Multi-View Feature Fusion

Estimating pasture biomass from field images can support grazing decisions without repeated destructive sampling. The task remains difficult because available datasets are small, biomass components are imbalanced and a single photograph contains two spatially distinct views of the sampled quadrat. This work evaluates vision foundation models for five target pasture biomass regression on the 357 image CSIRO dataset. Across 25 model configurations, the analysis covers convolutional and self supervised transformer backbones, seven cross view fusion configurations, models with and without 23 metadata features, classical regressors and frozen foundation features. All comparisons use one locked five fold split and raw scale weighted R^2. DINOv3 Large with cross view gated attention gives the strongest verified single model result, 0.8338 +/- 0.0503 across folds. Under a matched gated depthwise convolution head, replacing DINOv2 Large with DINOv3 Large improves R^2 by 0.0372. Fusion results are not ordered by module complexity. Cross view gated attention is best, while a full Mamba fusion block reaches only 0.7930. Across four matched DINOv3 pathways, metadata narrows the score range from 0.0157 to 0.0013 while changing the mean by only +0.0034. It improves three weaker pathways but lowers the strongest attention model by 0.0061. Classical metadata models are strong, frozen DINOv3 features are linearly useful and Dry Dead biomass remains the hardest target. These results show that backbone choice, cross view interaction and metadata must be tested as separate factors on small agricultural datasets.

The significance of cross-view 3D geometric modeling capabilities for autonomous driving is self-evident, yet existing Vision-Language Models (VLMs) inherently lack this capability, resulting in their mediocre performance. While some promising approaches attempt to mitigate this by constructing Q&A data for auxiliary training, they still fail to fundamentally equip VLMs with the ability to comprehensively handle diverse evaluation protocols. We thus chart a new course, advocating for the infusion of VLMs with the cross-view geometric grounding of mature 3D foundation models, closing this critical capability gap in autonomous driving. In this spirit, we propose a novel architecture, VGGDrive, which empowers Vision-language models with cross-view Geometric Grounding for autonomous Driving. Concretely, to bridge the cross-view 3D geometric features from the frozen visual 3D model with the VLM's 2D visual features, we introduce a plug-and-play Cross-View 3D Geometric Enabler (CVGE). The CVGE decouples the base VLM architecture and effectively empowers the VLM with 3D features through a hierarchical adaptive injection mechanism. Extensive experiments show that VGGDrive enhances base VLM performance across five autonomous driving benchmarks, including tasks like cross-view risk perception, motion prediction, and trajectory planning. It's our belief that mature 3D foundation models can empower autonomous driving tasks through effective integration, and we hope our initial exploration demonstrates the potential of this paradigm to the autonomous driving community.

Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Recent breakthroughs in Artificial Intelligence (AI) have shown significant promise in development of advanced Deep Learning (DL) architectures for breast cancer diagnosis through mammography. In this context, the paper focuses on the integration of AI within a Human-Centric workflow to enhance breast cancer diagnostics. Key challenges are, however, largely overlooked such as reliance on detailed tumor annotations and susceptibility to missing views, particularly during test time. To address these issues, we propose a hybrid, multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that enhances diagnostic robustness and accuracy. The proposed MSMV-Swin framework is designed to work as a decision-support tool, helping radiologists analyze multi-view mammograms more effectively. More specifically, the MSMV-Swin framework leverages the Segment Anything Model (SAM) to isolate the breast lobe, reducing background noise and enabling comprehensive feature extraction. The multi-scale nature of the proposed MSMV-Swin framework accounts for tumor-specific regions as well as the spatial characteristics of tissues surrounding the tumor, capturing both localized and contextual information. The integration of contextual and localized data ensures that MSMV-Swin's outputs align with the way radiologists interpret mammograms, fostering better human-AI interaction and trust. A hybrid fusion structure is then designed to ensure robustness against missing views, a common occurrence in clinical practice when only a single mammogram view is available.