相关论文: ReXCam: Resource-Efficient, Cross-Camera Video Ana…

We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input…

Event cameras provide a number of benefits over traditional cameras, such as the ability to track incredibly fast motions, high dynamic range, and low power consumption. However, their application into computer vision problems, many of…

The problem of video object segmentation can become extremely challenging when multiple instances co-exist. While each instance may exhibit large scale and pose variations, the problem is compounded when instances occlude each other causing…

A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets requires efficient systems for pixel data access and parallel…

Spike cameras, with their exceptional temporal resolution, are revolutionizing high-speed visual applications. Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction…

Millions of network cameras are streaming real-time multimedia content (images or videos) for various environments (e.g., highways and malls) and can be used for a variety of applications. Analyzing the content from many network cameras…

Detection and localization of actions in videos is an important problem in practice. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries because actions often involve a complex…

Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantages but faces…

In the past decades, there has been a significant increase in sensor density and sensor deployment, driven by a significant miniaturization and decrease in size down to the chip level, addressing ubiquitous computing, edge computing, as…

Video Large Language Models (VideoLLMs) have made significant strides in video understanding but struggle with long videos due to the limitations of their backbone LLMs. Existing solutions rely on length extrapolation, which is…

Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipeline, limiting their availability and…

The automotive industry is transitioning from federated, homogeneous, interconnected devices to integrated, heterogeneous, mixed-criticality systems (MCS). This leads to challenges in achieving timing predictability techniques due to access…

This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently achieved outstanding VOS performance by performing non-local…

In recent years, the development of deep learning approaches for the task of person re-identification led to impressive results. However, this comes with a limitation for industrial and practical real-world applications. Firstly, most of…

With advances in optical sensor technology, heterogeneous camera systems are increasingly used for high-resolution (HR) video acquisition and analysis. However, motion transfer across multiple cameras poses challenges. To address this, we…

Long-sequence streaming 3D reconstruction remains a significant open challenge. Existing autoregressive models often fail when processing long sequences because they anchor poses to the first frame, leading to attention decay, scale drift,…

Real-time 3D mapping is a critical component in many important applications today including robotics, AR/VR, and 3D visualization. 3D mapping involves continuously fusing depth maps obtained from depth sensors in phones, robots, and…

Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from…

This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction scenarios, existing methods rely solely on visual…

This paper presents the Nexar Dashcam Collision Prediction Dataset and Challenge, designed to support research in traffic event analysis, collision prediction, and autonomous vehicle safety. The dataset consists of 1,500 annotated video…