AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Experimental results indicate that the proposed framework provides competitive rate–distortion performance and improves downstream detection and tracking performance in the evaluated settings.
Multimodal large language models (MLLMs) have recently extended from static image understanding to video comprehension, but representing videos as frame-level token sequences incurs substantial computational overhead. Existing visual token compression methods typically rely on uniform sampling or single-dimension redun...
Video-based intelligent applications such as autonomous driving, video understanding, and telemedicine rely heavily on the availability of robust and transferable semantic representations. In practical systems, however, video signals transmitted over wireless links are often exposed to severe and unpredictable distorti...
A context-aware framework that specializes a pretrained semantic decoder to each group of pictures by optimizing only its channel-wise scale factors and transposed-convolution biases, enabling the receiver to reproduce the adapted decoder without full-model retraining is introduced.
We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of relying on heavy 3D architectures or reconstruction-based autoencoders for learning with unlabeled data, we repurpose standard image Vision Transformers by representing v...
Neural video codecs derive motion and temporal contexts mainly from RGB frames, leaving room for cross-modal guidance from complementary temporal observations. Event streams can provide such observations by recording brightness changes between frames. In this work, we propose an Event-guided Neural Video Codec (ENVC) t...
A resolution-flexible decoder framework for hybrid NVRs that preserves the basic representation format of hybrid NVRs and can therefore be applied to different backbones and experiments show that the proposed approach improves reconstruction quality over the corresponding NVR baselines.
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