Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training

• URL: http://arxiv.org/abs/2107.05736v1
• Date: Thu, 8 Jul 2021 04:28:21 GMT
• Title: Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training
• Authors: Darshan Gera, S Balasubramanian
• Abstract summary: This report presents Consensual Collaborative Training (CCT) framework used in our submission to the Affective Behaviour Analysis in-the-wild (ABAW) 2021 competition. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss. Co-training reduces overall error, and consistency loss prevents overfitting to noisy samples.
• Score: 2.538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial expression recognition (FER) in the wild is crucial for building reliable human-computer interactive systems. However, annotations of large scale datasets in FER has been a key challenge as these datasets suffer from noise due to various factors like crowd sourcing, subjectivity of annotators, poor quality of images, automatic labelling based on key word search etc. Such noisy annotations impede the performance of FER due to the memorization ability of deep networks. During early learning stage, deep networks fit on clean data. Then, eventually, they start overfitting on noisy labels due to their memorization ability, which limits FER performance. This report presents Consensual Collaborative Training (CCT) framework used in our submission to expression recognition track of the Affective Behaviour Analysis in-the-wild (ABAW) 2021 competition. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss, without making any assumption about the noise distribution. A dynamic transition mechanism is used to move from supervision loss in early learning to consistency loss for consensus of predictions among networks in the later stage. Co-training reduces overall error, and consistency loss prevents overfitting to noisy samples. The performance of the model is validated on challenging Aff-Wild2 dataset for categorical expression classification. Our code is made publicly available at https://github.com/1980x/ABAW2021DMACS.

Related papers

• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation [6.738522094694818]
  Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data. We propose a novel Cross-head mutual mean-teaching Network (CMMT-Net) incorporated strong-weak data augmentation.
  arXiv  Detail & Related papers  (2023-10-08T09:13:04Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition [3.823356975862006]
  We propose a dynamic FER learning framework (DNFER) to handle noisy annotations. Specifically, DNFER is based on supervised training using selected clean samples and unsupervised consistent training using all the samples. We demonstrate the robustness of DNFER on both synthetic as well as on real noisy annotated FER datasets like RAFDB, FERPlus, SFEW and AffectNet.
  arXiv  Detail & Related papers  (2022-08-22T12:02:41Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Synergistic Network Learning and Label Correction for Noise-robust Image Classification [28.27739181560233]
  Deep Neural Networks (DNNs) tend to overfit training label noise, resulting in poorer model performance in practice. We propose a robust label correction framework combining the ideas of small loss selection and noise correction. We demonstrate our method on both synthetic and real-world datasets with different noise types and rates.
  arXiv  Detail & Related papers  (2022-02-27T23:06:31Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Consistency Regularization Can Improve Robustness to Label Noise [4.340338299803562]
  This paper empirically studies the relevance of consistency regularization for training-time robustness to noisy labels. We show that a simple loss function that encourages consistency improves the robustness of the models to label noise.
  arXiv  Detail & Related papers  (2021-10-04T08:15:08Z)
• Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations [2.538209532048867]
  This work proposes an effective training strategy in the presence of noisy labels, called as Consensual Collaborative Training (CCT) framework. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss. State-of-the-art performance is reported on the benchmark FER datasets RAFDB (90.84%), FERPlus (89.99%) and AffectNet (66%)
  arXiv  Detail & Related papers  (2021-07-10T03:37:06Z)
• Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise [85.59576523297568]
  We consider a one-hidden-layer leaky ReLU network of arbitrary width trained by gradient descent. We prove that SGD produces neural networks that have classification accuracy competitive with that of the best halfspace over the distribution.
  arXiv  Detail & Related papers  (2021-01-04T18:32:49Z)
• Suppressing Uncertainties for Large-Scale Facial Expression Recognition [81.51495681011404]
  This paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with textbf88.14% on RAF-DB, textbf60.23% on AffectNet, and textbf89.35% on FERPlus.
  arXiv  Detail & Related papers  (2020-02-24T17:24:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.