The IAPR TC-12 Benchmark: A New Evaluation Resource for Visual Information Systems

By Michael Grubinger, Paul D. Clough, Henning Müller, Thomas Deselaers. 2006

526 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions (2014, background): “Instead of several independent captions, Grubinger et al. (2006)’s IAPR TC-12 data set contains longer descriptions.”
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, background): “…that should be recognized as such (e.g., well-known public figures or landmark locations such as the Eiffel Tower) we argue that image understanding should focus on the information captured by Hodosh, Young & Hockenmaier BBC captions (Feng and Lapata 2010) IAPR-TC12 data set (Grubinger et al. 2006)”
Microsoft COCO Captions: Data Collection and Evaluation Server (2015, background): “Recently there has been a surprising resurgence of interest in this area [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], due to the renewed interest in neural network learning techniques [31], [32] and increasingly large datasets [33], [34], [35], [7], [36], [37], [38].”
ReferItGame: Referring to Objects in Photographs of Natural Scenes (2014, methodology): “We build our dataset of referring expressions on top of the ImageCLEF IAPR image retrieval dataset (Grubinger et al., 2006).”
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding (2016, methodology): “, 2014), which contains 20K images from IAPR TC12 dataset (Grubinger et al., 2006) with segmented regions from SAIAPR-12 dataset (Escalante et al.”
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question (2015, background): “, [21, 43, 11]) play a crucial role in this progress.”
Explain Images with Multimodal Recurrent Neural Networks (2014, methodology): “In the experiments, we validate our model on three benchmark datasets: IAPR TC-12 [9], Flickr 8K [31], Flickr 30K [14] and MS COCO [23].”
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, background): “2002), ESP Game (Ahn and Dabbish 2004), and IAPR-TC (Grubinger et al. 2006)—have only two views and are rather small-scale (5–20K images and 260–290 tags).”
Exploring Nearest Neighbor Approaches for Image Captioning (2015, background): “This surge in research is due in part to the creation of large caption datasets [11], [29], [13], [37], [1], [24], and new learning techniques [20], [12].”
Natural Language Object Retrieval (2015, methodology): “It contains 20,000 images from IAPR TC-12 dataset [9], together with segmented image regions from SAIAPR-12 dataset [5], and 120K annotated descriptions for the image regions collected in a two-player game that aims to make the image region identifiable from the annotation.”
Multi30K: Multilingual English-German Image Descriptions (2016, methodology): “They performed a series of experiments on the IAPR-TC12 dataset (Grubinger et al., 2006) of images aligned with German translations, showing that both English and German image description could be improved by transferring features from a multimodal neural language model trained to generate descripti”
Choosing Linguistics over Vision to Describe Images (2012, neutral): “The IAPR TC-12 benchmark was ﬁrst published for cross-language retrieval by Grubinger et al. (2006).”
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description (2016, background): “With very few exceptions (Grubinger et al., 2006; Funaki and Nakayama, 2015; Gao et al., 2015), these applications are inherently monolingual and existing work explore mostly English data.”
Segmentation from Natural Language Expressions (2016, methodology): “The ReferIt dataset [26] is built upon the IAPR TC-12 dataset [27] and has 20,000 images.”
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (2016, methodology): “Currently, among the available benchmark datasets, only the IAPR-TC12 dataset (Grubinger et al., 2006) has multilingual descriptions (in English and German).”
A Comprehensive Survey of Deep Learning for Image Captioning (2018, background): “n and post-generation tasks in social media networks. This dataset contains 1.1m posts on a wide range of topics and a long hashtag lists from 6.3k users. 4.1.6 IAPR TC-12 Dataset. IAPR TC-12 dataset [50] has 20k images. The images are collected from various sources such as sports, photographs of pe”
Captioning Images Taken by People Who Are Blind (2020, neutral): “thentic image captioning scenarios. Speciﬁcally, such prior work included image captions in newspaper articles [19] as well as captions provided by tour guides about images taken of tourist locations [21]. Our work differs from these prior works, both because we focus on a distinct use case (i.e., c”
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning (2019, methodology): “Some widely used databases include Corel 5k [16] (including 4,999 images with 260 object categories), ESP Game [17] (including 20,770 images with 268 categories), IAPRTC-12 [18] (including 19,627 images with 291 categories), NUSWIDE [19] (including 270K images with 81 categories), MS COCO [20] (incl”