arXiv Computer Vision
Efficient Multi-modal Video Fusion Network with Dynamic Region-Guided Temporal Interaction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jul 16
ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding
arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.
arXiv Computer Vision
1d ago
Temporal Slice Learning for AI-Generated Video Detection with 400$\times$ Fewer FLOPs
arXiv:2610.35148v1 Announce Type: new Abstract: Recent advances in generative video models have improved visual realism, making detection increasingly dependent on subtle temporal inconsistencies. Ho...
arXiv AI
Jun 2
Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection
arXiv:2605. 20301v2 Announce Type: replace-cross Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making.
arXiv AI
Jun 30
CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detection
arXiv:2606. 29136v1 Announce Type: cross Abstract: Event cameras capture sparse brightness changes with high temporal resolution and high dynamic range, compensating for the deficiencies of the conventional RGB frames.
arXiv AI
Jun 12
DIMOS: Disentangling Instance-level Moving Object Segmentation
arXiv:2606. 12826v1 Announce Type: cross Abstract: Moving instance segmentation (MIS) attracts increasing attention due to its broad applications in traffic surveillance, autonomous driving, and animal tracking.
arXiv AI
Jun 8
Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization
arXiv:2606. 07033v1 Announce Type: new Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events, including categories unseen during training.