Visual place recognition (VPR) aims to localize a query image by matching its visual representation against a geotagged database. One major challenge in VPR is to learn place representations that remain robust under appearance changes, viewpoint vari...
Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications...
Visual malware recognition classifies byte-plot images of executables and reports near-perfect accuracy, yet it is rarely evaluated the way a deployed detector would be. We present ByteScope, a framework that adds a cosine-margin family classifier, a...
Automatic speech recognition (ASR) that relies on audio input suffers from significant degradation in noisy conditions and is particularly vulnerable to speech interference. However, video recordings of speech capture both visual and audio signals, p...
Speech recognition approaches typically fall into three categories: audio, visual, and audio–visual. Visual speech recognition, or lip reading, is the most difficult because visual cues are ambiguous and data is scarce. To address these challen...
Speech is a commonly used interaction-recognition technique in edutainment-based systems and is a key technology for smooth educational learning and user–system interaction. However, its application to real environments is limited owing to the...
Visual place recognition (VPR) is crucial for enabling autonomous agents to accurately localize themselves within a known environment. While existing methods leverage neural networks to enhance performance and robustness, they often suffer from the l...
Perception-level interference patterns, such as abutting gratings that induce illusory contours and pseudoisochromatic dot camouflage, can trigger failures that are not well captured by conventional corruption benchmarks. We construct an illusion-dri...
Existing publicly available datasets for insulator recognition primarily focus on overhead transmission lines and are commonly acquired using unmanned aerial vehicles. As a result, they often do not reflect the visual complexity of high-voltage subst...
In real-world scenarios, the facial expression recognition task faces several challenges, including lighting variations, image noise, face occlusion, and other factors, which limit the performance of existing models in dealing with complex situations...
Fine-grained bird identification is crucial for ecosystem monitoring, species conservation, and habitat assessment. However, in real-world environments, there are challenges such as imbalances in modality quality and interference from background nois...
The application of deep learning is blooming in the field of visual place recognition, which plays a critical role in visual Simultaneous Localization and Mapping (vSLAM) applications. The use of convolutional neural networks (CNNs) achieve better pe...
In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur...
Aerial-to-satellite visual localization enables GNSS-denied UAV navigation, but the appearance gap between low-altitude (50–150 m) UAV imagery and nadir satellite tiles makes per-frame Visual Place Recognition (VPR) unreliable. Under perceptual...
Reliable facial expression recognition (FER) is essential for human–computer interaction across scenarios. However, existing methods often overlook spatial structure when seeking region-specific features due to fixed or limited local regions, l...
Feature preprocessing directly shapes the geometry seen by distance- and similarity-based classifiers. This study introduces DART (Discriminative Adaptive Robust Transformation), a supervised preprocessing method combining median/MAD normalization, b...
In the published publication [...]
A major challenge in place recognition is to be robust against viewpoint changes and appearance changes caused by self and environmental variations. Humans achieve this by recognizing objects and their relationships in the scene under different condi...
The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based dee...
Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which l...
Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to un...
Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this fie...
This publication describes an innovative approach to voice control of operational and technical functions in a real Smart Home (SH) environment, where, for voice control within SH, it is necessary to provide robust technological systems for building...
Robust visual place recognition (VPR) enables mobile robots to identify previously visited locations. For this purpose, the extracted visual information and place matching method plays a significant role. In this paper, we critically review the exist...
Our approach to action recognition is grounded in the intrinsic coexistence of and complementary relationship between audio and visual information in videos. Going beyond the traditional emphasis on visual features, we propose a transformer-based net...
Robust speech recognition in real world situations is still an important problem, especially when it is affected by environmental interference factors and conversational multi-speaker interactions. Supplementing audio information with other modalitie...
Recognizing visual artworks is difficult because images of art combine large within-class diversity, ambiguous boundaries, and non-photographic textures with intertwined cues related to the artist, genre, style, and historical period. Standard convol...
With the progress of agricultural modernization, intelligent fruit harvesting is gaining importance. While fruit detection and recognition are essential for robotic harvesting, existing methods suffer from limited generalizability, including adapting...
In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eye...
Ear recognition is an active research area in the biometrics community with the ultimate goal to recognize individuals effectively from ear images. Traditional ear recognition methods based on handcrafted features and conventional machine learning cl...
Deep visual recognition has achieved remarkable success across various domains, including medical imaging, autonomous vehicles, and security systems. However, the black-box nature of deep learning models poses challenges in terms of transparency and...
Lipreading, or visual speech recognition, is the task of interpreting utterances solely from visual cues of lip movements. While early approaches relied on Hidden Markov Models (HMMs) and handcrafted spatiotemporal descriptors, recent advances in dee...
Robotic visual perception and terrain recognition are critical for autonomous locomotion and adaptive control in complex environments. However, existing models often extract weak features, confuse classes, and deliver unstable recognition. Most prior...
Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to i...
Facial expression recognition has wide application prospects in many occasions. Due to the complexity and variability of facial expressions, facial expression recognition has become a very challenging research topic. This paper proposes a Vision Tran...
Curved, blurred, low-contrast, and cluttered scene text remains difficult to localize and transcribe because visual boundaries and character identities are often ambiguous. Most detectors refine spatial regions from visual and positional features, wh...
Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still k...
Robust object detection in complex environments, poor visual conditions, and open scenarios presents significant technical challenges in autonomous driving. These challenges necessitate the development of advanced fusion methods for millimeter-wave (...
GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, v...
Auditory stimuli are known to enhance visual target recognition in rapid serial visual presentation (RSVP) tasks, yet the robustness and potential trade-offs of this audiovisual integration (AVI) effect remain debated. Attribute amnesia (AA) refers t...
This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-langua...
Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone i...
Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize...
To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign tem...
Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a no...
India has an estimated 12 million visually impaired people and is home to the world’s largest number in any country. Smart walking stick devices use various technologies including machine vision and different sensors for improving the safe move...
Automated recognition of identity documents is a critical component in digital identity verification systems. The development of robust recognition models is often constrained by the limited availability of large, diverse, high-quality, and publicly...
The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variat...
Emotion recognition is crucial in understanding human affective states with various applications. Electroencephalography (EEG)—a non-invasive neuroimaging technique that captures brain activity—has gained attention in emotion recognition....
Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances. However, DNN-based visual recognition systems on UAVs show seriou...
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