Hybrid Convolution and Frequency State Space Network for Image Compression

A new model named SMARC has been introduced, enabling surface material reconstruction and classification from minimal visual cues, specifically using just a 10% contiguous patch of an image. This approach addresses the limitations of existing methods that require dense observations, making it particularly useful in constrained environments.

A study has demonstrated the cross-domain generalization capabilities of a multimodal large language model (LLM) for assessing global photovoltaic (PV) systems, addressing challenges posed by undocumented installations and the limitations of traditional computer vision models. The model integrates detection, localization, and quantification, achieving superior performance across unseen regions compared to conventional methods.

A novel hybrid architecture named HyM-UNet has been proposed to enhance medical image segmentation by combining the local feature extraction strengths of Convolutional Neural Networks (CNNs) with the global modeling capabilities of Mamba. This architecture employs a Hierarchical Encoder and a Mamba-Guided Fusion Skip Connection to effectively bridge local and global features, addressing the limitations of traditional CNNs in capturing complex anatomical structures.

A recent study has benchmarked deep learning models for differentiating true tumor progression from treatment-related pseudoprogression in glioblastoma using follow-up MRI scans from the Burdenko GBM Progression cohort. The analysis involved various deep learning architectures, revealing comparable accuracies across stages, with improved discrimination at later follow-ups.

CoD, a new compression-oriented diffusion foundation model, has been introduced to enhance image compression efficiency, particularly at ultra-low bitrates. Unlike existing models that rely on text conditioning, CoD is designed for end-to-end optimization of both compression and generation, achieving state-of-the-art results when integrated with downstream codecs like DiffC.

The recent paper titled 'Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs' addresses key challenges in adapting deep convolutional neural networks (CNNs) for fully homomorphic encryption (FHE) inference. It introduces a single-stage fine-tuning strategy and a generalized interleaved packing scheme to enhance the performance of CNNs while maintaining accuracy and supporting high-resolution image processing.