Video Is Worth a Thousand Images: Exploring the Latest Trends in Long Video Generation

An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-the-art system, is still limited to producing videos that are up to one minute in length. This limitation stems from the complexity of long video generation, which requires more than generative AI techniques for approximating density functions essential aspects such as planning, story development, and maintaining spatial and temporal consistency present additional hurdles. Integrating generative AI with a divide-and-conquer approach could improve scalability for longer videos while offering greater control. In this survey, we examine the current landscape of long video generation, covering foundational techniques like GANs and diffusion models, video generation strategies, large-scale training datasets, quality metrics for evaluating long videos, and future research areas to address the limitations of the existing video generation capabilities. We believe it would serve as a comprehensive foundation, offering extensive information to guide future advancements and research in the field of long video generation.

VizInspect Pro -- Automated Optical Inspection (AOI) solution

Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregated insights. The lack of flexibility in these systems presents a unique opportunity for a deep learning based AOI system specifically for factory automation. The proposed solution, VizInspect pro is a generic computer vision based AOI solution built on top of Leo - An edge AI platform. Innovative features that overcome challenges of traditional vision systems include deep learning based image analysis which combines the power of self-learning with high speed and accuracy, an intuitive user interface to configure inspection profiles in minutes without ML or vision expertise and the ability to solve complex inspection challenges while being tolerant to deviations and unpredictable defects. This solution has been validated by multiple external enterprise customers with confirmed value propositions. In this paper we show you how this solution and platform solved problems around model development, deployment, scaling multiple inferences and visualizations.

Visual anomaly detection in video by variational autoencoder

Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security, self-driving cars and crop monitoring. Videos can capture a variety of anomalies. Due to efforts needed to label training data, unsupervised approaches to train anomaly detection models for videos is more practical An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. Variational autoencoder uses distribution (mean and variance) as compared to latent vector as bottleneck layer and can have better regularization effect. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder