# Curriculum Learning for Multi-Task Classification of Visual Attributes

arXiv: https://arxiv.org/abs/1708.08728 · HTML: https://mlsanity.com/paper/1708.08728/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2017.

## Built on by (most cited first, 13 of 13)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion](https://mlsanity.com/paper/2312.10692/) (2023)
- [Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent](https://mlsanity.com/paper/2106.06080/) (2021)
- [A Mathematical Model for Curriculum Learning for Parities](https://mlsanity.com/paper/2301.13833/) (2023)
- [Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception](https://mlsanity.com/paper/2312.09812/) (2023)
- [Video-Guided Curriculum Learning for Spoken Video Grounding](https://mlsanity.com/paper/2209.00277/) (2022)
- [Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images](https://mlsanity.com/paper/2110.10381/) (2021)
- [Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning](https://mlsanity.com/paper/2603.22758/) (2026)
- [MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs](https://mlsanity.com/paper/2202.12450/) (2022)
- [Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills](https://mlsanity.com/paper/2303.15349/) (2023)
- [HuCurl: Human-induced Curriculum Discovery](https://mlsanity.com/paper/2307.07412/) (2023)
- [Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning](https://mlsanity.com/paper/2110.03106/) (2021)

## Abstract (arXiv)

Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped based on their correlation so that two groups of strongly and weakly correlated tasks are formed. The two groups of tasks are learned in a curriculum learning setup by transferring the acquired knowledge from the strongly to the weakly correlated. The learning process within each group though, is performed in a multi-task classification setup. The proposed method learns better and converges faster than learning all the tasks in a typical multi-task learning paradigm. We demonstrate the effectiveness of our approach on the publicly available, SoBiR, VIPeR and PETA datasets and report state-of-the-art results across the board.