# Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach

arXiv: https://arxiv.org/abs/1709.02476 · HTML: https://mlsanity.com/paper/1709.02476/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 27 of 27)

- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.07830/) (2021)
- [Danish Fungi 2020 -- Not Just Another Image Recognition Dataset](https://mlsanity.com/paper/2103.10107/) (2021)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.12389/) (2022)
- [STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay](https://mlsanity.com/paper/2407.15773/) (2024)
- [Deep Image Harmonization by Bridging the Reality Gap](https://mlsanity.com/paper/2103.17104/) (2021)
- [Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries](https://mlsanity.com/paper/2110.02833/) (2021)
- [Fine-Grained Visual Classification using Self Assessment Classifier](https://mlsanity.com/paper/2205.10529/) (2022)
- [The Curious Layperson: Fine-Grained Image Recognition without Expert Labels](https://mlsanity.com/paper/2111.03651/) (2021)
- [A Survey on Visual Transfer Learning using Knowledge Graphs](https://mlsanity.com/paper/2201.11794/) (2022)
- [DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation](https://mlsanity.com/paper/2402.05615/) (2024)
- [Rethinking Generalizable Infrared Small Target Detection: A Real-scene Benchmark and Cross-view Representation Learning](https://mlsanity.com/paper/2504.16487/) (2025)
- [Enhancing Fine-Grained Classification for Low Resolution Images](https://mlsanity.com/paper/2105.00241/) (2021)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)
- [BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention](https://mlsanity.com/paper/2106.02566/) (2021)
- [Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation](https://mlsanity.com/paper/2107.04523/) (2021)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Probabilistic Inverse Modeling: An Application in Hydrology](https://mlsanity.com/paper/2210.06213/) (2022)
- [Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval](https://mlsanity.com/paper/2211.14515/) (2022)
- [Facial Forgery-based Deepfake Detection using Fine-Grained Features](https://mlsanity.com/paper/2310.07028/) (2023)
- [Clone-Robust Weights in Metric Spaces: Handling Redundancy Bias for Benchmark Aggregation](https://mlsanity.com/paper/2502.03576/) (2025)
- [Sparse Pose Trajectory Completion](https://mlsanity.com/paper/2105.00125/) (2021)
- [Learning a Discriminant Latent Space with Neural Discriminant Analysis](https://mlsanity.com/paper/2107.06209/) (2021)
- [HBReID: Harder Batch for Re-identification](https://mlsanity.com/paper/2112.04761/) (2021)
- [Uncertainty Quantification in Inverse Models in Hydrology](https://mlsanity.com/paper/2310.02193/) (2023)
- [Utilizing Graph Generation for Enhanced Domain Adaptive Object Detection](https://mlsanity.com/paper/2406.06535/) (2024)

## Abstract (arXiv)

While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild. These fully supervised models need additional annotated images to classify objects in every new scenario, a task that is infeasible. However, sources such as e-commerce websites and field guides provide annotated images for many classes. In this work, we study fine-grained domain adaptation as a step towards overcoming the dataset shift between easily acquired annotated images and the real world. Adaptation has not been studied in the fine-grained setting where annotations such as attributes could be used to increase performance. Our work uses an attribute based multi-task adaptation loss to increase accuracy from a baseline of 4.1% to 19.1% in the semi-supervised adaptation case. Prior do- main adaptation works have been benchmarked on small datasets such as [46] with a total of 795 images for some domains, or simplistic datasets such as [41] consisting of digits. We perform experiments on a subset of a new challenging fine-grained dataset consisting of 1,095,021 images of 2, 657 car categories drawn from e-commerce web- sites and Google Street View.