Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

• URL: http://arxiv.org/abs/2310.15985v1
• Date: Tue, 24 Oct 2023 16:36:51 GMT
• Title: Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
• Authors: Xin Xing, Zhexiao Xiong, Abby Stylianou, Srikumar Sastry, Liyu Gong, Nathan Jacobs
• Abstract summary: In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with standard multi-class image classification, where the task is predicting a single label from many possible labels for an image.
• Score: 11.489541220229798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel approach to Single-Positive Multi-label Learning. In general multi-label learning, a model learns to predict multiple labels or categories for a single input image. This is in contrast with standard multi-class image classification, where the task is predicting a single label from many possible labels for an image. Single-Positive Multi-label Learning (SPML) specifically considers learning to predict multiple labels when there is only a single annotation per image in the training data. Multi-label learning is in many ways a more realistic task than single-label learning as real-world data often involves instances belonging to multiple categories simultaneously; however, most common computer vision datasets predominantly contain single labels due to the inherent complexity and cost of collecting multiple high quality annotations for each instance. We propose a novel approach called Vision-Language Pseudo-Labeling (VLPL), which uses a vision-language model to suggest strong positive and negative pseudo-labels, and outperforms the current SOTA methods by 5.5% on Pascal VOC, 18.4% on MS-COCO, 15.2% on NUS-WIDE, and 8.4% on CUB-Birds. Our code and data are available at https://github.com/mvrl/VLPL.

Related papers

• Pseudo Labels for Single Positive Multi-Label Learning [0.0]
  Single positive multi-label (SPML) learning is a cost-effective solution, where models are trained on a single positive label per image. In this work, we propose a method to turn single positive data into fully-labeled data: Pseudo Multi-Labels.
  arXiv  Detail & Related papers  (2023-06-01T17:21:42Z)
• Understanding Label Bias in Single Positive Multi-Label Learning [20.09309971112425]
  It is possible to train effective multi-labels using only one positive label per image. Standard benchmarks for SPML are derived from traditional multi-label classification datasets. This work introduces protocols for studying label bias in SPML and provides new empirical results.
  arXiv  Detail & Related papers  (2023-05-24T21:41:08Z)
• Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification [78.15629210659516]
  In this paper, we propose an incomplete multi-view partial multi-label classification network named RANK. We break through the view-level weights inherent in existing methods and propose a quality-aware sub-network to dynamically assign quality scores to each view of each sample. Our model is not only able to handle complete multi-view multi-label datasets, but also works on datasets with missing instances and labels.
  arXiv  Detail & Related papers  (2023-03-30T03:09:25Z)
• PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification [25.451065364433028]
  Multi-label image classification aims to predict all possible labels in an image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. This paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
  arXiv  Detail & Related papers  (2022-08-22T01:23:08Z)
• Open-Vocabulary Multi-Label Classification via Multi-modal Knowledge Transfer [55.885555581039895]
  Multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding. We propose a novel open-vocabulary framework, named multimodal knowledge transfer (MKT) for multi-label classification.
  arXiv  Detail & Related papers  (2022-07-05T08:32:18Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [70.36722026729859]
  We propose a dual-perspective semantic-aware representation blending (DSRB) that blends multi-granularity category-specific semantic representation across different images. The proposed DS consistently outperforms current state-of-the-art algorithms on all proportion label settings.
  arXiv  Detail & Related papers  (2022-05-26T00:33:44Z)
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [86.17081952197788]
  We propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels. Experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors.
  arXiv  Detail & Related papers  (2022-03-04T07:56:16Z)
• Structured Semantic Transfer for Multi-Label Recognition with Partial Labels [85.6967666661044]
  We propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations. Experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-12-21T02:15:01Z)
• Multi-label Iterated Learning for Image Classification with Label Ambiguity [3.5736176624479654]
  We propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions. We show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision.
  arXiv  Detail & Related papers  (2021-11-23T22:10:00Z)
• Multi-Label Learning from Single Positive Labels [37.17676289125165]
  Predicting all applicable labels for a given image is known as multi-label classification. We show that it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.
  arXiv  Detail & Related papers  (2021-06-17T17:58:04Z)

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.