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Claim this profileWorks62 from public data
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- Multi-fiber Networks for Video Recognition235
This paper presents the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network, and incorporates multiplexer modules to facilitate information flow between fibers.
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- Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis169
Experimental results show that the proposed Dual-Agent Generative Adversarial Network (DA-GAN) model not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark.
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- DriveWorld: 4D Pre-Trained Scene Understanding via World Models for Autonomous Driving129
This paper proposes a Memory State-Space Model for spatiotemporal modelling, which consists of a Dynamic Memory Bank module for learning temporal-aware latent dynamics to predict future changes and a Static Scene Propagation module for learning spatial-aware latent statics to offer comprehensive scene contexts.
- Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition104
This work proposes a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties, and proposes a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research.
- 3D-Aided Deep Pose-Invariant Face Recognition83
A 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way, and demonstrates superiority of the proposed model over state-of-the-arts.
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- Recursive identification of time-varying systems: Self-tuning and matrix RLS algorithms61
It is shown that the performance of the proposed self-tuning and matrix RLS algorithms compare favorably with two improved RLSgorithms recently proposed in the literature.
- Multistage Object Detection With Group Recursive Learning59
A new EM-like group recursive learning approach to iteratively refine object proposals by incorporating such context of surrounding proposals and provide an optimal spatial configuration of object detections is proposed.
- Multi-Prototype Networks for Unconstrained Set-based Face Recognition52
A novel Multi-Prototype Network (MPNet) model is proposed to learn multiple prototype face representations adaptively from the media sets to demonstrate superiority of the proposed model over state-of-the-arts.
- Happiness level prediction with sequential inputs via multiple regressions50
This paper presents the solution submitted to the Emotion Recognition in the Wild (EmotiW 2016) group-level happiness intensity prediction sub-challenge, which uses a convolutional neural network to extract discriminative face features, and a recurrent neuralnetwork to selectively memorize the important features to perform the group- level happiness prediction task.
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- Robust Face Recognition with Deep Multi-View Representation Learning47
This paper describes the proposed method targeting at the MSR Image Recognition Challenge MS-Celeb-1M, to recognize one million celebrities from their face images captured in the real world, which provides a large scale dataset crawled from the Web, which contains a large number of celebrities with many images for each subject.
- Fine-Grained Multi-human Parsing45
A new large-scale database “Multi-human Parsing (MHP v2.0)” is presented, and a novel deep Nested Adversarial Network (NAN) model for multi-human parsing is proposed, which consistently outperforms existing state-of-the-art solutions on MHP and several other datasets.
- Deep Face Beautification44
A deep face beautification framework, which is able to automatically modify the geometrical structure of a face so as to boost the attractiveness via training the Deep Beauty Predictor (DBP).
- Marginalized CNN: Learning Deep Invariant Representations40
It is proved that mCNN, the marginalized Convolutional Neural Network, can be understood as a classic CNN with a special form of regularization and thus is efficient for implementation and not restricted to the CNN module used.
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- Deep Attribute-preserving Metric Learning for Natural Language Object Retrieval28
A deep Attribute-Preserving Metric learning (AP-Metric) framework that jointly generates unique query-sensitive region proposals and conducts novel cross-modal feature learning that explicitly pursues consistency over semantic attribute abstraction within both domains for deep metric learning is proposed.
- Multi-human Parsing with a Graph-based Generative Adversarial Model27
A novel multi- human parsing model named MH-Parser is introduced, which uses a graph-based generative adversarial model to address the challenges of close-person interaction and occlusion in multi-human parsing.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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