Recently, egocentric action anticipation for wearable robotics cameras has gained considerable attention due to its capability to analyze nouns and verbs from a firstperson view. However, this field encounters challenges due to various uncertainties, such as action-irrelevant information and semantically fused representations of verbs and nouns. To overcome these issues, we introduce Ego-$A^{3}$, designed to improve the robustness and reliability of egocentric action anticipation systems. Ego-$A^{3}$adaptively extracts actionrelevant data to efficiently utilize additional information beyond visual data. Additionally, Ego-$A^{3}$produces effective disentangled representations for verbs and nouns by employing learnable verb and noun queries. Experiments on the EpicKitchens-100 and EGTEA Gaze+ datasets demonstrate that Ego-$A^{3}$outperforms existing methods in top-1 accuracy and mean top- 5 recall. Our code is publicly available at https://github.com/alsgur0720/egocentricanticipation.

The development of a multimodal fusion technique utilizing LiDAR-camera data has enabled precise 3D object detection for self-driving vehicles, particularly in ideal conditions with clear weather. Nevertheless, adverse weathers such as fog, snow, and rain remain a challenge for existing multimodal methods. These conditions lead to a reduced density of point clouds as a result of laser signal occlusion and attenuation. Additionally, as the distance grows, the point cloud becomes sparser, further challenging object detection tasks. To address these problems, we introduce a point reconstruction network employing equirectangular projection tailored for multimodal 3D object detection. This network incorporates a range-constrained noise filter to remove noise caused by adverse weather and an object-centric point generator designed to flexibly generate points for distant objects. Moreover, we propose a dual 2D auxiliary module to enhance image features and support the point reconstruction. Experimental evaluations conducted on adverse weather datasets demonstrate that the suggested approach surpasses current techniques. The implementation can be accessed at https://github.com/jhyoon964/oprnet.

Face-swapping deepfake poses significant risks, including privacy violations, misinformation, and defamation, amplified by the availability of pretrained models on open-source platforms. Proactive defense strategies aim to disrupt deepfake generation by modifying the original images to protect identity features. However, existing methods often introduce artifacts in facial images or rely on specific deepfake models, limiting their usability. To address these problems, we propose a style code orchestration in latent space (SCOL) method that obfuscates identity by fusing different identities in the latent space without requiring face recognition models. This study optimizes the generator to follow the original appearance while retaining the obfuscated identity via identity-preserving constraints. Further, appearance-dominant components in the latent code are aligned for visual consistency. An identity inversion attack is introduced using opposite style codes to improve the effectiveness of the defense. Experimental results demonstrate that SCOL robustly defends against various face-swapping deepfake methods, maintaining visual consistency.

Facial expression recognition (FER) models often face challenges when generalizing across domains, such as different datasets and age groups. Despite the significance of this problem, FER in children (child FER) research remains relatively understudied, and such studies exhibit vulnerability to cross-domain evaluation. In response to the scarcity of child FER research, we propose a novel domain-agnostic approach for robust child FER. The architecture integrates a child-centric source-domain reconstructor and a child emotion feature-guided classifier. First, we use a secondary image diffusion model to reconstruct the image with source-domain childlike features while preserving emotion. Second, we recognize facial expressions based on domain-agnostic features from reconstructed images through a cross-attention mechanism. This approach counters performance degradation caused by domain discrepancies and improves the generalizability of child FER. We validate the proposed approach with diverse, publicly available datasets to highlight its effectiveness. The source code is available at https://github.com/st0421/Child-FER.

Most research on facial expression recognition (FER) is conducted in highly controlled environments, but its performance is often unacceptable when applied to real-world situations. This is because when unexpected objects occlude the face, the FER network faces difficulties extracting facial features and accurately predicting facial expressions. Therefore, occluded FER (OFER) is a challenging problem. Previous studies on occlusion-aware FER have typically required fully annotated facial images for training. However, collecting facial images with various occlusions and expression annotations is time-consuming and expensive. Latent-OFER, the proposed method, can detect occlusions, restore occluded parts of the face as if they were unoccluded, and recognize them, improving FER accuracy. This approach involves three steps: First, the vision transformer (ViT)based occlusion patch detector masks the occluded position by training only latent vectors from the unoccluded patches using the support vector data description algorithm. Second, the hybrid reconstruction network generates the masking position as a complete image using the ViT and convolutional neural network (CNN). Last, the expression-relevant latent vector extractor retrieves and uses expression-related information from all latent vectors by applying a CNN-based class activation map. This mechanism has a significant advantage in preventing performance degradation from occlusion by unseen objects. The experimental results on several databases demonstrate the superiority of the proposed method over state-of-the-art methods. This code is available at https://github.com/leeisack/Latent-OFER.