TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types

Wave-Former is a new method for high-accuracy 3D shape reconstruction of completely occluded everyday objects. Utilizing millimeter-wave (mmWave) wireless signals, it can penetrate common obstructions and reflect off hidden items. Unlike previous methods that faced limitations in coverage and noise, Wave-Former employs a physics-aware shape completion model to infer full 3D geometry. Its innovative three-stage pipeline connects raw wireless signals with advancements in vision-based shape completion, enhancing applications in robotics, augmented reality, and logistics.

The X-VMamba model introduces a controllability-based interpretability framework for State Space Models (SSMs), particularly the Mamba architecture. This framework aims to clarify how Vision SSMs process spatial information, which has been a challenge due to the absence of transparent mechanisms. The proposed methods include a Jacobian-based approach for any SSM architecture and a Gramian-based method for diagonal SSMs, both designed to enhance understanding of internal state dynamics while maintaining computational efficiency.

The paper titled 'Dynamic Gaussian Scene Reconstruction from Unsynchronized Videos' presents a novel approach to multi-view video reconstruction, crucial for applications in computer vision, film production, virtual reality, and motion analysis. The authors address the common issue of temporal misalignment in unsynchronized video streams, which can degrade reconstruction quality. They propose a temporal alignment strategy that utilizes a coarse-to-fine alignment module to estimate and compensate for time shifts between cameras, enhancing the overall reconstruction process.

An AI-assisted Augmented Reality (AR) assembly workflow has been developed, utilizing deep learning-based object recognition to identify assembly components and provide step-by-step instructions. The system displays bounding boxes around components in real-time, indicating their placement, thus eliminating the need for manual searching or sorting. A case study involving the assembly of LEGO sculptures demonstrates the system's feasibility and effectiveness in enhancing the assembly process.

The article discusses a significant advancement in computer vision, focusing on large-scale modality-invariant foundation models for brain MRI analysis. These models utilize self-supervised learning to leverage extensive unlabeled MRI data, enhancing performance in neuroimaging tasks such as lesion segmentation for stroke and epilepsy. The study highlights the importance of maintaining modality-specific features despite successful cross-modality alignment, and the model's code and checkpoints are publicly available.

The article presents D-GAP (Dataset-agnostic and Gradient-guided augmentation in Amplitude and Pixel spaces), a novel approach aimed at enhancing out-of-domain (OOD) robustness in computer vision applications. Traditional augmentations often fail under varying image conditions, while D-GAP introduces targeted augmentations in both amplitude and pixel spaces. This method addresses the learning bias of neural networks towards domain-specific frequency components, leading to improved performance across diverse datasets.