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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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- Fast Propagation Is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks20
This work proposes to exploit the interior building blocks of the model to improve efficiency and achieves better model robustness by dynamically sample lightweight subnetworks as a surrogate model during training.
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- Towards Real World Human Parsing: Multiple-Human Parsing in the Wild.20
A novel Multiple-Human Parser (MH-Parser) is proposed, which considers both the global context and local cues for each person in the parsing process and is demonstrated to outperform the naive "detect-and-parse" approach by a large margin.
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- UniParser: Multi-Human Parsing With Unified Correlation Representation Learning14
By unifying instance-level and category-level output, UniParser circumvents manually designed post-processing techniques and surpasses state-of-the-art methods, achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP.
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- SIGMA-DF: Single-Side Guided Meta-Learning for Deepfake Detection7
A novel method named Single-sIde Guided Meta-leArning framework for DeepFake detection (SIGMA-DF) which simulates the cross-domain scenarios during training by synthesizing virtual testing domain through meta-learning, which achieves state-of-the-art performance on unseen Deepfake data.
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- Latent Thought Flow: Efficient Latent Reasoning in Large Language Models3
Experiments under finetuning and transfer learning settings show that LTF outperforms explicit CoT and latent reasoning baselines, improving accuracy by 9.5% while reducing reasoning length by 27.2% on average compared with strong latent reasoning baselines.
- LaV-CoT: Language-Aware Visual CoT with Multi-Aspect Reward Optimization for Multilingual Text-Centric VQA3
LaV-CoT is introduced, the first Language-aware Visual CoT framework with Multi-Aspect Reward Optimization, which outperforms open-source models of similar size by up to ~9.5% accuracy, even surpassing open-source models more than twice its size, and further exceeding several state-of-the-art proprietary models.
- Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection1
This paper presents the solutions from the Top 3 teams of the Multi-Forgery Detection Challenge, held concurrently with the IEEE Computer Society Workshop on Biometrics at CVPR 2022 to boost the research work in the field of image forgery detection.
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- The SkatingVerse Workshop & Challenge: Methods and Results–
A brief summary of the SkatingVerse Workshop&Challenge is provided including brief introductions to the top three methods including brief introductions to the top three methods.
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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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