Summary of Inquire: a Natural World Text-to-image Retrieval Benchmark, by Edward Vendrow et al.
INQUIRE: A Natural World Text-to-Image Retrieval Benchmark
by Edward Vendrow, Omiros Pantazis, Alexander Shepard, Gabriel Brostow, Kate E. Jones, Oisin Mac Aodha, Sara Beery, Grant Van Horn
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The INQUIRE benchmark is a text-to-image retrieval challenge designed to test the abilities of multimodal vision-language models on expert-level queries. The benchmark includes a new dataset of five million natural world images, along with 250 expert-level queries that require nuanced image understanding and domain expertise. Two core retrieval tasks are evaluated: full dataset ranking (INQUIRE-Fullrank) and reranking (INQUIRE-Rerank). Recent multimodal models struggle to achieve high mAP@50 scores, indicating a significant challenge. Reranking with more powerful models can improve performance, but there is still room for improvement. The INQUIRE benchmark aims to bridge the gap between AI capabilities and the needs of real-world scientific inquiry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The INQUIRE benchmark is a new way to test how well computers can understand images when given a text description. It uses a big dataset of pictures from nature, along with 250 tricky questions that need nuanced answers. The goal is to see which computer models are best at finding the right pictures for each question. So far, even the most advanced models aren’t doing very well, but there’s still room for improvement. This benchmark is important because it can help computers work better in real-world scientific research, like identifying species and understanding ecosystems. |
