Meta-Learning for Multi-Label Few-Shot Classification
- URL: http://arxiv.org/abs/2110.13494v1
- Date: Tue, 26 Oct 2021 08:47:48 GMT
- Title: Meta-Learning for Multi-Label Few-Shot Classification
- Authors: Christian Simon, Piotr Koniusz, Mehrtash Harandi
- Abstract summary: This work targets the problem of multi-label meta-learning, where a model learns to predict multiple labels within a query.
We introduce a neural module to estimate the label count of a given sample by exploiting the relational inference.
Overall, our thorough experiments suggest that the proposed label-propagation algorithm in conjunction with the neural label count module (NLC) shall be considered as the method of choice.
- Score: 38.222736913855115
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Even with the luxury of having abundant data, multi-label classification is
widely known to be a challenging task to address. This work targets the problem
of multi-label meta-learning, where a model learns to predict multiple labels
within a query (e.g., an image) by just observing a few supporting examples. In
doing so, we first propose a benchmark for Few-Shot Learning (FSL) with
multiple labels per sample. Next, we discuss and extend several solutions
specifically designed to address the conventional and single-label FSL, to work
in the multi-label regime. Lastly, we introduce a neural module to estimate the
label count of a given sample by exploiting the relational inference. We will
show empirically the benefit of the label count module, the label propagation
algorithm, and the extensions of conventional FSL methods on three challenging
datasets, namely MS-COCO, iMaterialist, and Open MIC. Overall, our thorough
experiments suggest that the proposed label-propagation algorithm in
conjunction with the neural label count module (NLC) shall be considered as the
method of choice.
Related papers
- Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification [11.19022605804112]
This paper introduces RR2QC, a novel Retrieval Reranking method To multi-label Question Classification.
It uses label semantics and meta-label refinement to enhance personalized learning and resource recommendation.
Experimental results demonstrate that RR2QC outperforms existing classification methods in Precision@k and F1 scores.
arXiv Detail & Related papers (2024-11-04T06:27:14Z) - Deep Partial Multi-Label Learning with Graph Disambiguation [27.908565535292723]
We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN)
Specifically, we introduce the instance-level and label-level similarities to recover label confidences.
At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
arXiv Detail & Related papers (2023-05-10T04:02:08Z) - Exploring Structured Semantic Prior for Multi Label Recognition with
Incomplete Labels [60.675714333081466]
Multi-label recognition (MLR) with incomplete labels is very challenging.
Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations.
We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
arXiv Detail & Related papers (2023-03-23T12:39:20Z) - Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact
Supervision [53.530957567507365]
In some real-world tasks, each training sample is associated with a candidate label set that contains one ground-truth label and some false positive labels.
In this paper, we formalize such problems as multi-instance partial-label learning (MIPL)
Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems.
arXiv Detail & Related papers (2022-12-18T03:28:51Z) - A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation [42.0958430465578]
We study the partial multi-label (PML) image classification problem.
Existing PML methods typically design a disambiguation strategy to filter out noisy labels.
We propose a deep model for PML to enhance the representation and discrimination ability.
arXiv Detail & Related papers (2022-07-06T02:49:02Z) - 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) - Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint
Localization [88.74813798138466]
Localizing keypoints of an object is a basic visual problem.
Supervised learning of a keypoint localization network often requires a large amount of data.
We propose to automatically select reliable pseudo-labeled samples with a series of dynamic thresholds.
arXiv Detail & Related papers (2022-01-21T09:51:58Z) - Multi-label Few/Zero-shot Learning with Knowledge Aggregated from
Multiple Label Graphs [8.44680447457879]
We present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships.
We show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.
arXiv Detail & Related papers (2020-10-15T01:15:43Z) - SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning [87.27700889147144]
We propose to select a small subset of labels as landmarks which are easy to predict according to input (predictable) and can well recover the other possible labels (representative)
We employ the Alternating Direction Method (ADM) to solve our problem. Empirical studies on real-world datasets show that our method achieves superior classification performance over other state-of-the-art methods.
arXiv Detail & Related papers (2020-08-16T11:07:44Z)
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.