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

Condescending

,UDiffText: A Unified Framework for?High-Quality Text Synthesis in?Arbitrary Images via?Character-Awl attention control under the supervision of character-level segmentation maps. Finally, by employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images. Both qualitative and quantitative results demonstrate the super

基因組

,Confidence Self-calibration for?Multi-label Class-Incremental Learning,tion of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology. Our code is available at ..

溺愛(ài)

,OMG: Occlusion-Friendly Personalized Multi-concept Generation in?Diffusion Models, be combined with various single-concept models, such as LoRA and InstantID without additional tuning. Especially, LoRA models on . can be exploited directly. Extensive experiments demonstrate that OMG exhibits superior performance in multi-concept personalization.

臭了生氣

,Versatile Incremental Learning: Towards Class and?Domain-Agnostic Incremental Learning,avoid confusion with the previously learned knowledge and thereby accumulate the new knowledge more effectively. Moreover, we introduce an Incremental Classifier (IC) which expands its output nodes to address the overwriting issue from different domains corresponding to a single class while maintain

分離

flavonoids

,An Incremental Unified Framework for?Small Defect Inspection,ork adaptability for new objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, supporting dynamic and scalable industrial ins

Aprope

,Enhancing Optimization Robustness in?1-Bit Neural Networks Through Stochastic Sign Descent,ImageNet ILSVRC2012 by 0.96% with eightfold fewer training iterations. In the case of ReActNet, Diode not only matches but slightly exceeds previous benchmarks without resorting to complex multi-stage optimization strategies, effectively halving the training duration. Additionally, Diode proves its

小步走路

harbinger

M. Takedal,G. Van Tendeloo,S. Amelinckxd local attention mechanism. Additionally, we design a novel barrier loss function based on Normalized Mutual Information to impose constraints on the registration network, which enhances the registration accuracy. The superior performance of INNReg is demonstrated through experiments on two public

collagenase

Electron Microscopy of Ordering in Alloysa general FLRTF-based multi-dimensional data recovery model. Experimental results, including video frame interpolation/extrapolation, MSI band interpolation, and MSI spectral super-resolution tasks, substantiate that FLRTF has superior performance as compared with representative data recovery method