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

Digitalis

https://doi.org/10.1007/978-3-7091-3396-5oad implications for content creators and recommendation systems. This study delves deep into the intricacies of predicting engagement for newly published videos with limited user interactions. Surprisingly, our findings reveal that Mean Opinion Scores from previous video quality assessment datasets

Misgiving

https://doi.org/10.1007/978-3-0348-6370-4ons that bias classifiers. This problem is often aggravated by discrepancies between labeled and unlabeled class distributions, leading to biased pseudo-labels, neglect of rare classes, and poorly calibrated probabilities. To address these issues, we introduce Flexible Distribution Alignment (FlexDA

Latency

Ein Streifzug durch das Universumiously learned information, when presented with a new task. CL aims to instill the lifelong learning characteristic of humans in intelligent systems, making them capable of learning continuously while retaining what was already learned. Current CL problems involve either learning new domains (domain

頑固

institute

自作多情

,Retargeting Visual Data with?Deformation Fields,is technique applies to different kinds of visual data, including images, 3D scenes given as neural radiance fields, or even polygon meshes. Experiments conducted on different visual data show that our method achieves better content-aware retargeting compared to previous methods.

grotto

Pericarditis

公共汽車

Conference proceedings 2025ter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as Computer vision, Machine learning, Deep neural networks, Reinforcemen

Vo2-Max

,FARSE-CNN: Fully Asynchronous, Recurrent and?Sparse Event-Based CNN,both in space and time. We theoretically derive the complexity of all components in our architecture,?and experimentally validate our method on tasks for object recognition, object detection and gesture recognition. FARSE-CNN achieves similar or better performance than the state-of-the-art among asy