Computer Vision - ECCV 2024 Workshops

The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29October 4, 2024.

These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online.

Inhalt

Wild Berry image dataset collected in Finnish forests and peatlands using drones.- Soybean pod and seed counting in both outdoor fields and indoor laboratories using unions of deep neural networks.- A Framework for Enhanced Decision Support in Digital Agriculture Using Explainable Machine Learning.- Lincoln's Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw).- 3D Phenotyping of Canopy Occupation Volume as a Major Predictor for Canopy Photosynthesis in Rice (Oryza sativa L.).- Retrieval of sun-induced plant fluorescence in the O2-A absorption band from DESIS imagery.- Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders.- KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation.- RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection.- Consolidation of symbolic instances using sensor data via tracklet merging for long-term monitoring of crops.- Automated Generation of Accurate, Compact and Focused Crop and Weed Segmentation Models.- Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection.- Towards Auto-Generated Ground Truth for Evaluation of Perception Systems in Agriculture.- AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models.- Deep Learning Based Growth Modeling of Plant Phenotypes.- A simple approach to pavement cell segmentation.- Enhancing weed detection performance by means of GenAI-based image augmentation.- SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture.- Robust UDA for Crop and Weed Segmentation: Multi-Scale Attention and Style-Adaptive Techniques.- Ordinal-Meta Learning for Fine-grained Fruit Quality Prediction.- Beyond Annotations: Efficient Wheat Head Segmentation Using L-Systems, Game Engines, and Student-Teacher Models.- Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves.