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

Ambiguous

,Sequential Representation Learning via Static-Dynamic Conditional Disentanglement,to the introduction of a novel theoretically grounded disentanglement constraint that can be directly and efficiently incorporated into our new framework. The experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.

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,Diverse Text-to-3D Synthesis with Augmented Text Embedding,ose to use augmented text prompts via textual inversion of reference images to diversify the joint generation. We show that our method leads to improved diversity in text-to-3D synthesis qualitatively and quantitatively. Project page:

Hdl348

刚毅

,Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Groundedponse to visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consi

断断续续

,Watching it in Dark: A Target-Aware Representation Learning Framework for High-Level Vision Tasks i issue through either image-level enhancement or feature-level adaptation, they often focus solely on the image itself, ignoring how the task-relevant target varies along with different illumination. In this paper, we propose a target-aware representation learning framework designed to improve high-

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mydriatic

,OP-Align: Object-Level and Part-Level Alignment for Self-supervised Category-Level Articulated Objeignificance, this task remains challenging due to the varying shapes and poses of objects, expensive dataset annotation costs, and complex real-world environments. In this paper, we propose a novel self-supervised approach that leverages a single-frame point cloud to solve this task. Our model consi

PAEAN

,BAFFLE: A Baseline of Backpropagation-Free Federated Learning,ising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these clients are typically edge devices and not fully trusted, executing backpropagation on them incurs computational and storage overh

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