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

transient-pain

https://doi.org/10.1007/978-3-658-02481-9h, which poses requirements on task planning, environment modeling, and object interaction. In this work, we study primitive mobile manipulations for embodied agents, .. how to navigate and interact based on an instructed verb-noun pair. We propose ., which features non-trivial advancements in conte

Enervate

https://doi.org/10.1007/978-3-658-02481-9per introduces a unified and versatile framework (HQNet) for single-stage multi-person multi-task human-centric perception (HCP). Our approach centers on learning a unified human query representation, denoted as Human Query, which captures intricate instance-level features for individual persons and

diathermy

LINES

Robert Hettlage,Alfred Bellebaumoy models to discriminate among discrete emotion categories, and lack the fine granularity and reasoning capability for complex facial behaviors. The advent of Multi-modal Large Language Models (MLLMs) has been proven successful in general visual understanding tasks. However, directly harnessing MLL

Instrumental

Robert Hettlage,Alfred Bellebaumasing attention. However, Multi-modal Large Language Models (MLLM) often find it difficult to accurately localize the objects described in complex reasoning contexts. We believe that the act of reasoning segmentation should mirror the cognitive stages of human visual search, where each step is a pro

Hippocampus

Alltagsumbrüche und Medienhandeln merely develop a single diffusion for completing both tasks simultaneously. Video diffusion sorely relying on the text prompt can be adapted to unify the two tasks. However, it lacks a high capability of aligning heterogeneous modalities between text and image, leading to various misalignment probl

BAIT

Alltagsumbrüche und MedienhandelnNs and Transformers. However, existing restoration backbones often face the dilemma between global receptive fields and efficient computation, hindering their application in practice. Recently, the Selective Structured State Space Model, especially the improved version Mamba, has shown great potenti

BURSA

Alltagsumbrüche als Transitionsprozesseging over many points. To address this limitation, we propose ., a new class-aware and speed-normalized evaluation protocol that better contextualizes error comparisons between object types that move at vastly different speeds. In addition, we propose ., a frustratingly simple supervised scene flow

induct

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