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

深陷

,Non-Line-of-Sight Estimation of Fast Human Motion with Slow Scanning Imagers,sor systems that scan the visible region and analyze secondary reflections of light that has interacted with the hidden static scene. Estimating human activity around the corner will be a task of major interest for emerging NLoS applications, and some attempts have been reported in the recent litera

Conquest

,Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding,t typically on a large-scale cloud platform. Conventional methods propose to employ a serial concatenation of a learned image and source encoder, the latter projecting the image encoder output (bottleneck features) into a quantized representation for bitrate-efficient transmission. In the cloud, a r

squander

,NePhi: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration,inant voxel-based transformation fields used in learning-based registration approaches, . represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation r

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genesis

,Image Manipulation Detection with Implicit Neural Representation and Limited Supervision, require high-quality training datasets featuring image- and pixel-level annotations. The effectiveness of these methods suffers when applied to manipulated or noisy samples that differ from the training data. To address these challenges, we present a unified framework that combines unsupervised and

AER

,Scalar Function Topology Divergence: Comparing Topology of 3D Objects,pology between sublevel sets of two functions having a common domain. Functions can be defined on an undirected graph or Euclidean space of any dimensionality. Most of the existing methods for comparing topology are based on Wasserstein distance between persistence barcodes and they don’t take into

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,Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank Bott-trained models to adapt to new data through this low-rank bottleneck. However, PEFT tasks involving multiple modalities, like vision-language (VL) tasks, require not only adaptation to new data but also learning the relationship between different modalities. Targeting at VL PEFT tasks, we propose a

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