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- Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes273
The proposed method for detecting and locating anomalous activities in video sequences of crowded scenes is a coupling of anomaly detection with a spatial-temporal Convolutional Neural Networks (CNN), which to the best of the knowledge has not been previously done.
- Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images206
A novel adjacent context coordination network (ACCoNet) to explore the coordination of adjacent features in an encoder–decoder architecture for RSI-SOD, which outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU.
- RGB-T Semantic Segmentation With Location, Activation, and Sharpening205
This paper proposes a novel feature fusion-based network for RGB-T semantic segmentation, named LASNet, which follows three steps of location, activation, and sharpening, and proposes three specific modules for better segmentation.
- The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances165
This survey article presents a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved their capability of them.
- DiffusionAD: Norm-Guided One-Step Denoising Diffusion for Anomaly Detection142
DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network, is introduced and a norm-guided paradigm is proposed to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions.
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- A Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification133
A novel two-stream architecture that combines global-based visual features and object-based location features, so as to improve the feature representation capability of features and achieves competitive accuracy against other state-of-the-art approaches is introduced.
- DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction120
The DiffMOT is the first to introduce a diffusion probabilistic model into the MOT to tackle non-linear motion prediction, and optimizes the diffusion process with much fewer sampling steps.
- Student Network Learning via Evolutionary Knowledge Distillation114
Instead of a fixed pre-trained teacher, an evolutionary teacher is learned online and consistently transfers intermediate knowledge to supervise student network learning on-the-fly to improve the transfer effectiveness of teacher knowledge.
- Detecting Deepfake Videos with Temporal Dropout 3DCNN111
A Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) is proposed to detect deepfake videos and can enhance the representation and generalization ability, avoiding model overfitting and improving detecting accuracy.
- TMSDNet: Transformer with multi‐scale dense network for single and multi‐view 3D reconstruction108
This article proposes a novel framework TMSDNet (transformer with multi‐scale dense network) for single‐view and multi‐view 3D reconstruction with transformer to solve the problem of transformer‐based3D reconstruction.
- Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features85
A novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs) that outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets.
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- Deepfake Video Detection via Predictive Representation Learning64
A predictive representative learning approach termed Latent Pattern Sensing to capture semantic change characteristics for deepfake video detection, which can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective and robust deep fake video detector.
- Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking62
This work proposes to learn Occlusion-Robust Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance of the feature representation of a target with respect to random masking operations modeled by a spatial Cox process.
- Deepfake Video Detection with Spatiotemporal Dropout Transformer60
This paper proposes a simple yet effective patch-level approach to facilitate deepfake video detection via spatiotemporal dropout transformer that reorganizes each input video into bag of patches that is then fed into a vision transformer to achieve robust representation.
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- Multi-Scale CNN Based Garbage Detection of Airborne Hyperspectral Data53
A new hyperspectral image (HSI) classification network MSCNN (Multi-Scale Convolutional Neural Network) is proposed to classify the pixels of HSI data and generate binary garbage segmentation map and the experiment results show that the proposed algorithm has a good performance on garbage detection in large areas.
- Dual Spoof Disentanglement Generation for Face Anti-Spoofing With Depth Uncertainty Learning49
A lightweight Depth Uncertainty Module (DUM), which alleviates the adverse effects of noisy samples by depth uncertainty learning, developed without extra-dependency, thus can be flexibly integrated with any depth supervised network for face anti-spoofing.
- Text detection in scene images based on exhaustive segmentation49
This paper proposes a novel method, which is based on exhaustive segmentation, to detect text in scene images, and argues that the text line grouping problem can be posed as the edge cut of the fully connected graph.
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- Trustable Co-Label Learning From Multiple Noisy Annotators39
This paper proposes a data-efficient approach, called Trustable Co-label Learning (TCL), to learn deep classifiers from multiple noisy annotators when a small set of trusted data is available, which effectively uses trusted data as a guide to generate trustable soft labels (termed co-labels).
- Flexible Auto-Weighted Local-Coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering39
A novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization framework is proposed for clustering high-dimensional data, which improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly.
- Introduction to the Special Issue on Trustworthy Multimedia Computing and Applications in Urban Scenes36
The goal of this special issue (SI) is to encourage a collaborative endeavor to explore the possibilities and obstacles associated with trustworthy multimedia analysis and gather inventive approaches, systems, and applications focused on trustworthy multimedia computing and its applications in urban environments.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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