Despite advances in multimodal learning, challenging benchmarks for mixed-modal image retrieval that combines visual and textual information are lacking. This paper introduces a novel benchmark to rigorously evaluate image retrieval that demands deep cross-modal contextual understanding. We present two new datasets: the Entity Image Dataset (EI), providing canonical images for Wikipedia entities, and the Mixed-Modal Image Retrieval Dataset (MMIR), derived from the WIT dataset. The MMIR benchmark features two challenging query types requiring models to ground textual descriptions in the context of provided visual entities: single entity-image queries (one entity image with descriptive text) and multi-entity-image queries (multiple entity images with relational text). We empirically validate the benchmark's utility as both a training corpus and an evaluation set for mixed-modal retrieval. The quality of both datasets is further affirmed through crowd-sourced human annotations. The datasets are accessible through the GitHub page: https://github.com/google-research-datasets/wit-retrieval.

Cross-modal (image-to-text and text-to-image) retrieval is an established task used in evaluation benchmarks to test the performance of vision-language models (VLMs). Several state-of-the-art VLMs (e.g. CLIP, BLIP-2) have achieved near-perfect performance on widely-used image-text retrieval benchmarks such as MSCOCO-Test-5K and Flickr30K-Test-1K. As a measure of out-of-distribution (OOD) generalization, prior works rely on zero-shot performance evaluated on one dataset (Flickr) using a VLM finetuned on another one (MSCOCO). We argue that such comparisons are insufficient to assess the OOD generalization capability of models due to high visual and linguistic similarity between the evaluation and finetuning datasets. To address this gap, we introduce WikiDO (drawn from Wikipedia Diversity Observatory), a novel cross-modal retrieval benchmark to assess the OOD generalization capabilities of pretrained VLMs. This consists of newly scraped 380K image-text pairs from Wikipedia with domain labels, a carefully curated, human-verified a)in-distribution (ID) test set (3K) and b) OOD test set (3K). The image-text pairs are very diverse in topics and geographical locations. We evaluate different VLMs of varying capacity on the \wikido benchmark; BLIP-2 achieves zero-shot performance of $R@1\approx66\%$ on the OOD test set, compared to $\approx$ $81\%$ on COCO and $\approx95\%$ on Flickr. When fine-tuned on WikiDO, the $R@1$ improvement is at most $\approx5\%$ on OOD instances compared to $\approx12\%$ on ID instances. We probe the VLMs with varying finetuning objectives and datasets of varying sizes to identify what aids OOD generalization the most. Our results confirm that WikiDO offers a strong cross-modal benchmark for current VLMs in specifically evaluating for OOD generalization. Our benchmark is hosted as a competition at https://kaggle.com/competitions/wikido24 with public access to dataset and code.

Andrea Burns, Krishna Srinivasan, Joshua Ainslie, Geoff Brown, Bryan Plummer, Kate Saenko, Jianmo Ni, Mandy Guo. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

This paper presents the AToMiC (Authoring Tools for Multi media Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision--language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image--text relationships and underspecified user models of retrieval tasks. To address the gap between these oversimplified settings and real-world applications for multimedia content creation, we introduce a new approach for building retrieval test collections. We leverage hierarchical structures and diverse domains of texts, styles, and types of images, as well as large-scale image--document associations embedded in Wikipedia. We formulate two tasks based on a realistic user model and validate our dataset through retrieval experiments using baseline models. AToMiC offers a testbed for scalable, diverse, and reproducible multimedia retrieval research. Finally, our dataset provides the basis for a dedicated track at the 2023 Text Retrieval Conference (TREC), and is publicly available at https://github.com/TREC-AToMiC/AToMiC.

Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks.Casting a sequence tagging task as a Seq2Seq one requires deciding the formats of the input and output sequences.However, we lack a principled understanding of the tradeoffs associated with these formats (such as the effect on model accuracy, sequence length, multilingual generalization, hallucination).In this paper, we rigorously study different formats one could use for casting input text sentences and their output labels into the input and target (i.e., output) of a Seq2Seq model.Along the way, we introduce a new format, which we show to to be both simpler and more effective.Additionally the new format demonstrates significant gains in the multilingual settings -both zero-shot transfer learning and joint training.Lastly, we find that the new format is more robust and almost completely devoid of hallucination -an issue we find common in existing formats.With well over a 1000 experiments studying 14 different formats, over 7 diverse public benchmarksincluding 3 multilingual datasets spanning 7 languages -we believe our findings provide a strong empirical basis in understanding how we should tackle sequence tagging tasks.Dataset Lang # Train # Valid # Test Token/ex Tagged span/ex % tokens tagged # Tag classes Tag Entropy mATIS en 4478 500 893 11.28 3.32 36.50 79 3.

The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information across image and text modalities. In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.5 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example. WIT Dataset is available for download and use via a Creative Commons license here: https://github.com/google-research-datasets/wit.

This paper describes a methodology for performing system level signal and power integrity analyses of SiP-based systems. The paper briefly outlines some new modeling and simulation techniques that have been developed to enable the proposed methodology. Some results based on the application of this methodology on test systems are also presented.