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- When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition334
An extensive evaluation of CNN-based face recognition systems (CNN-FRS) and proposes three CNN architectures which are the first reported architectures trained using LFW data to make the work easily reproducible.
- Ranked List Loss for Deep Metric Learning279
This work presents two limitations of existing ranking-motivated structured losses and proposes a novel ranked list loss to solve both of them and proposes to learn a hypersphere for each class in order to preserve the similarity structure inside it.
- A Multiresolution 3D Morphable Face Model and Fitting Framework248
The Surrey Face Model is presented, a multi-resolution 3D Morphable Model that is made available to the public for non-commercial purposes and a lightweight open-source C++ library designed with simplicity and ease of integration as its foremost goals.
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- DPT: Deformable Patch-based Transformer for Visual Recognition141
A new Deformable Patch (DePatch) module is proposed which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches, which can well preserve the semantics in patches.
- Frankenstein: Learning Deep Face Representations Using Small Data137
This paper proposes a method to generate very large training data sets of synthetic images by compositing real face images in a given data set and shows that this method enables to learn models from as few as 10 000 training images, which perform on par with models trained from 500 000 images.
- Differentiable Automatic Data Augmentation120
Differentiable Automatic Data Augmentation is proposed which dramatically reduces the cost, relaxes the discrete DA policy selection to a differentiable optimization problem via Gumbel-Softmax, and introduces an unbiased gradient estimator, RELAX, leading to an efficient and effective one-pass optimization strategy to learn an efficient and accurate DA policy.
- Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets113
Conv-Adapter, a PET module designed for ConvNets, is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks with comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains.
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- MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection81
This paper proposes a multi-level aggregation architecture via memory bank called MAMBA, which employs two novel operations to eliminate disadvantages of existing methods: light-weight key-set construction which can significantly reduce the computational cost and fine-grained feature-wise updating strategy which enables the method to utilize knowledge from the whole video.
- Adaptive Variance Based Label Distribution Learning for Facial Age Estimation71
An adaptive variance based distribution learning (AVDL) method for facial age estimation that introduces the data-driven optimization framework, meta-learning, to achieve this and can approximate the true age probability distribution more e-ectively.
- Semantic Alignment: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection71
A novel probabilistic model is proposed which introduces a latent variable, i.e. ‘real’ groundtruth which is semantically consistent, to optimize the landmark detection accuracy and achieves state-of-the-art performance.
- OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection69
A novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy, and an efficient one-shot search method to find the optimal path aggregation architecture.
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- Face Recognition Using a Unified 3D Morphable Model56
The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture.
- CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning55
This work proposes a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images, which is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
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- Deep Metric Learning by Online Soft Mining and Class-Aware Attention53
This work proposes a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch, and introduces Class-Aware Attention (CAA) that assigns little attention to abnormal data samples.
- MetaMixUp: Learning Adaptive Interpolation Policy of MixUp With Metalearning52
A metalearning-based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way (learning to learn better) and extensive experiments show that the MetaMixUp adapted SSL greatly outperforms MixUp and many state-of-the-art methods on CIFAR-10 and SVHN benchmarks under the SSL configuration.
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- Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions51
This work applies Convolutional AutoEncoder to learn a stock representation, based on which a novel portfolio construction strategy is proposed, using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors.
- Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States47
This work addresses subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (MTL) methods to effectively leverage a side task: facial landmark detection and achieves very competitive performance on Oulu-Casia NIR&Vis and CK+ databases via transfer learning.
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 2026-10-16. 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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