# A Challenging Benchmark of Anime Style Recognition

arXiv: https://arxiv.org/abs/2204.14034 · HTML: https://mlsanity.com/paper/2204.14034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs](https://mlsanity.com/paper/2002.10964/) (2020)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Deep Animation Video Interpolation in the Wild](https://mlsanity.com/paper/2104.02495/) (2021)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [Breaking the cycle -- Colleagues are all you need](https://mlsanity.com/paper/1911.10538/) (2019)
- [Second-order Non-local Attention Networks for Person Re-identification](https://mlsanity.com/paper/1909.00295/) (2019)
- [Building a Manga Dataset "Manga109" with Annotations for Multimedia Applications](https://mlsanity.com/paper/2005.04425/) (2020)
- [Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning](https://mlsanity.com/paper/1903.02678/) (2019)
- [Assessment of iris recognition reliability for eyes affected by ocular pathologies](https://mlsanity.com/paper/1809.00206/) (2018)
- [ACFD: Asymmetric Cartoon Face Detector](https://mlsanity.com/paper/2007.00899/) (2020)

## Built on by (most cited first, 6 of 6)

- [ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video](https://mlsanity.com/paper/2401.05314/) (2024)
- [ClearGCD: Mitigating Shortcut Learning For Robust Generalized Category Discovery](https://mlsanity.com/paper/2511.22892/) (2025)
- [Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems](https://mlsanity.com/paper/2301.01914/) (2023)
- [MedXplore: Towards Reliable and Unbiased Generalized Category Discovery in Medical Imaging](https://mlsanity.com/paper/2607.27620/) (2026)
- [Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI](https://mlsanity.com/paper/2608.05258/) (2026)
- [Close Shortcut Wins Long: Seeking Diverse and Stable Generators for Data-Free Knowledge Distillation](https://mlsanity.com/paper/2608.22003/) (2026)

## Abstract (arXiv)

Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem. Unlike biometric recognition, such as face recognition, iris recognition, and person re-identification, ASR suffers from a much larger semantic gap but receives less attention. In this paper, we propose a challenging ASR benchmark. Firstly, we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of 190 anime works and each work at least has ten different roles. In addition to the large-scale, LSASRD contains a list of challenging factors, such as complex illuminations, various poses, theatrical colors and exaggerated compositions. Secondly, we design a cross-role protocol to evaluate ASR performance, in which query and gallery images must come from different roles to validate an ASR model is to learn abstract painting style rather than learn discriminative features of roles. Finally, we apply two powerful person re-identification methods, namely, AGW and TransReID, to construct the baseline performance on LSASRD. Surprisingly, the recent transformer model (i.e., TransReID) only acquires a 42.24% mAP on LSASRD. Therefore, we believe that the ASR task of a huge semantic gap deserves deep and long-term research. We will open our dataset and code at https://github.com/nkjcqvcpi/ASR.