DinoLizer: Learning from the Best for Generative Inpainting Localization

LLaVA-UHD v3 has been introduced as a new multi-modal large language model (MLLM) that utilizes Progressive Visual Compression (PVC) for efficient native-resolution encoding, enhancing visual understanding capabilities while addressing computational overhead. This model integrates refined patch embedding and windowed token compression to optimize performance in vision-language tasks.

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.

The introduction of V^2-SAM represents a significant advancement in cross-view object correspondence, specifically addressing the challenges of ego-exo object correspondence by adapting the SAM2 model through two innovative prompt generators. This framework enhances the ability to establish consistent associations of objects across varying viewpoints, overcoming limitations posed by drastic viewpoint and appearance variations.

A new automated histopathologic assessment framework for Hirschsprung Disease has been developed using a multi-stage Vision Transformer approach. This framework effectively segments the muscularis propria, delineates the myenteric plexus, and identifies ganglion cells, achieving a Dice coefficient of 89.9% and a Plexus Inclusion Rate of 100% across 30 whole-slide images with expert annotations.

A recent study has introduced a modular, on-site solution for sustainable nutrient management in agriculture, utilizing lightweight anomaly detection techniques to optimize nutrient consumption and enhance crop growth. The approach employs a tiered pipeline for status estimation and anomaly detection, integrating multispectral imaging and an autoencoder for early warnings during nutrient depletion experiments.

A new method called DWFF-Net has been developed to identify multi-scale farmland system habitats using an adaptive dynamic weight strategy. This approach addresses the shortcomings of existing habitat classification systems by providing a comprehensive dataset of ultra-high-resolution remote sensing images that categorize cultivated land into 15 distinct habitat types.

A new study introduces the Interleaved Multi-Domain Identity Curriculum (IMIC), enabling models to perform object recognition, face recognition from varying image qualities, and person recognition in a unified embedding space without significant catastrophic forgetting. This approach was tested on foundation models DINOv3, CLIP, and EVA-02, demonstrating comparable performance to domain experts across all tasks.

A new method for classifying glioblastoma subregions using a contrastive learning-based encoder has been developed, achieving notable performance metrics in the BraTS-Path 2025 Challenge. The model, which fine-tunes a pre-trained Vision Transformer, secured second place with an MCC of 0.6509 and an F1-score of 0.5330 on the final test set.