An Analysis of Multi-Task Architectures for the Hierarchic Multi-Label Problem of Vehicle Model and Make Classification

• URL: http://arxiv.org/abs/2603.01746v1
• Date: Mon, 02 Mar 2026 11:17:32 GMT
• Title: An Analysis of Multi-Task Architectures for the Hierarchic Multi-Label Problem of Vehicle Model and Make Classification
• Authors: Alexandru Manole, Laura Diosan,
• Abstract summary: We analyze the advantages and limitations of multi-task learning in a hierarchical multi-label classification problem.<n>Considering both parallel and cascaded multi-task architectures, we evaluate their impact on different Deep Learning classifiers.<n>We observe the effectiveness of the multi-task paradigm on both datasets, improving the performance of the investigated CNN in almost all scenarios.
• Score: 46.03321798937855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most information in our world is organized hierarchically; however, many Deep Learning approaches do not leverage this semantically rich structure. Research suggests that human learning benefits from exploiting the hierarchical structure of information, and intelligent models could similarly take advantage of this through multi-task learning. In this work, we analyze the advantages and limitations of multi-task learning in a hierarchical multi-label classification problem: car make and model classification. Considering both parallel and cascaded multi-task architectures, we evaluate their impact on different Deep Learning classifiers (CNNs, Transformers) while varying key factors such as dropout rate and loss weighting to gain deeper insight into the effectiveness of this approach. The tests are conducted on two established benchmarks: StanfordCars and CompCars. We observe the effectiveness of the multi-task paradigm on both datasets, improving the performance of the investigated CNN in almost all scenarios. Furthermore, the approach yields significant improvements on the CompCars dataset for both types of models.

Related papers

• A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset [44.94304541427113]
  We propose a multitask deep learning model to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-23T11:14:54Z)
• Investigating Self-Supervised Methods for Label-Efficient Learning [27.029542823306866]
  We study different self supervised pretext tasks, namely contrastive learning, clustering, and masked image modelling for their low-shot capabilities. We introduce a framework involving both mask image modelling and clustering as pretext tasks, which performs better across all low-shot downstream tasks. When testing the model on full scale datasets, we show performance gains in multi-class classification, multi-label classification and semantic segmentation.
  arXiv  Detail & Related papers  (2024-06-25T10:56:03Z)
• Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion [86.6191592951269]
  Merging models fine-tuned from common extensively pretrained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multitask model that performs well across diverse tasks. We propose the CONtinuous relaxation dis (Concrete) subspace learning method to identify a common lowdimensional subspace and utilize its shared information track interference problem without sacrificing performance.
  arXiv  Detail & Related papers  (2023-12-11T07:24:54Z)
• How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model [47.617093812158366]
  We introduce the Random Hierarchy Model: a family of synthetic tasks inspired by the hierarchical structure of language and images. We find that deep networks learn the task by developing internal representations invariant to exchanging equivalent groups. Our results indicate how deep networks overcome the curse of dimensionality by building invariant representations.
  arXiv  Detail & Related papers  (2023-07-05T09:11:09Z)
• Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification [68.19713459228369]
  We compare transfer learning, meta-learning and contrastive learning against reference Machine Learning (ML) tree-based and monolithic DL models. We show that (i) using large datasets we can obtain more general representations, (ii) contrastive learning is the best methodology. While ML tree-based cannot handle large tasks but fits well small tasks, by means of reusing learned representations, DL methods are reaching tree-based models performance also for small tasks.
  arXiv  Detail & Related papers  (2023-05-21T11:20:49Z)
• Learning Transferable Adversarial Robust Representations via Multi-view Consistency [57.73073964318167]
  We propose a novel meta-adversarial multi-view representation learning framework with dual encoders. We demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains.
  arXiv  Detail & Related papers  (2022-10-19T11:48:01Z)
• Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning [0.8883733362171032]
  We study whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance.
  arXiv  Detail & Related papers  (2021-11-15T12:31:31Z)
• Multi-Robot Deep Reinforcement Learning for Mobile Navigation [82.62621210336881]
  We propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt) At training time, HInt learns separate perception and dynamics models, and at test time, HInt integrates the two models in a hierarchical manner and plans actions with the integrated model. Our mobile navigation experiments show that HInt outperforms conventional hierarchical policies and single-source approaches.
  arXiv  Detail & Related papers  (2021-06-24T19:07:40Z)
• Sequential Sentence Classification in Research Papers using Cross-Domain Multi-Task Learning [4.2443814047515716]
  We propose a uniform deep learning architecture and multi-task learning to improve sequential sentence classification in scientific texts across domains. Our approach outperforms the state of the art on three benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-11T13:54:10Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Adaptive Hierarchical Decomposition of Large Deep Networks [4.272649614101117]
  As datasets get larger, a natural question is if existing deep learning architectures can be extended to handle the 50+K classes thought to be perceptible by a typical human. This paper introduces a framework that automatically analyzes and configures a family of smaller deep networks as a replacement to a singular, larger network. The resulting smaller networks are highly scalable, parallel and more practical to train, and achieve higher classification accuracy.
  arXiv  Detail & Related papers  (2020-07-17T21:04:50Z)
• Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
  Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint. We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
  arXiv  Detail & Related papers  (2020-04-28T09:15:50Z)

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.