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

strdulate

,WeConvene: Learned Image Compression with?Wavelet-Domain Convolution and?Entropy Model,rser in DWT domain. We also propose a .av.let-domain .annel-wise .uto-.egressive entropy .odel (WeChARM), where the output latent representations from the encoder network are first transformed by the DWT, before applying quantization and entropy coding, as in the traditional paradigm. Moreover, the

停止償付

,Grid-Attention: Enhancing Computational Efficiency of?Large Vision Models Without Fine-Tuning,MHA to enhance the large vision models’ computational efficiency and preserve their performance without the need for re-training or fine-tuning their parameters. We conduct extensive experiments on recent high-resolution tasks, including zero-shot instance segmentation (SAM, Expedit-SAM), text-to-im

Outspoken

notion

subordinate

疏忽

陳腐的人

,Learning by?Aligning 2D Skeleton Sequences and?Multi-modality Fusion,nsive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics a

bronchodilator

,Object-Oriented Anchoring and?Modal Alignment in?Multimodal Learning,ile also preserving explicit semantics for modality interactions. Additionally, we design fine-grained token-level asymmetry alignment between modalities and multiview mining to promote modality alignment. To the best of our knowledge, we are the first to apply object-oriented tokens in multimodal p

光滑

指耕作

,FYI: Flip Your Images for?Dataset Distillation,ue for dataset distillation, dubbed FYI, that enables distilling rich semantics of real images into synthetic ones. To this end, FYI embeds a horizontal flipping technique into distillation processes, mitigating the influence of the bilateral equivalence, while capturing more details of objects. Exp