuous video (3D) VAE for latent diffusion-based video models in the research community. Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization. To address this issue, we propose a method for training a video VAE of latent video models, namely CV-VAE, whose latent space is compatible with that of a given image VAE, e.g., image VAE of Stable Diffusion (SD). The compatibility is achieved by the proposed novel latent space regularization, which involves formulating a regularization loss using the image VAE. Benefiting from the latent space compatibility, video models can be trained seamlessly from pre-trained T2I or video models in a truly spatio-temporally compressed latent space, rather than simply sampling video frames at equal intervals. To improve the training efficiency, we also design a novel architecture for the video VAE. With our CV-VAE, existing video models can generate four times more frames with minimal finetuning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed video VAE.

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the- wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://github.com/MyNiuuu/VCSD.

Low-light video enhancement in the visible (VIS) range is important yet technically challenging, and it is likely to become more tractable by introducing near-infrared (NIR) information for assistance, which in turn arouses a new challenge on how to obtain appropriate multispectral data for model training. In this paper, we defend the feasibility and superiority of NIR-assisted low-light video enhancement results by using unpaired 24-hour data for the first time, which significantly eases data collection and improves generalization performance on in-the-wild data. By accounting for different physical characteristics between unpaired daytime and nighttime videos, we first propose to turn daytime NIR & VIS into "nighttime mode". Specifically, we design a heuristic yet physics-inspired relighting algorithm to produce realistic pseudo nighttime NIR, and use a resampling strategy followed by a noiseGAN for nighttime VIS conversion. We further devise a temporal-aware network for video enhancement that extracts and fuses bi-directional temporal streams and is trained using real daytime videos and pseudo nighttime videos. We capture multi-spectral data using a co-axial camera and contribute Fulltime Multi-Spectral Video Dataset (FMSVD), the first dataset including aligned 24-hour NIR & VIS videos. Compared to alternative methods, we achieve significantly improved video quality as well as generalization ability on in-the-wild data in terms of both evaluation metrics and visual judgment. Codes and Data Available: https://github.com/MyNiuuu/NVEU.

Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut filter. At night, the filter is disabled to capture near-infrared (NIR) light emitted from NIR LEDs typically mounted around the lens. While the vulnerabilities of RGB-based AI algorithms have been widely reported, those of NIR-based AI have rarely been investigated. In this paper, we identify fundamental vulnerabilities in NIR-based image understanding caused by color and texture loss due to the intrinsic characteristics of clothes' reflectance and cameras' spectral sensitivity in the NIR range. We further show that the nearly co-located configuration of illuminants and cameras in existing surveillance systems facilitates concealing and fully passive attacks in the physical world. Specifically, we demonstrate how retro-reflective and insulation plastic tapes can manipulate the intensity distribution of NIR images. We showcase an attack on the YOLO-based human detector using binary patterns designed in the digital space (via black-box query and searching) and then physically realized using tapes pasted onto clothes. Our attack highlights significant reliability concerns about nighttime surveillance systems, which are intended to enhance security. Codes Available: https://github.com/MyNiuuu/AdvNIR.

Automatic colorization of anime line art videos aims to produce color frames given line art frames and reference color images, which is challenging due to various motions and geometric transformations across frame sequences. Existing methods usually utilize the feature maps of reference images directly and treat all the regions in an image equally. However, this may overlook the details of the regions undergoing geometric transformations . To emphasize the regions with significant transformations between the reference and target frames, we propose a Transformation Region Enhancement Network (TRE-Net) to exploit useful reference information and enhance the colorization of key transformation regions with Region Localization Module (RLM) and Feature Enhancement Module (FEM). Specifically, we propose Multi-scale Euclidean Distance Difference (Multi-scale EDD) Maps in RLM which effectively locate geometric transformation regions by contrasting the Euclidean Distance Maps of two line arts and aggregating representations at multiple scales of the network. In addition, FEM is devised to enhance feature learning in the regions with geometric transformation and to ensure proper color alignment. FEM learns locally enhanced features through an attention-gating operation at a low computational cost. With the well-represented key geometric transformation regions, our method exploits the multi-scale reference information well for color alignment, thus produces perceptually pleasing frames. Comprehensive experimental results show that our proposed method is superior to existing methods in terms of the overall quality of colorized anime line art videos.

Automatic sketch colorization is a challenging task that aims to generate a color image from a sketch, primarily due to its inherently ill-posed nature. While many approaches have shown promising results, two significant challenges remain: limited color patterns and a wide range of artifacts such as color bleeding and semantic inconsistencies among relevant regions. These issues stem from the operation of traditional convolutional structures, which capture structural features in a pixel-wise manner, resulting in inadequate utilization of regional information within the sketch. Therefore, we propose the Region-Assisted Sketch Coloring (RASC) method, which introduces an intermediate representation called the 'Region Map' to explicitly characterize the regional information of the sketch. This Region Map is derived from the input sketch and is effectively formulated by our RASC architecture, enhancing the perception of region-wise features beyond the original pixel-wise features. Specifically, we start by employing the sketch encoder to extract hierarchical feature maps from the input sketches. Subsequently, we introduce a coarse-to-fine decoder comprising a series of Region-based Modulation (RM) blocks. This decoder modulates features that combine the modulation results of its previous block and the sketch features of the corresponding encoder block with our Region Formulation module. Each module explicitly formulates the sketch features in a region-wise manner. This accurately captures both the inner-region local style and inter-region global context dependency, resulting in various color patterns and fewer synthesis artifacts. Our experimental results show that our proposed method surpasses state-of-the-art methods in both synthetic and real sketch datasets.

Muyao Niu, Jie Cai. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.