# The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

arXiv: https://arxiv.org/abs/2103.06937 · HTML: https://mlsanity.com/paper/2103.06937/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020)
- [ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring](https://mlsanity.com/paper/1911.09785/) (2019)
- [Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning](https://mlsanity.com/paper/2001.06001/) (2020)
- [Adapting Models to Signal Degradation using Distillation](https://mlsanity.com/paper/1604.00433/) (2016)
- [Semi-Supervised Recognition under a Noisy and Fine-grained Dataset](https://mlsanity.com/paper/2006.10702/) (2020)
- [Reasoning about Fine-grained Attribute Phrases using Reference Games](https://mlsanity.com/paper/1708.08874/) (2017)

## Built on by (most cited first, 21 of 21)

- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (2021)
- [DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2106.05682/) (2021)
- [A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification](https://mlsanity.com/paper/2104.00679/) (2021)
- [Revisiting Consistency Regularization for Semi-Supervised Learning](https://mlsanity.com/paper/2112.05825/) (2021)
- [The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop](https://mlsanity.com/paper/2106.01364/) (2021)
- [Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning](https://mlsanity.com/paper/2406.11148/) (2024)
- [SemiReward: A General Reward Model for Semi-supervised Learning](https://mlsanity.com/paper/2310.03013/) (2023)
- [Prompting Scientific Names for Zero-Shot Species Recognition](https://mlsanity.com/paper/2310.09929/) (2023)
- [Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations](https://mlsanity.com/paper/2305.12715/) (2023)
- [Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning](https://mlsanity.com/paper/2312.12237/) (2023)
- [RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning](https://mlsanity.com/paper/2208.04619/) (2022)
- [Fine-Grained Adversarial Semi-supervised Learning](https://mlsanity.com/paper/2110.05848/) (2021)
- [Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2306.17699/) (2023)
- [Active Learning via Vision-Language Model Adaptation with Open Data](https://mlsanity.com/paper/2506.01724/) (2025)
- [MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning](https://mlsanity.com/paper/2405.06227/) (2024)
- [Decoupled Entropy Minimization](https://mlsanity.com/paper/2511.03256/) (2025)
- [SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes](https://mlsanity.com/paper/2311.01646/) (2023)
- [Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment](https://mlsanity.com/paper/2407.05370/) (2024)
- [Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective](https://mlsanity.com/paper/2512.10244/) (2025)
- [Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors](https://mlsanity.com/paper/2512.15748/) (2025)

## Abstract (arXiv)

This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~citesemi-aves at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150k images. From this collection, we sample a subset of classes and their labels, while adding the images from the remaining classes to the unlabeled set of images. The presence of out-of-domain data (novel classes), high class-imbalance, and fine-grained similarity between classes poses significant challenges for existing semi-supervised recognition techniques in the literature. The dataset is available here: urlhttps://github.com/cvl-umass/semi-inat-2020