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

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,Evaluating the?Adversarial Robustness of?Semantic Segmentation: Trying Harder Pays Off,than previously reported. We also demonstrate a size-bias: small objects are often more easily attacked, even if the large objects are robust, a phenomenon not revealed by current evaluation metrics. Our results also demonstrate?that a diverse set of strong attacks is necessary, because different mo

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,SKYSCENES: A Synthetic Dataset for?Aerial Scene Understanding,point conditions (height and pitch), weather and time of day, and (4) incorporating additional sensor modalities (depth) can improve aerial scene understanding. Our dataset and associated generation code are publicly available at:

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Large-Scale Multi-hypotheses Cell Tracking Using Ultrametric Contours Maps,a faster integer linear programming formulation, and the framework is flexible, supporting segmentations from individual off-the-shelf cell segmentation models or their combination as an ensemble. The code is available as supplementary material.

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,EraseDraw: Learning to?Insert Objects by?Erasing Them from?Images,r model achieves state-of-the-art results in object insertion, particularly for in-the-wild images. We show compelling results on diverse insertion prompts and images across various domains. In addition, we automate iterative insertion by combining our insertion model with beam search guided by CLIP

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,SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for?On-device Inference,tedly for each case. SuperFedNAS addresses these challenges by decoupling the training and search in federated NAS. SuperFedNAS co-trains a large number of diverse DNN architectures contained inside one supernet in the FL setting. Post-training, clients perform NAS locally to find specialized DNNs b