Learning Triangular Distribution in Visual World

• URL: http://arxiv.org/abs/2311.18605v3
• Date: Mon, 18 Mar 2024 09:03:05 GMT
• Title: Learning Triangular Distribution in Visual World
• Authors: Ping Chen, Xingpeng Zhang, Chengtao Zhou, Dichao Fan, Peng Tu, Le Zhang, Yanlin Qian,
• Abstract summary: Convolution neural network is successful in pervasive vision tasks, including label distribution learning. We study the mathematical connection between feature and its label, presenting a general and simple framework for label distribution learning. We propose a so-called Triangular Distribution Transform (TDT) to build an injective function between feature and label, guaranteeing that any symmetric feature discrepancy linearly reflects the difference between labels.
• Score: 5.796362696313493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolution neural network is successful in pervasive vision tasks, including label distribution learning, which usually takes the form of learning an injection from the non-linear visual features to the well-defined labels. However, how the discrepancy between features is mapped to the label discrepancy is ambient, and its correctness is not guaranteed.To address these problems, we study the mathematical connection between feature and its label, presenting a general and simple framework for label distribution learning. We propose a so-called Triangular Distribution Transform (TDT) to build an injective function between feature and label, guaranteeing that any symmetric feature discrepancy linearly reflects the difference between labels. The proposed TDT can be used as a plug-in in mainstream backbone networks to address different label distribution learning tasks. Experiments on Facial Age Recognition, Illumination Chromaticity Estimation, and Aesthetics assessment show that TDT achieves on-par or better results than the prior arts.

Related papers

• Online Multi-Label Classification under Noisy and Changing Label Distribution [9.17381554071824]
  We propose an online multi-label classification algorithm under Noisy and Changing Label Distribution (NCLD) The objective is to simultaneously model the label scoring and the label ranking for high accuracy, whose robustness to NCLD benefits from three novel works.
  arXiv  Detail & Related papers  (2024-10-03T11:16:43Z)
• Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment [62.73503467108322]
  This topic is widely studied in 3D point cloud segmentation due to the difficulty of annotating point clouds densely. Until recently, pseudo-labels have been widely employed to facilitate training with limited ground-truth labels. Existing pseudo-labeling approaches could suffer heavily from the noises and variations in unlabelled data. We propose a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2024-08-29T13:31:15Z)
• Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models [55.99654128127689]
  Visual Foundation Models (VFMs) are used to enhance 3D representation learning. VFMs generate semantic labels for weakly-supervised pixel-to-point contrastive distillation. We adapt sampling probabilities of points to address imbalances in spatial distribution and category frequency.
  arXiv  Detail & Related papers  (2024-05-23T07:48:19Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Contrastive Label Enhancement [13.628665406039609]
  We propose Contrastive Label Enhancement (ConLE) to generate high-level features by contrastive learning strategy. We leverage the obtained high-level features to gain label distributions through a welldesigned training strategy.
  arXiv  Detail & Related papers  (2023-05-16T14:53:07Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentation [7.748333539159297]
  We train the model by the pseudo labels which are implicitly produced by itself to learn new complementary knowledge about target domain. Experiments on GTA5 to Cityscapes and SYNTHIA to Cityscapes tasks show that the proposed method has considerable improvements.
  arXiv  Detail & Related papers  (2022-04-14T04:06:22Z)
• Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning [118.45388912229494]
  We propose a Modal-Alternating Propagation Network (MAP-Net) to supplement the absent semantic information of unlabeled samples. We design a Relation Guidance (RG) strategy to guide the visual relation vectors via semantics so that the propagated information is more beneficial. Our proposed method achieves promising performance and outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-09-03T03:43:53Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)

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.