GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval

@inproceedings{Schall2022GPR1200AB,
  title={GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval},
  author={Konstantin Schall and Kai Uwe Barthel and Nico Hezel and Klaus Jung},
  booktitle={MMM},
  year={2022}
}
Even though it has extensively been shown that retrieval specific training of deep neural networks is beneficial for nearest neighbor image search quality, most of these models are trained and tested in the domain of landmarks images. However, some applications use images from various other domains and therefore need a network with good generalization properties a general-purpose CBIR model. To the best of our knowledge, no testing protocol has so far been introduced to benchmark models with… 

References

SHOWING 1-10 OF 33 REFERENCES
Deep Image Retrieval: Learning Global Representations for Image Search
TLDR
This work proposes a novel approach for instance-level image retrieval that produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors by leveraging a ranking framework and projection weights to build the region features.
Fine-Tuning CNN Image Retrieval with No Human Annotation
TLDR
It is shown that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval.
Unifying Deep Local and Global Features for Image Search
TLDR
This work unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction, and introduces an autoencoder-based dimensionality reduction technique for local features, which is integrated into the model, improving training efficiency and matching performance.
Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval
TLDR
This paper presents a simple yet effective supervised aggregation method built on top of existing regional pooling approaches, and applies the newly proposed NRA loss function for deep metric learning to fine tune the backbone neural network and to learn the aggregation weights.
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
TLDR
Issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets are addressed: in particular, annotation errors, the size of the dataset, and the level of challenge are addressed.
Particular object retrieval with integral max-pooling of CNN activations
TLDR
This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN, and significantly improves existing CNN-based recognition pipeline.
Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
TLDR
This work introduces the Google Landmarks Dataset v2 (GLDv2), a new benchmark for large-scale, fine-grained instance recognition and image retrieval in the domain of human-made and natural landmarks, and demonstrates the suitability of the dataset for transfer learning by showing that image embeddings trained on it achieve competitive retrieval performance on independent datasets.
Lost in quantization: Improving particular object retrieval in large scale image databases
The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images
Neural Codes for Image Retrieval
TLDR
A thorough discussion of several state-of-the-art techniques in image retrieval by considering the associated subproblems: image description, descriptor compression, nearest-neighbor search and query expansion, and the combined use of deep architectures and hand-crafted image representations for accurate and efficient image retrieval.
INSTRE: A New Benchmark for Instance-Level Object Retrieval and Recognition
TLDR
This article proposes a new benchmark for INSTance-level visual object REtrieval and REcognition (INSTRE), and comprehensively evaluates several popular algorithms to large-scale object retrieval problem with multiple evaluation metrics.
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