# Learning Attributes Equals Multi-Source Domain Generalization

arXiv: https://arxiv.org/abs/1605.00743 · HTML: https://mlsanity.com/paper/1605.00743/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2016.

## Built on by (most cited first, 30 of 44)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://mlsanity.com/paper/2103.02370/) (2021)
- [Model-Based Domain Generalization](https://mlsanity.com/paper/2102.11436/) (2021)
- [NICO++: Towards Better Benchmarking for Domain Generalization](https://mlsanity.com/paper/2204.08040/) (2022)
- [Semantic-Aware Domain Generalized Segmentation](https://mlsanity.com/paper/2204.00822/) (2022)
- [Elaborative Rehearsal for Zero-shot Action Recognition](https://mlsanity.com/paper/2108.02833/) (2021)
- [Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation](https://mlsanity.com/paper/2108.02376/) (2021)
- [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023)
- [Contrastive Syn-to-Real Generalization](https://mlsanity.com/paper/2104.02290/) (2021)
- [WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation](https://mlsanity.com/paper/2109.14196/) (2021)
- [Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition](https://mlsanity.com/paper/2108.04603/) (2021)
- [MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2110.11264/) (2021)
- [Object Priors for Classifying and Localizing Unseen Actions](https://mlsanity.com/paper/2104.04715/) (2021)
- [Synthetic Sample Selection for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2304.02846/) (2023)
- [CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition](https://mlsanity.com/paper/2101.07042/) (2021)
- [Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification](https://mlsanity.com/paper/2203.15381/) (2022)
- [Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actions](https://mlsanity.com/paper/2106.03158/) (2021)
- [A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction](https://mlsanity.com/paper/2208.10004/) (2022)
- [Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning](https://mlsanity.com/paper/2303.15833/) (2023)
- [Learning Single/Multi-Attribute of Object with Symmetry and Group](https://mlsanity.com/paper/2110.04603/) (2021)
- [Zero-Shot Action Recognition from Diverse Object-Scene Compositions](https://mlsanity.com/paper/2110.13479/) (2021)
- [HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization](https://mlsanity.com/paper/2211.08253/) (2022)
- [ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition](https://mlsanity.com/paper/2401.11654/) (2024)
- [Best-Effort Adaptation](https://mlsanity.com/paper/2305.05816/) (2023)
- [Self-balanced Learning For Domain Generalization](https://mlsanity.com/paper/2108.13597/) (2021)
- [Improving Deep Metric Learning by Divide and Conquer](https://mlsanity.com/paper/2109.04003/) (2021)

## Abstract (arXiv)

Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem---how to accurately and robustly detect attributes from images---has been left under explored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multi-source domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and three different problems.