Computer Vision – ECCV 2024 Workshops : Milan, Italy, September 29-October 4,…

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Computer Vision – ECCV 2024 Workshops : Milan, Italy, September 29-October 4, 2024, Proceedings, Paperback by Del Bue, Alessio (EDT); Canton, Cristian (EDT); Pont-tuset, Jordi (EDT); Tommasi, Tatiana (EDT), ISBN 303191855X, ISBN-13 9783031918551, Brand New, Free shipping in the US 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 29–October 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.

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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29-October 4, 2024,

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AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark. - Learning from Strong to Weak - An Enhanced Quality Comparison Network via Efficient Transfer Learning. - Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency.

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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29-October 4, 2024,

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Subtitle Milan, Italy, September 29–October 4, 2024, Proceedings, Part XV. - Single Image 3D Human Pose Estimation Using Sequential Joint Group Generation. Series Lecture Notes in Computer Science. Illustrations 100 Illustrations, color; 1 Illustrations, black and white.

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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29-October 4, 2024,

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Subtitle Milan, Italy, September 29–October 4, 2024, Proceedings, Part VIII. Series Lecture Notes in Computer Science. Multi-agent Collaborative Perception for Robotic Fleet: A Systematic Review. - StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection.

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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29-October 4, 2024,

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Subtitle Milan, Italy, September 29–October 4, 2024, Proceedings, Part XIX. Practical Dataset Distillation Based on Deep Support Vectors. - DiM: Distilling Dataset into Generative Model. - Data-Efficient Generation for Dataset Distillation.

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