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Claim this profileWorks165 from public data
- 1,597
- A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition369
The effectiveness and advantage of the proposed BiHDM model in solving the EEG emotion recognition problem are demonstrated and the important brain areas in emotion expression are investigated.
- MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition336
A multi-modal physiological emotion database is designed and built, which collects four modal physiological signals, i.e., electroencephalogram (EEG), galvanic skin response, respiration, and electrocardiogram (ECG), and a novel attention-long short-term memory (A-LSTM), which strengthens the effectiveness of useful sequences to extract more discriminative features.
- Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation331
A novel Pattern-Affinitive Propagation framework to jointly predict depth, surface normal and semantic segmentation and achieves the state-of-the-art or competitive results on the three related datasets, NYUD-v2, SUN-RGBD and KITTI.
- Deep Network Cascade for Image Super-resolution264
Experiments on image super-resolution demonstrate that the proposed DNC can gradually upscale a low-resolution image with the increase of network layers and achieve more promising results in visual quality as well as quantitative performance.
- Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation237
A novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks and propagate previous learning experiences on both tasks into the next network evolution by explicitly concatenating previous responses.
- Learning Normal Dynamics in Videos with Meta Prototype Network222
This work proposes a dynamic prototype unit (DPU) to encode the normal dynamics as prototypes in real time, free from extra memory cost, and introduces meta-learning to the authors' DPU to form a novel few-shot normalcy learner, namely Meta-Prototype Unit (MPU).
- 207
- Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection185
This work proposes a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD and demonstrates the effectiveness of this framework on benchmarks UCF-Crime and TAD.
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- 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.
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- Instance-Adaptive Graph for EEG Emotion Recognition120
A novel instance-adaptive graph method (IAG), which employs a more flexible way to construct graphic connections so as to present different graphic representations determined by different input instances, which achieves the state-of-the-art performance.
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- Cross-Database Micro-Expression Recognition: A Benchmark78
A CDMER experimental evaluation protocol is established and a standard platform for evaluating their proposed methods is provided and extensive benchmark experiments are conducted by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating the CDMER problem from two different perspectives.
- Consistent Instance False Positive Improves Fairness in Face Recognition75
A false positive rate penalty loss, which mitigates face recognition bias by increasing the consistency of instance False Positive Rate (FPR), and which requires no demographic annotations to be previously predefined during training.
- Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance73
A new unsupervised domain adaptation method called domain adaptation by shifting covariance (DASC) by utilizing the principal components of each covariance matrix for object recognition without requiring any labeled samples from the target domain is proposed.
- A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting71
The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.
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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