Infrared small target detection (ISTD) has been a critical technology in various civilian and industrial applications over the past several decades, such as civilian patrol missions aboard UAVs or shipboard systems, and industrial inspection tasks li...
This paper presents a new method for semantic parsing with upper ontologies using FrameNet annotations and BERT-based sentence context distributed representations. The proposed method leverages WordNet upper ontology mapping and PropBank-style semant...
In spatio-temporal predictive coding problems, like next-frame prediction in video, determining the content of plausible future frames is primarily based on the image dynamics of previous frames. We establish an alternative approach based on their un...
Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs how popular and trending posts framed semantically and emotionally COVID-19 vaccines on Twitter....
Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-base...
Video anomaly detection has two very different answers to the question of how a detector should acquire its notion of “normal” for a given camera: adapt its parameters to hours of normal footage recorded by that exact camera, or perform n...
This paper proposes a semantically oriented knowledge-representation method for transforming primary cyber-event data into structured semantic knowledge to support explainable reasoning in cybersecurity decision-making. The core contribution is a tra...
This paper aims at exploring how conservative and liberal newspapers in South Korea framed PyeongChang 2018 directly. Our research questions addressed four points: first, different attitudes of conservative and liberal newspapers in the PyeongChang n...
Since the referendum in 2016, Brexit has become the most controversial conundrum in the UK. This study aimed to revisit this issue by focusing on the communicative patterns of Brexit-related parties (the Conservatives, Labour, and UK Independence Par...
To autonomously move and operate objects in cluttered indoor environments, a service robot requires the ability of 3D scene perception. Though 3D object detection can provide an object-level environmental description to fill this gap, a robot always...
Semantic segmentation often suffers from class imbalance, where the label ratio for each class in the dataset is not uniform. Recent studies have addressed the issue of class imbalance in semantic segmentation by leveraging the neural collapse phenom...
In GNSS-denied environments, UAV visual positioning faces the critical bottleneck of low matching accuracy between heterogeneous images. To address this, we propose SemGeoFrame, a visual matching framework that leverages surface semantic information...
Reliable semantic perception is crucial for service robots operating in complex public indoor environments. However, existing semantic mapping approaches often face the dual challenges of high computational overhead and semantic redundancy in maps. T...
Road defect detection is essential for traffic safety and infrastructure maintenance. Excising automated methods based on 2D image analysis lack spatial context and cannot provide accurate 3D localization required for maintenance planning. We propose...
Building information models (BIMs) serve as a foundational tool for digital management of existing structures. Traditional methods suffer from low automation and heavy reliance on manual intervention. This paper proposes an automated method for struc...
Wing icing alters the aerodynamic shape of lifting surfaces and threatens aviation safety. Existing icing-image datasets come mainly from wind tunnels or simulations, and image-level random splits overestimate generalisation for video data. We build...
The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background su...
Loop closure detection is a crucial part of VSLAM. However, the traditional loop closure detection algorithms are difficult to adapt to complex and changeable scenes. In this paper, we fuse Gist features, semantic features and appearance features of...
The shot-type decision is a very important pre-task in movie analysis due to the vast information, such as the emotion, psychology of the characters, and space information, from the shot type chosen. In order to analyze a variety of movies, a techniq...
Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping sys...
Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscop...
Given a video sequence acquired from a fixed camera, the stationary background generation problem consists of generating a unique image estimating the stationary background of the sequence. During the IEEE Scene Background Modeling Contest (SBMC) org...
Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is large...
Modern web and tourist maps use pictorial symbols to help users quickly and easily identify Points of Interest (POIs). Pictorial symbols are sometimes misinterpreted due to poor design choices. As a result, it is important to evaluate pictorial symbo...
In this paper, we propose a semantic segmentation-based static video stitching method to reduce parallax and misalignment distortion for sports stadium scenes with dynamic foreground objects. First, video frame pairs for stitching are divided into se...
Intelligent drones or flying robots play a significant role in serving our society in applications such as rescue, inspection, agriculture, etc. Understanding the scene of the surroundings is an essential capability for further autonomous tasks. Intu...
Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardio...
The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which...
This paper summarizes the OpenEDS 2020 Challenge dataset, the proposed baselines, and results obtained by the top three winners of each competition: (1) Gaze prediction Challenge, with the goal of predicting the gaze vector 1 to 5 frames into the fut...
We consider restricted forms of the algorithmic problem of definability of first-order sentences by propositional formulas with intuitionistic Kripke frames semantics. We demonstrate positive resolutions for classes of intuitionistic Kripke frames ba...
Weakly supervised temporal language grounding (TLG) aims to locate events in untrimmed videos based on natural language queries without temporal annotations, necessitating a deep understanding of semantic context across both video and text modalities...
The core challenge in video-text retrieval lies in measuring the cross-modal gap between visual and linguistic representations. While mainstream methods leverage deep learning to project video and text features into a shared space for similarity matc...
Recent advances in object tracking based on deep Siamese networks shifted the attention away from correlation filters. However, the Siamese network alone does not have as high accuracy as state-of-the-art correlation filter-based trackers, whereas co...
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the st...
Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud s...
EcoLexicon is a terminological knowledge base on environmental science, whose design permits the geographic contextualization of data. For the geographic contextualization of landform concepts, this paper presents a semi-automatic method for extracti...
Online semantic Gaussian SLAM typically updates three-dimensional semantic maps using frame-wise predictions from two-dimensional semantic segmentation networks. However, occlusions, viewpoint changes, and object boundaries may introduce transient mi...
Visual–language tracking often faces challenges like target deformation and confusion caused by similar objects. These issues can disrupt the alignment between visual inputs and their textual descriptions, leading to cross-modal semantic drift...
Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware re...
The foreground segmentation method is a crucial first step for many video analysis methods such as action recognition and object tracking. In the past five years, convolutional neural network based foreground segmentation methods have made a great br...
Three-dimensional semantic segmentation plays a crucial role in accurately identifying terrain features and objects by effectively extracting 3D spatial information from the environment. However, the inherent sparsity of point clouds and unclear terr...
This paper introduces ‘SkeletonCLIP++’, an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, ‘Weighted Frame Integration&rsqu...
In order to enhance the matting performance in multi-person dynamic scenarios, we introduce a robust, real-time, high-resolution, and controllable human video matting method that achieves state of the art on all metrics. Unlike most existing methods...
Reconstructing animatable humans, together with their surrounding static environments, from monocular, motion-blurred videos is still challenging for current neural rendering methods. Existing monocular human reconstruction approaches achieve impress...
Video question answering (QA) aims to understand the video scene and underlying plot by answering video questions. An algorithm that can competently cope with this task needs to be able to: (1) collect multi-modal information scattered in the video f...
In the energy industry, like industrial monitoring scenarios, using generative AI for video captioning technology is crucial in event understanding and safety analysis. Current approaches typically rely on a single language model to decode visual sem...
Product recognition in e-commerce live streaming is hindered by rapid viewpoint changes, occlusions, motion blur, and inconsistencies between visual and spoken information. Existing approaches typically focus on individual components such as detectio...
Temporal action localization aims to identify the boundaries of the action of interest in a video. Most existing methods take a two-stage approach: first, identify a set of action proposals; then, based on this set, determine the accurate temporal lo...
In this paper, first, we delved into the experiment by comparing various attention mechanisms in the semantic pixel-wise segmentation framework to perform frame-level transcription tasks. Second, the Viterbi algorithm was utilized by transferring the...
Imaging sonar is a crucial tool for underwater visual perception. Compared to 2D sonar images, 3D sonar images offer superior spatial positioning capabilities, although the data acquisition cost is higher and lacks open source references for data ann...
of 12