# Boosting vision transformers for image retrieval

arXiv: https://arxiv.org/abs/2210.11909 · HTML: https://mlsanity.com/paper/2210.11909/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 81 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 30 of 68)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Going deeper with Image Transformers](https://mlsanity.com/paper/2103.17239/) (2021)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [Rethinking Spatial Dimensions of Vision Transformers](https://mlsanity.com/paper/2103.16302/) (2021)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)

## Built on by (most cited first, 19 of 19)

- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Chatting Makes Perfect: Chat-based Image Retrieval](https://mlsanity.com/paper/2305.20062/) (2023)
- [AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrieval](https://mlsanity.com/paper/2408.03282/) (2024)
- [RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models](https://mlsanity.com/paper/2310.10221/) (2023)
- [A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships](https://mlsanity.com/paper/2408.15178/) (2024)
- [Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification](https://mlsanity.com/paper/2404.14985/) (2024)
- [Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval](https://mlsanity.com/paper/2312.09716/) (2023)
- [Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions](https://mlsanity.com/paper/2502.08438/) (2025)
- [Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding](https://mlsanity.com/paper/2308.11448/) (2023)
- [Dark Side Augmentation: Generating Diverse Night Examples for Metric Learning](https://mlsanity.com/paper/2309.16351/) (2023)
- [On Train-Test Class Overlap and Detection for Image Retrieval](https://mlsanity.com/paper/2404.01524/) (2024)
- [3rd Place Solution to Meta AI Video Similarity Challenge](https://mlsanity.com/paper/2304.11964/) (2023)
- [LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition](https://mlsanity.com/paper/2407.06730/) (2024)
- [Conditional Cross Attention Network for Multi-Space Embedding without Entanglement in Only a SINGLE Network](https://mlsanity.com/paper/2307.13254/) (2023)
- [Enhancing Landmark Detection in Cluttered Real-World Scenarios with Vision Transformers](https://mlsanity.com/paper/2308.13671/) (2023)
- [PReP: Efficient context-based shape retrieval for missing parts](https://mlsanity.com/paper/2410.14245/) (2024)
- [Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation](https://mlsanity.com/paper/2501.13968/) (2025)
- [Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers](https://mlsanity.com/paper/2604.00809/) (2026)
- [Region-Aware CLS Token Augmentation for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2610.10991/) (2026)

## Abstract (arXiv)

Vision transformers have achieved remarkable progress in vision tasks such as image classification and detection. However, in instance-level image retrieval, transformers have not yet shown good performance compared to convolutional networks. We propose a number of improvements that make transformers outperform the state of the art for the first time. (1) We show that a hybrid architecture is more effective than plain transformers, by a large margin. (2) We introduce two branches collecting global (classification token) and local (patch tokens) information, from which we form a global image representation. (3) In each branch, we collect multi-layer features from the transformer encoder, corresponding to skip connections across distant layers. (4) We enhance locality of interactions at the deeper layers of the encoder, which is the relative weakness of vision transformers. We train our model on all commonly used training sets and, for the first time, we make fair comparisons separately per training set. In all cases, we outperform previous models based on global representation. Public code is available at https://github.com/dealicious-inc/DToP.