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

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One-Stage Prompt-Based Continual Learning,introduce a Query-Pool Regularization (QR) loss that regulates the relationship between the prompt query and the prompt pool to improve representation power. The QR loss is only applied during training time, so there is no computational overhead at inference from the QR loss. With the QR loss, our a

Enliven

,SpaceJAM: a Lightweight and Regularization-Free Method for Fast Joint Alignment of Images, while significantly reducing computational demands and achieving at least a 10x speedup. SpaceJAM sets a new standard for rapid and effective image alignment, making the process more accessible and efficient. Our code is available at: ..

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avenge

,: Quantization in Low Data Regimes with Generative Synthetic Data,ta generation process, enhancing fidelity through the inversion of learnable token embeddings. Through rigorous experimentation, . establishes new benchmarks in data-free and data-scarce quantization, significantly outperforming existing methods in accuracy and efficiency, thereby setting a new stan

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,Dual-Level Adaptive Self-labeling for Novel Class Discovery in Point Cloud Segmentation,ning, reducing noise in generated segmentation. Finally, we conduct extensive experiments on two widely used datasets, SemanticKITTI and SemanticPOSS, and the results show our method outperforms the state of the art by a large margin.

Contort

,EBDM: Exemplar-Guided Image Translation with Brownian-Bridge Diffusion Models,image. To efficiently guide the diffusion process toward the style of exemplar, we delineate three pivotal components: the Global Encoder, the Exemplar Network, and the Exemplar Attention Module to incorporate global and detailed texture information from exemplar images. Leveraging Bridge diffusion,

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https://doi.org/10.1007/978-3-531-92543-1tion of important city blocks and buildings. Our approach achieves good realism, semantic consistency, and correctness across the heterogeneous urban styles in 330 US cities. Codes and datasets are released at ..