Generating anomalies is a crucial method to enhance detection and classification performance by expanding anomalous data repository. However, existing anomaly generation methods overlook the intrinsic entanglement between diverse anomaly types and product structures, leading to semantic ambiguity. We propose CADiff, a context-aware generation framework that reframes anomalies as compositional perturbations. Firstly, we propose Context-aware Text Prompt (CTP), a mechanism which contains multiple tokens that characterize anomalies and products separately to enhance the contextual consistency of generated images and refine the local variability of anomalies. Secondly, we develop Self-adaptive Spatial Control (SSC), a self-adaptive interaction design that mitigates anomaly leakage or missing phenomena. Thirdly, we introduce Intensity-controllable Attention Re-weighting (IAR), an inference scheduling scheme with the ability to amplify or attenuate abnormal semantic effects to improve generation diversity. Extensive experiments on MVTec AD and VisA datasets demonstrate the superiority of our proposed method over state-of-the-art methods in both realism and diversity of the generated results, and significantly improve the performance of downstream tasks, including anomaly detection, anomaly localization, and anomaly classification tasks.

Affective Forecasting is an psychology task that involves predicting an individual's future emotional responses, often hampered by reliance on external factors leading to inaccuracies, and typically remains at a qualitative analysis stage. To address these challenges, we narrows the scope of Affective Forecasting by introducing the concept of Human-interaction-based Emotion Forecasting (EF). This task is set within the context of a two-party interaction, positing that an individual's emotions are significantly influenced by their interaction partner's emotional expressions and informational cues. This dynamic provides a structured perspective for exploring the patterns of emotional change, thereby enhancing the feasibility of emotion forecasting.

Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically integrate emotions from various angles, including environmental cues and body language, whereas existing DFER methods tend to consider the scene as noise that needs to be filtered out, focusing solely on facial information. We refer to this as the Rigid Cognitive Problem. The Rigid Cognitive Problem can lead to discrepancies between the cognition of annotators and models in some samples. To align more closely with the human cognitive paradigm of emotions, we propose an Overall Understanding of the Scene DFER method (OUS). OUS effectively integrates scene and facial features, combining scene-specific emotional knowledge for DFER. Extensive experiments on the two largest datasets in the DFER field, DFEW and FERV39k, demonstrate that OUS significantly outperforms existing methods. By analyzing the Rigid Cognitive Problem, OUS successfully understands the complex relationship between scene context and emotional expression, closely aligning with human emotional understanding in real-world scenarios.

The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We identified two types of noise: one is caused by low-quality data resulting from factors such as occlusion, dim lighting, and blurriness; the other arises from mislabeled data due to annotation bias by annotators. Addressing the two types of noise, we have meticulously crafted a Dynamic Dual-Stage Purification (D2SP) Framework. This initiative aims to dynamically purify the DFER datasets of these two types of noise, ensuring that only high-quality and correctly labeled data is used in the training process. To mitigate low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which computes sample weights and prunes those low-weight samples. After CGP, the Fine-Grained Correction (FGC) stage evaluates prediction stability to correct mislabeled data. Moreover, D2SP is conceived as a general, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methods. Extensive experiments covering prevalent DFER datasets and deploying multiple benchmark methods have substantiated D2SP’s ability to enhance performance metrics.

Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits traditional detection methods, making anomaly generation essential for expanding the data repository. However, recent generative models often produce unrealistic anomalies increasing false positives, or require real-world anomaly samples for training. In this work, we treat anomaly generation as a compositional problem and propose ComGEN, a component-aware and unsupervised framework that addresses the gap in logical anomaly generation. Our method comprises a multi-component learning strategy to disentangle visual components, followed by subsequent generation editing procedures. Disentangled text-to-component pairs, revealing intrinsic logical constraints, conduct attention-guided residual mapping and model training with iteratively matched references across multiple scales. Experiments on the MVTecLOCO dataset confirm the efficacy of ComGEN, achieving the best AUROC score of$\mathbf{9 1. 2 \%}$. Additional experiments on the real-world scenario of Diesel Engine and widelyused MVTecAD dataset demonstrate significant performance improvements when integrating simulated anomalies generated by ComGEN into automated production workflows.

In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by the process through which the human brain handles emotions and the theory of cross-modal plasticity, we propose UMBEnet, a brain-like unified modal affective processing network. The primary design of UMBEnet includes a Dual-Stream (DS) structure that fuses inherent prompts with a Prompt Pool and a Sparse Feature Fusion (SFF) module. The design of the Prompt Pool is aimed at integrating information from different modalities, while inherent prompts are intended to enhance the system's predictive guidance capabilities and effectively manage knowledge related to emotion classification. Moreover, considering the sparsity of effective information across different modalities, the SSF module aims to make full use of all available sensory data through the sparse integration of modality fusion prompts and inherent prompts, maintaining high adaptability and sensitivity to complex emotional states. Extensive experiments on the largest benchmark datasets in the Dynamic Facial Expression Recognition (DFER) field, including DFEW, FERV39k, and MAFW, have proven that UMBEnet consistently outperforms the current state-of-the-art methods. Notably, in scenarios of Modality Missingness and multimodal contexts, UMBEnet significantly surpasses the leading current methods, demonstrating outstanding performance and adaptability in tasks that involve complex emotional understanding with rich multimodal information. Code can be obtained at https://github.com/Xinji-Mai/UMBEnet.

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect.

Video object segmentation (VOS) is a fundamental task for computer vision and multimedia. Despite significant progress of VOS models in recent works, there has been little research on the VOS models' adversarial robustness, posing serious security risks in the VOS models' practical applications (e.g., autonomous driving and video surveillance). Adversarial robustness refers to the ability of the model to resist malicious attacks on adversarial examples. To address this gap, we propose a one-shot adversarial robustness evaluation framework (i.e., the adversary only perturbs the first frame) for VOS models, including white-box and black-box attacks. For white-box attacks, we introduce Objective Attention (OA) and Boundary Attention (BA) mechanisms to enhance the attention of attack on objects from both pixel and object levels while mitigating issues such as multi-objects attack imbalance, attack bias towards the background, and boundary reservation. For black-box attacks, we propose the Video Diverse Input (VDI) module, which utilizes data augmentation to simulate historical information, improving our method's black-box transferability. We conduct extensive experiments to evaluate the adversarial robustness of VOS models with different structures. Our experimental results reveal that existing VOS models are more vulnerable to our attacks (both white-box and black-box) compared to other state-of-the-art attacks. We further analyze the influence of different designs (e.g., memory and matching mechanisms) on adversarial robustness. Finally, we provide insights for designing more secure VOS models in the future.

The in-the-wild dynamic facial expression recognition (DFER) has been challenging due to several high-dynamics factors such as limited dynamic expression-related frames and variable non-expression noise in facial expression sequences. To provide more expression-related clips for DFER models, we propose a novel and interpretable frequency-based method (Freq-HD) for high-dynamics affective clip selection. It can select clips containing pure expression changes from sequences and aid different DFER network structures in recognizing in-the-wild dynamic facial expressions more accurately and efficiently. We first design a novel spatial-temporal frequency analysis (STFA) module to compute the dynamics values of each clip by using sliding windows and spatial-temporal frequency analysis. Moreover, we propose a multi-band complementary selection (MBC) module to amend the inappropriate reaction of the dynamics values of different spatial frequency bands in STFA when expression-irrelevant noise occurs. Specifically, the MBC uses an ingenious mapping method to generate the inhibitory factors to complement and separate the dynamics of expressions and non-expressions in different frequency bands. The Freq-HD can select the most expression-correlated clips and the consisting frames, which could be incorporated into any existing DFER models. We extensively evaluate the Freq-HD on two in-the-wild datasets and four DFER baselines, showing that our method significantly improves the subsequent network performance while using fewer input frames and reducing computation cost. More ablation studies and visualization analysis provide further empirical evidence of the effectiveness of our method.

The acquisition and processing of visual data in industrial environments are of paramount importance. High-resolution (HR) images offer superior clarity and richer textural detail compared to low-resolution (LR) images. On the one hand, owing to the incorporation of richer information, HR images demonstrate substantially enhanced performance compared to LR images in downstream applications, such as anomaly detection. On the other hand, they provide valuable insights to designers and quality inspectors who require a detailed understanding of the images. Currently, the majority of research on super-resolution focuses on natural scenes such as cities and fields, however, the development of datasets for industrial scenes is still in its infancy. To address the image distortion in building realistic LR-HR image pairs in the industry environment, we design a capture to registration framework. It consists of the standard imaging system, physical calibration of the imaging system, as well as the rigid to elastic registration of the LR-HR image pairs. Thus, we build the first realistic industrial sence super-resolution dataset (IndSR), comprises of 50 sets of calibrated images with three scale factors and five typical defects. To benchmark IndSR, we employ quantitative, qualitative, and task-oriented studies to evaluate the representative super-resolution and anomaly detection methods. Besides, we systematically investigate and discuss the performances and results of the existing SISR methods to advance research in the field of super-resolution in industry environment. The IndSR dataset can be available from https://byw4ng.github.io/IndSR/.