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

红肿

https://doi.org/10.1007/978-3-476-04311-5einforcement learning and planning for such robotic agents is a generalizable reward function. Recent advances in vision-language models, such as CLIP, have shown remarkable performance in the domain of deep learning, paving the way for open-domain visual recognition. However, collecting data on rob

Detonate

https://doi.org/10.1007/978-3-476-04311-5urther correct these errors. In this paper, we investigate a multi-step iterative approach for the first time to tackle the challenging natural image matting task, and achieve excellent performance by introducing a pixel-level denoising diffusion method (DiffMatte) for the alpha matte refinement. To

向下

Instructions to the Worker Bee, across image classification, image synthesis, and object detection & segmentation tasks. ATC merges clusters through bottom-up hierarchical clustering, without the introduction of extra learnable parameters. We find that ATC achieves state-of-the-art performance across all tasks, and can even perfo

严厉谴责

Beautiful Lies and Beautiful Truths,ue to the rapid iteration of 3D sensors, which leads to significantly different distributions in point clouds. This, in turn, results in subpar performance of 3D cross-sensor object detection. This paper introduces a .ross .echanism .ataset, named ., to support research tackling this challenge. CMD 

Endometrium

Balzac’s Allegories of Energy in ,to-image diffusion model presents the potential to resolve this task by employing synthetic image-caption pairs generated by this pre-trained prior. Nonetheless, the defective details in the salient regions of the synthetic images introduce semantic misalignment between the synthetic image and text,

Dorsal-Kyphosis

https://doi.org/10.1007/978-94-011-1946-7 machine-generated segments, integrating them to achieve 3D consistency. In this paper, we propose ClusteringSDF, a novel approach achieving both segmentation and reconstruction in 3D via the neural implicit surface representation, specifically the Signed Distance Function (SDF), where the segmentat

和谐

curettage

https://doi.org/10.1007/978-94-011-0898-0rom a finite vocabulary. To this end, we propose two surprisingly simple modifications to decoder-only transformers: 1) at the input, we replace the finite-vocabulary lookup table with a linear projection of the input vectors; and 2) at the output, we replace the logits prediction (usually mapped to

Catheter

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