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

散布

Coronation

0302-9743 e on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as Computer vision, Machine learning, Deep neural networks, Re

BADGE

,Teddy: Efficient Large-Scale Dataset Distillation via?Taylor-Approximated Matching,taset to generalize effectively on real data. Tackling this challenge, as defined, relies on a bi-level optimization algorithm: a novel model is trained in each iteration within a nested loop, with gradients propagated through an unrolled computation graph. However, this approach incurs high memory

confide

,Rethinking and?Improving Visual Prompt Selection for?In-Context Learning Segmentation,xel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting specific objects. However, existing works mostly ignore the value of visual prompts or simply apply similarity sorting to select co

FRET

奇思怪想

TC4D: Trajectory-Conditioned Text-to-4D Generation,presentations, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate—they cannot synthesize motion extending far beyond the bounding box used for volume rendering. The lack of a more flexible motion model contributes to the gap in

CLEFT

,Blind Image Deconvolution by?Generative-Based Kernel Prior and?Initializer via?Latent Encoding,motivated a series of DIP-based approaches, demonstrating remarkable success in BID. However, due to the high non-convexity of the inherent optimization process, these methods are notorious for their sensitivity to the initialized kernel. To alleviate this issue and further improve their performance

你正派

AdvDiff: Generating Unrestricted Adversarial Examples Using Diffusion Models,ms for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often directly inject Projected Gradient Descent (PGD) gradients into the sampling of generative models, which are not theoretically provable and thus generate unrealistic examp

威脅你

Junction