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

栏杆

Ambulatory

,Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion, fusion to further mitigate forgetting during adapter module fine-tuning. Experiments on several conventional benchmarks show that our method achieves state-of-the-art results. Our code is available at ..

机警

来这真柔软

转向

,Delving Deep into Engagement Prediction of Short Videos,al content, background music, and text data, are investigated to enhance engagement prediction. With the proposed dataset and two key metrics, our method demonstrates its ability to predict engagements of short videos purely from video content.

法律

,Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibrati proves robust against label shift, significantly improves model calibration in LTSSL contexts, and surpasses previous state-of-of-art approaches across multiple benchmarks, including CIFAR100-LT, STL10-LT, and ImageNet127, addressing class imbalance challenges in semi-supervised learning. Our code

Ringworm

,CLEO: Continual Learning of Evolving Ontologies,ver time, such as those in autonomous driving. We use Cityscapes, PASCAL VOC, and Mapillary Vistas to define the task settings and demonstrate the applicability of CLEO. We highlight the shortcomings of existing CIL methods in adapting to CLEO and propose a baseline solution, called Modelling Ontolo

终点

Advocacy for Persons with Senile Dementiaboth 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

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遗弃

Vijaya L. Melnick,Nancy Neveloff Dublerrompt Learning, utilizing multiple context-specific prompts for text embeddings to capture diverse class representations across masks. Overall, MTA-CLIP achieves state-of-the-art, surpassing prior works by an average of 2.8% and 1.3% on standard benchmark datasets, ADE20k and Cityscapes, respectivel