Recently, Vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patch-divided object regions through an inherent self-attention mechanism. In addition,...
While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species...
On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overl...
Fine-grained action detection remains a challenging and actively studied problem. While previous methods predominantly rely on convolutional neural networks (CNNs), their limited modeling capacity and high computational cost restrict their effectiven...
Vegetable and fruit recognition can be considered as a fine-grained visual categorization (FGVC) task, which is challenging due to the large intraclass variances and small interclass variances. A mainstream direction to address the challenge is to ex...
Aircraft recognition in remote sensing images has long been a meaningful topic. Most related methods treat entire images as a whole and do not concentrate on the features of parts. In fact, a variety of aircraft types have small interclass variance,...
Multi-scale feature fusion techniques and covariance pooling have been shown to have positive implications for completing computer vision tasks, including fine-grained image classification. However, existing algorithms that use multi-scale feature fu...
Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the...
In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combin...
Bird identification is the first step in collecting data on bird diversity and abundance, which also helps research on bird distribution and population measurements. Most research has built end-to-end training models for bird detection task via CNNs...
Smart fisheries, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and image processing, are pivotal in enhancing aquaculture efficiency, sustainability, and resource management by enabling real-tim...
As a long-standing research area, class incremental learning (CIL) aims to effectively learn a unified classifier along with the growth of the number of classes. Due to the small inter-class variances and large intra-class variances, fine-grained vis...
Artificial intelligence research in natural language processing in the context of poetry struggles with the recognition of holistic content such as poetic symbolism, metaphor, and other fine-grained attributes. Given these challenges, multi-modal ima...
The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g.,...
For fine-grained recognition, capturing distinguishable features and effectively utilizing local information play a key role, since the objects of recognition exhibit subtle differences in different subcategories. Finding subtle differences between s...
Recently, causal models have gained significant attention in natural language processing (NLP) and computer vision (CV) due to their capability of capturing features with causal relationships. This study addresses Fine-Grained Visual Categorization (...
Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose cha...
Car fine recognition is a typical scenario for fine-grained image classification, which has great research and application value in both civilian and military fields. However, current research on fine-grained classification is often limited to improv...
Target recognition and fine-grained ship classification in remote sensing face challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This al...
Fine-grained image classification is a challenging computer visual task due to the small interclass variations and large intra-class variations. Extracting expressive feature representation is an effective way to improve the accuracy of fine-grained...
Tongue coating is a crucial diagnostic indicator in traditional Chinese medicine, intuitively reflecting the body’s physiological and pathological conditions. However, traditional visual inspection methods are highly susceptible to subjective b...
Fine-grained aircraft classification in remote sensing is a critical task within the field of remote sensing image processing, aiming to precisely distinguish between different types of aircraft in aerial images. Due to the high visual similarity amo...
The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional net...
Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class vari...
Aiming at the severe occlusion problem and the tiny-scale object problem in the multi-fitting detection task, the Scene Knowledge Integrating Network (SKIN), including the scene filter module (SFM) and scene structure information module (SSIM) is pro...
Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high sim...
Egocentric activity recognition in first-person video (FPV) requires fine-grained matching of the camera wearer’s action and the objects being operated. The traditional method used for third-person action recognition does not suffice because of (1) t...
With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applicat...
The alarming realism of Deepfake presents a significant challenge to digital authenticity, yet its inherent difficulty in synchronizing the emotional cues between facial expressions and speech offers a critical opportunity for detection. However, mos...
The analysis of thin sections for lithology identification is a staple technique in geology. Although recent strides in deep learning have catalyzed the development of models for thin section recognition leveraging varied deep neural networks, there...
Zero-shot action recognition remains challenging due to the visual–semantic gap and the persistent bias toward seen classes, particularly under the generalized setting where both seen and unseen categories appear during inference. To address th...
Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details—all of which are essential for real-time han...
Currently, existing multimodal segmentation methods face limitations in effectively leveraging medical text to guide visual feature learning. They often suffer from insufficient multimodal fusion and inadequate accuracy in fine-grained lesion segment...
The development of effective and comprehensive methods for mapping and monitoring reservoirs is essential for the utilization of water resources and flood control. Remote sensing has the great advantages of broad spatial coverage and regular revisit...
Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle w...
Phytoplankton plays a pivotal role in marine ecosystems and global biogeochemical cycles. Accurate identification and monitoring of phytoplankton are essential for understanding environmental dynamics and climate variations. Despite the significant p...
Micro-expression recognition (MER), as an important branch of intelligent visual sensing, enables the analysis of subtle facial movements for applications in emotion understanding, human–computer interaction and security monitoring. However, ex...
Person re-identification (Re-ID) has attracted considerable attention in the field of computer vision, primarily due to its critical role in video surveillance and public security applications. However, most existing Re-ID approaches rely on image-le...
Accurate object detection in UAV imagery is critical for situational awareness, yet conventional deep learning models often struggle to distinguish between visually similar targets. To address this challenge, this study introduces a hierarchical deep...
Weakly Supervised Video Anomaly Detection (WSVAD) is a critical task in computer vision. It aims to localize and recognize abnormal behaviors using only video-level labels. Without frame-level annotations, it becomes significantly challenging to mode...
The dynamic expressions of emotion convey both the emotional and functional states of an individual’s interactions. Recognizing the emotional states helps us understand human feelings and thoughts. Systems and frameworks designed to recognize h...
Iconic words constitute an integral part of the lexicon of a language, exhibiting form-meaning resemblance. Over the course of time, semantic and phonetic transformations “weaken” the degree of iconicity of a word. This iconicity loss is...
Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a...
1 August 2025
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). Th...
Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading...
Bilinear pooling, as an aggregation approach that outputs second-order statistics of deep learning features, has demonstrated effectiveness in a wide range of visual recognition tasks. Among major improvements on the bilinear pooling, matrix square r...
With the rapid growth of the marine economy and the increasing demand for maritime security, ship target detection has become critically important in both military and civilian applications. However, in complex remote sensing scenarios, challenges su...
Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fi...
Facial expression recognition (FER) is crucial for affective computing and human–computer interaction; however, it is still difficult to achieve under various conditions in the real world, such as lighting, occlusion, and pose. This work presen...
Timely detection of road surface defects such as cracks and potholes is critical for ensuring traffic safety and reducing infrastructure maintenance costs. While recent advances in image-based deep learning techniques have shown promise for automated...
of 2