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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書目名稱Computer Vision – ECCV 2024影響因子(影響力)




書目名稱Computer Vision – ECCV 2024影響因子(影響力)學科排名




書目名稱Computer Vision – ECCV 2024網絡公開度




書目名稱Computer Vision – ECCV 2024網絡公開度學科排名




書目名稱Computer Vision – ECCV 2024被引頻次




書目名稱Computer Vision – ECCV 2024被引頻次學科排名




書目名稱Computer Vision – ECCV 2024年度引用




書目名稱Computer Vision – ECCV 2024年度引用學科排名




書目名稱Computer Vision – ECCV 2024讀者反饋




書目名稱Computer Vision – ECCV 2024讀者反饋學科排名

誤傳

,Face-Adapter for?Pre-trained Diffusion Models with?Fine-Grained ID and?Attribute Control,perior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce ., an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-tr

projectile

,WeConvene: Learned Image Compression with?Wavelet-Domain Convolution and?Entropy Model,sform (DWT). However, LIC mainly reduces spatial redundancy in the autoencoder networks and entropy coding, but has not fully removed the frequency-domain correlation explicitly as in DCT or DWT. To leverage the best of both worlds, we propose a surprisingly simple but efficient WeConvene framework,

有助于

,Grid-Attention: Enhancing Computational Efficiency of?Large Vision Models Without Fine-Tuning,he computer vision field. However, the quartic complexity within the transformer’s Multi-Head Attention (MHA) leads to substantial computational costs in these models whose inputs and outputs are high-resolution. Although several prior works attempted to alleviate this challenge, none have successfu

缺乏

,Mitigating Background Shift in?Class-Incremental Semantic Segmentation, achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background weight transfer, which leverages the broad coverage of background in learning new classes by transferring background weight to the new class classifier. Ho

ENDOW

,Relation DETR: Exploring Explicit Position Relation Prior for?Object Detection,e problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. To address this issue, we explore incorporating position relation prior as attention bias to augment object detection, following the verification of its

ENDOW

,BKDSNN: Enhancing the?Performance of?Learning-Based Spiking Neural Networks Training with?Blurred Kls with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low inference latency (.., number of time-step) have emerged recently. Nevertheless, due to the difficulty of deriving precise gradie

arousal

,Agent Attention: On the?Integration of?Softmax and?Linear Attention,l cost restricts its applicability in various scenarios. In this paper, we propose a novel attention paradigm, ., to strike a favorable balance between computational efficiency and representation power. Specifically, the Agent Attention, denoted as a quadruple (.,?.,?.,?.), introduces an additional

Spina-Bifida

,Learning by?Aligning 2D Skeleton Sequences and?Multi-modality Fusion,tions. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps

適宜

,Resolving Scale Ambiguity in?Multi-view 3D Reconstruction Using Dual-Pixel Sensors,iew 3D reconstruction suffers from unknown scale ambiguity unless a reference object of known size is recorded together with the scene, or the camera poses are pre-calibrated. In this paper, we show that multi-view images recorded by a dual-pixel (DP) sensor allow us to automatically resolve the sca