Robust Arithmetic Word Problem (AWP) solving is important for applying mathematical reasoning systems in educational scenarios, where problem statements may contain changed numerical values, paraphrased descriptions, or irrelevant distracting informa...
Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing...
Accurate weld seam tracking under severe arc interference remains a major challenge for intelligent robotic welding systems because radiometric saturation and transient visual degradation significantly reduce the reliability of vision-based measureme...
With the development of multimedia systems in wireless environments, the rising need for artificial intelligence is to design a system that can properly communicate with humans with a comprehensive understanding of various types of information in a h...
Vision-based Re-identification (ReID) is crucial for intelligent surveillance yet remains vulnerable to adverse-weather degradations such as rain and fog, which simultaneously corrupt visual clarity and identity-specific cues. Existing image-enhancem...
To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception...
This paper presents an improved approach for scene-aware camera relocalization using RGB images and poses. Building upon the ACE network, we proposed a refined head structure that integrates skip and dense connections alongside channel attention mech...
In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in t...
Recent studies have shown that deep learning achieves excellent performance in reconstructing 3D scenes from multiview images or videos. However, these reconstructions do not provide the identities of objects, and object identification is necessary f...
Developing Arithmetic Word Problem (AWP) -solving algorithms has recently become one of the hottest research areas because it can simultaneously advance general artificial intelligence and the application of AI technology in education. This paper pre...
This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on mu...
Although current scene text detectors achieve remarkable accuracy across different and diverse styles of datasets by fine-tuning models multiple times, these approaches are time-consuming and hinder model generalization. As such, exploring a training...
Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often...
Single-view RGB-D observations are often affected by occlusion and restricted viewpoints, leading to incomplete object geometry and underestimated obstacle extents in indoor robot perception. This paper proposes a single-view scene completion framewo...
Urban sustainability depends on rural hinterlands, yet national-scale evaluation and AI screening often rely on urban-centric proxies, which can under-recognize remote villages where the evidence base is sparse. Using China’s national honored-v...
Due to carbon deposits, lean flames, or damaged metal parts, sparks can occur in aero engine chambers. At present, the detection of such sparks deeply depends on laborious manual work. Considering that interference has the same features as sparks, al...
Zero-shot remote sensing is attractive for scene classification because new regions, sensors, and label taxonomies often appear before sufficient annotated data are available for supervised adaptation. We present OATS-RS, an inference-centric framewo...
This paper introduces a novel and efficient approach for Gaussian Splatting in dynamic scenes that leverages symmetry principles for enhanced computational efficiency and visual fidelity. First, we diverge from conventional methods that process stati...
Mining-area remote sensing scene classification plays an important role in mineral resource monitoring and ecological environment assessment. However, high-accuracy deep learning models usually exhibit complex architectures, large parameter sizes, an...
Reliable optical sensing of smoke and flames in high-rise buildings is challenging due to weak early cues, vertical smoke diffusion, facade occlusions, nighttime illumination, and fire-like urban interferences. We propose CDU-YOLO, a scene-aware real...
The identification of scenes poses a notable challenge within the realm of image processing. Unlike object recognition, which typically involves relatively consistent forms, scene images exhibit a broader spectrum of variability. This research introd...
Texture privacy preservation is a key technique for enabling the secure sharing and compliant use of three-dimensional (3D) urban models. However, maintaining visually continuous textures after privacy-preserving processing remains a challenging task...
21 July 2022
Firefighters are repeatedly exposed to various pollutants that occur at fire scenes. There are three levels of exposure: primary exposure to pollutants, secondary exposure to pollutants on personal protective equipment (PPE), and tertiary exposure to...
Images captured by vision sensors provide visual evidence for scene understanding, including object appearances, pixel-level regions, and spatial relations among entities. Panoptic Scene Graph Generation (PSG) constructs structured scene representati...
Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing f...
While 3D Gaussian Splatting (3DGS) has significantly advanced large-scale 3D reconstruction and novel view synthesis, it still suffers from high memory consumption and slow training speed. To address these issues without compromising reconstruction q...
Cross-scene classification of hyperspectral images poses significant challenges due to the lack of a priori knowledge and the differences in data distribution across scenes. While traditional studies have had limited use of a priori knowledge from ot...
Incorporating external knowledge has been shown to improve emotion understanding in dialogues by enriching contextual information, such as character motivations, psychological states, and causal relations between events. Filtering and categorizing th...
Facial expression recognition (FER) technology has made considerable progress with the rapid development of deep learning. However, conventional FER techniques are mainly designed and trained for videos that are artificially acquired in a limited env...
Cross-Domain Few-Shot Scene Classification (CDFSSC) aims to transfer knowledge from a source domain to a target domain for few-shot classification tasks, and is essential for remote sensing applications involving diverse platforms and dynamic environ...
In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities su...
Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory...
Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view...
Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balan...
Partial indoor point clouds acquired by light detection and ranging (LiDAR) are frequently affected by occlusion, restricted viewpoints, and non-uniform sampling, making it difficult to recover complete scene geometry while preserving the semantics o...
Maritime aerial imaging is strongly affected by rapid illumination variations induced by dynamic sea conditions, which often cause conventional exposure control approaches to misinterpret intrinsic scene brightness as overexposure resulting from elev...
Autonomous driving in dense urban environments requires accurate trajectory forecasting supported by interpretable contextual evidence. This study presents a multimodal framework that performs driving scene context (DSC)-aware trajectory prediction w...
Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gr...
Fusing Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery is challenging when one modality is degraded. This study proposes a reliability-aware cross-modal fusion framework for patch-level land cover scene classification, combin...
3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexi...
Astronomical positioning of space targets is an important task in space situational awareness. In both ground-based and space-based optical observation scenarios, accurate positioning relies on the reliable matching of numerous stars in observational...
Dynamic scene reconstruction from thermal infrared imagery remains insufficiently studied due to several inherent challenges, including low texture, low contrast, and radiometric ambiguity. In this paper, we present Thermal4D, a novel framework for r...
An electronic guidance system is very helpful in improving blind people’s perceptions in a local environment. In our previous work “Lin, Q.; Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile Model. Sensor...
To address the challenge of high false alarm rates caused by dynamic viewpoint noise in mobile chemical inspections, this study established a highly robust adaptive dynamic risk inference model. This research proposes an inference framework integrati...
While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly...
The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We...
Underwater images frequently suffer from color casts, low illumination, and blur due to wavelength-dependent absorption and scattering. We present a practical two-stage, modular, and degradation-aware framework designed for real-time enhancement, pri...
Self-supervised monocular visual odometry (VO) commonly relies on photometric consistency and rigid-scene assumptions to learn camera motion, making pose estimation vulnerable to unreliable cross-frame observations caused by limited geometric observa...
Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and...
High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automa...
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