Advances in ML have motivated the design of video analytics systems that allow for
structured queries over video datasets. However, existing systems limit query expressivity …

We demonstrate Vaas, a video analytics system for largescale datasets. Vaas provides an
interactive interface to rapidly develop and experiment with different workflows for solving a …

Amidst the advancements in image-based Large Vision-Language Models (image-LVLM),
the transition to video-based models (video-LVLM) is hindered by the limited availability of …

Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many
computer vision tasks. But using them for video monitoring applications incurs high …

We describe VisFlow, a system that efficiently analyzes the feeds from many cameras.
Ubiquitous camera deployments are widely used for security, traffic monitoring, and …

Recent advances in neural networks (NNs) have enabled automatic querying of large
volumes of video data with high accuracy. While these deep NNs can produce accurate …

The availability of vast video collections and the accuracy of ML models has generated
significant interest in video analytics systems. Since naively processing all frames using …

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to
provide query-relevant information in enterprise documents to large language models …

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy
models to accelerate object retrieval and aggregate queries. The key assumption underlying …

This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for
video understanding, that capably understands arbitrary-length video with a constant …