Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

变化

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,YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,appropriate neural network architecture has to be designed. Existing methods ignore a fact that when input data undergoes layer-by-layer feature transformation, large amount of information will be lost. This paper delve into the important issues of information bottleneck and reversible functions. We

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,Functional Transform-Based Low-Rank Tensor Factorization for Multi-dimensional Data Recovery, discrete transforms along the third (., temporal/spectral) dimension are dominating in existing t-LRTF methods, which hinders their performance in addressing temporal/spectral degeneration scenarios, ., video frame interpolation and multispectral image (MSI) spectral super-resolution. To overcome t

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MOAT

,Domain Reduction Strategy for Non-Line-of-Sight Imaging, with significantly reduced reconstruction time. In NLOS imaging, the visible surfaces of the target objects are notably sparse. To mitigate unnecessary computations arising from empty regions, we design our method to render the transients through partial propagations from a continuously sampled set

POINT

,HPE-Li: WiFi-Enabled Lightweight Dual Selective Kernel Convolution for Human Pose Estimation,l cost hindering its widespread adoption. This paper introduces a novel HPE-Li approach that harnesses multi-modal sensors (. camera and WiFi) to generate accurate 3D skeletal in HPE. We then develop an efficient deep neural network to process raw WiFi signals. Our model incorporates a distinctive m

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