Advanced Scene Understanding Methods and Applications in Multi-Modal Data
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 675
Special Issue Editors
Interests: autonomous driving; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; deep learning; remote sensing
Special Issue Information
Dear Colleagues,
Deep-learning-based intelligent algorithms have demonstrated remarkable versatility and prowess across various domain-specific tasks such as remote sensing and automatic driving. Despite these significant achievements, existing unimodal models still exhibit limitations in meeting the diverse requirements of daily applications. This has spurred researchers to delve into the field of multimodal data pattern recognition, where models exemplified by Clip have significantly enhanced multimodal scene understanding capabilities. More recently developed Large Multimodal Models (LMMs) such as Gemini (Google) and Sora (OpenAI) further showcase powerful abilities in comprehending or creating realistic and imaginative videos. Although deep-learning-based multimodal algorithms have garnered widespread attention, they face numerous challenges when processing dynamic visual scenes.
These include the following: integrating and aligning multimodal information (e.g., video, audio, 3D data, temporal series data), addressing domain shift issues, handling noisy data and labeling defects, and discovering novel objects or patterns. Furthermore, infusing temporal consistency and coherence properties into these algorithms poses a significant challenge for understanding multimodal scenes. This special session aims to provide a platform for researchers to share the latest advances in multimodal model theories, methodologies, and applications. We also cordially invite submissions exploring the potential of multimodal data in enhancing the diversity and inclusivity of scene-understanding.
- Visual, LiDAR, and radar perception 2D/3D object detection and 2D/3D object tracking;
- Remote-sensing-related tasks;
- Temporal series data prediction/classification;
- Domain adaption for classification/detection/segmentation;
- Scene parsing, semantic segmentation, instance segmentation, and panoptic segmentation;
- Human-centric visual understanding, human–human/object interaction and understanding, human activity understanding, and human intention modeling;
- Person re-identification, pose estimation, and part-parsing;
- New benchmark datasets and survey papers related to these topics.
We look forward to receiving your contributions.
Dr. Xiankai Lu
Dr. Yiyou Guo
Dr. Jinsheng Ji
Guest Editors
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Keywords
- multimodal data representation and modeling
- large multimodal models and applications
- multimodal scene understanding and inference
- multimodal data alignment and fusion
- pattern recognition based on temporal series data and text–video/text–image
- object segmentation, detection, and recognition based on 2D, 3D, and ego-exocentric video data
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