Dynamic Gaussian Scene Reconstruction from Unsynchronized Videos

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.

TEyeD is the world's largest unified public dataset of eye images, featuring over 20 million images collected using seven different head-mounted eye trackers, including devices integrated into virtual and augmented reality systems. The dataset encompasses a variety of activities, such as car rides and sports, and includes detailed annotations like 2D and 3D landmarks, semantic segmentation, and gaze vectors. This resource aims to enhance research in computer vision, eye tracking, and gaze estimation.

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.