Learning with Preserving for Continual Multitask Learning

MMEdge is a new framework designed for real-time multimodal inference on resource-constrained edge devices, crucial for applications like autonomous driving and mobile health. The framework addresses the challenges of sensing dynamics and model execution by decomposing the inference process into fine-grained sensing and encoding units. This allows for incremental computation as data arrives, while a lightweight temporal aggregation module captures rich temporal dynamics to maintain accuracy.

The X-VMamba framework introduces a controllability-based interpretability approach for State Space Models (SSMs), particularly the Mamba architecture. This framework aims to enhance understanding of how Vision SSMs process spatial information, addressing the challenges posed by the lack of transparent mechanisms in existing models. Two methods are proposed: a Jacobian-based method for general SSM architectures and a Gramian-based approach for diagonal SSMs, both designed to measure the influence of input sequences on internal state dynamics efficiently.

The article discusses the growing interest in world models, particularly in light of advancements in multimodal large language models like GPT-4 and video generation models such as Sora. World models serve two main purposes: understanding current world mechanisms and predicting future dynamics. The review categorizes these models and highlights their applications in various fields, including generative games, autonomous driving, robotics, and social simulacra, emphasizing their role in decision-making processes.

Diabetic Retinopathy (DR) is a major cause of preventable blindness, making early detection essential for reducing global vision loss. Recent advancements in deep learning have significantly improved DR screening, evolving from basic convolutional neural networks to sophisticated methodologies that tackle issues like class imbalance and label scarcity. This survey synthesizes findings from over 50 studies and 20 datasets, highlighting methodological innovations and ongoing challenges in validation and reproducibility.

The article discusses advancements in autonomous driving systems that utilize 3D object detection through RGB cameras, which are more cost-effective than LiDAR. Despite their promising detection accuracy, these systems are vulnerable to adversarial attacks. The study introduces AdvRoad, a method to create realistic road-style adversarial posters that can deceive detection systems without being easily noticed. This approach aims to enhance the safety and reliability of autonomous driving technologies.

The CATS-V2V dataset introduces a pioneering real-world collection for Vehicle-to-Vehicle (V2V) cooperative perception, aimed at enhancing autonomous driving in complex adverse traffic scenarios. Collected using two time-synchronized vehicles, the dataset encompasses 100 clips featuring 60,000 frames of LiDAR point clouds and 1.26 million multi-view camera images across various weather and lighting conditions. This dataset is expected to significantly benefit the autonomous driving community by providing high-quality data for improved perception capabilities.

The article introduces MAFM^3, a framework designed for the modular adaptation of foundation models in multi-modal medical AI. It addresses the challenge of limited data in medical imaging by allowing a single foundation model to adapt to various domains, tasks, and modalities using lightweight modular components. This approach enables flexible activation of specific capabilities based on the input type or clinical objective, improving multitask and multimodality adaptation.

The paper titled 'FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection' addresses the challenges of deploying PETR models in autonomous driving due to their high computational costs and memory requirements. It introduces FQ-PETR, a fully quantized framework that aims to enhance efficiency without sacrificing accuracy. Key innovations include a Quantization-Friendly LiDAR-ray Position Embedding and techniques to mitigate accuracy degradation typically associated with quantization methods.