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- CenterNet: Keypoint Triplets for Object Detection3,587
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- CenterNet: Object Detection with Keypoint Triplets3,563
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics1,885
This work presents a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process and conducts a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celebrity-DF.
- UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking799
This work performs a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- Multi-Scale Structure-Aware Network for Human Pose Estimation300
A robust multi-scale structure-aware neural network for human pose estimation that effectively improves state-of-the-art pose estimation methods that suffer from difficulties in scale varieties, occlusions, and complex multi-person scenarios.
- A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction241
A convolution LSTM network model based on MTS prediction with two‐stage attention that is not only suitable for single‐step prediction of MTS, but also suitable for multistep prediction of time step in a certain range.
- Celeb-DF: A New Dataset for DeepFake Forensics214
A new DeepFake dataset is presented, Celeb-DF, for the development and evaluation of DeepFake detection algorithms, generated using a refined synthesis algorithm that reduces the visual artifacts observed in existing datasets.
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- One-Class Classification with Extreme Learning Machine145
The experimental evaluation shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one- class classification methods.
- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
- Exposing GAN-synthesized Faces Using Landmark Locations127
This work describes a new method to expose GAN-synthesized images using the locations of the facial landmark points, based on the observations that the facial parts configuration generated by GAN models are different from those of the real faces, due to the lack of global constraints.
- Corner Proposal Network for Anchor-Free, Two-Stage Object Detection121
A novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage is proposed, which is competitive among state-of-the-art object detection methods.
- CenterNet++ for Object Detection112
It is demonstrated that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates and a real-time CenterNet model, an anchor-free detector that achieves a good trade-off between accuracy and speed.
- The importance of short lag-time in the runoff forecasting model based on long short-term memory98
The experimental results demonstrate that, compared with state-of-art benchmark models, SA-LSTM achieves the best performance and can be attributed to its exploitation of information in short lag-time.
- A novel loss function of deep learning in wind speed forecasting97
A kernel MSE loss function to evaluate the ubiquitous nonlinearity of deep learning errors in the reproducing kernel Hilbert space is proposed and the results imply that developing a loss function with kernel skills is a new way to get better results.
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- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- Multi-Camera Multi-Target Tracking with Space-Time-View Hyper-graph66
A new multi-camera multi-target tracking method based on a space-time-view hyper-graph that encodes higher-order constraints on 3D geometry, appearance, motion continuity, and trajectory smoothness among 2D tracklets within and across different camera views is proposed.
- DETRAC: A New Benchmark and Protocol for Multi-Object Tracking63
This work performs a comprehensive quantitative study on the effect of object detection accuracy to the overall MOT performance based on a new large-scale DETection and tRACking (DETRAC) benchmark dataset.
- Geometric Hypergraph Learning for Visual Tracking58
A geometric hypergraph learning-based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in different frames, and a confidence-aware sampling method is developed to select representative vertices and hyperedges to construct the geometric hyper graph for more robustness and scalability.
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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 2026-10-14. 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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