Multi-View Feature Fusion

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33 papers6 papers in the last four weeks, up 50% on the four weeks before. 0.0% of all new papers.

Fusion Complexity Is Not Fusion Quality: Backbone, Cross View Fusion and Metadata as Separate Factors in Pasture Biomass Regression
Mar 8, 2026Mridankan MandalMulti-View Feature Fusion Agricultural Image Analysis
Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, India
VGGDrive: Empowering Vision-Language Models with Cross-View Geometric Grounding for Autonomous Driving
Feb 24, 2026Jie Wang, Guang Li, Zhijian Huang +4Multi-View Feature Fusion VLMs for Autonomous Driving
College of Intelligence and Computing, Tianjin University · Xiaomi EV
Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
Mar 17, 2025Farnoush Bayatmakou, Reza Taleei, Milad Amir Toutounchian +1Mammography Image Classification Multi-View Feature Fusion
Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada · Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA · College of Computing & Informatics, Drexel University, Philadelphia, Pennsylvania, USA

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Fusion Complexity Is Not Fusion Quality: Backbone, Cross View Fusion and Metadata as Separate Factors in Pasture Biomass Regression

Mridankan Mandal

Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, India

Multi-View Feature Fusion Agricultural Image Analysis Vision Foundation Model Adaptation Multi-Source Image Fusion Agricultural Remote Sensing

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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.

Latest papers

Fusion Complexity Is Not Fusion Quality: Backbone, Cross View Fusion and Metadata as Separate Factors in Pasture Biomass Regression

VGGDrive: Empowering Vision-Language Models with Cross-View Geometric Grounding for Autonomous Driving

Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework