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

不足的東西

Reiner Wichert,Birgid Eberhardt controllable LiDAR data generation is urgently needed, controlling with text is a common practice, but there is little research in this field. To this end, we propose Text2LiDAR, the first efficient, diverse, and text-controllable LiDAR data generation model. Specifically, we design an equirectangu

Evacuate

Advanced Technologies and Societal Changeploiting the benefits of RGB images in the existing vision-based joint perception and prediction (PnP) networks is limited in the perception stage, we delve into how the explicit utilization of the visual semantics in motion forecasting can enhance its performance. Specifically, this work proposes .

眨眼

Thomas Linner,Bernhard Ellmann,Thomas Bocking-based techniques for image compression and analysis. Previous studies often require training separate codecs to support various bitrate levels, machine tasks, and networks, thus lacking both flexibility and practicality. To address these challenges, we propose a rate-distortion-cognition control

迫擊炮

0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72991-1978-3-031-72992-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Anal-Canal

0302-9743 ce 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; r

CAMP

Conference proceedings 2025uter 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; reinforceme

STAT

https://doi.org/10.1007/1-4020-2597-1o capture?the contextual information beyond one single sentence. We further prompt the LLM to generate timestamps for each produced caption based?on the timestamps of the subtitles and finally align the generated captions to the video temporally. In this way, we obtain human-style video captions at