Morpho-Aware Global Attention for Image Matting

Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) face inherent challenges in image matting, particularly in preserving fine structural details. ViTs, with their global receptive field enabled by the self-attention mechanism, often lose local details such as hair strands. Conversely, CNNs, constrained by their local receptive field, rely on deeper layers to approximate global context but struggle to retain fine structures at greater depths. To overcome these limitations, we propose a novel Morpho-Aware Global Attention (MAGA) mechanism, designed to effectively capture the morphology of fine structures. MAGA employs Tetris-like convolutional patterns to align the local shapes of fine structures, ensuring optimal local correspondence while maintaining sensitivity to morphological details. The extracted local morphology information is used as query embeddings, which are projected onto global key embeddings to emphasize local details in a broader context. Subsequently, by projecting onto value embeddings, MAGA seamlessly integrates these emphasized morphological details into a unified global structure. This approach enables MAGA to simultaneously focus on local morphology and unify these details into a coherent whole, effectively preserving fine structures. Extensive experiments show that our MAGA-based ViT achieves significant performance gains, outperforming state-of-the-art methods across two benchmarks with average improvements of 4.3% in SAD and 39.5% in MSE.

Visual-Linguistic Agent: Towards Collaborative Contextual Object Reasoning

Multimodal Large Language Models (MLLMs) excel at descriptive tasks within images but often struggle with precise object localization, a critical element for reliable visual interpretation. In contrast, traditional object detection models provide high localization accuracy but frequently generate detections lacking contextual coherence due to limited modeling of inter-object relationships. To address this fundamental limitation, we introduce the \textbf{Visual-Linguistic Agent (VLA), a collaborative framework that combines the relational reasoning strengths of MLLMs with the precise localization capabilities of traditional object detectors. In the VLA paradigm, the MLLM serves as a central Linguistic Agent, working collaboratively with specialized Vision Agents for object detection and classification. The Linguistic Agent evaluates and refines detections by reasoning over spatial and contextual relationships among objects, while the classification Vision Agent offers corrective feedback to improve classification accuracy. This collaborative approach enables VLA to significantly enhance both spatial reasoning and object localization, addressing key challenges in multimodal understanding. Extensive evaluations on the COCO dataset demonstrate substantial performance improvements across multiple detection models, highlighting VLA's potential to set a new benchmark in accurate and contextually coherent object detection.

CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders

Cryo-electron microscopy (cryo-EM) emerges as a pivotal technology for determining the architecture of cells, viruses, and protein assemblies at near-atomic resolution. Traditional particle picking, a key step in cryo-EM, struggles with manual effort and automated methods' sensitivity to low signal-to-noise ratio (SNR) and varied particle orientations. Furthermore, existing neural network (NN)-based approaches often require extensive labeled datasets, limiting their practicality. To overcome these obstacles, we introduce cryoMAE, a novel approach based on few-shot learning that harnesses the capabilities of Masked Autoencoders (MAE) to enable efficient selection of single particles in cryo-EM images. Contrary to conventional NN-based techniques, cryoMAE requires only a minimal set of positive particle images for training yet demonstrates high performance in particle detection. Furthermore, the implementation of a self-cross similarity loss ensures distinct features for particle and background regions, thereby enhancing the discrimination capability of cryoMAE. Experiments on large-scale cryo-EM datasets show that cryoMAE outperforms existing state-of-the-art (SOTA) methods, improving 3D reconstruction resolution by up to 22.4%.