#eccv2022 — Public Fediverse posts

Did you know that you can follow our (pre-)recorded conference talks on our FIZ ISE youtube channel? For example, you can listen to @epoz presenting "The Art Historian's Bicycle becomes an E-Bike" about recent research results around #iconclass from the VisArt workshop at #ECCV2022
video: https://www.youtube.com/watch?v=gfIYaIZQ9DI
paper: https://zenodo.org/records/7225425
FIZ ISE youtube channel: https://www.youtube.com/@ISEFIZKarlsruhe

#arthistory #computervision @fiz_karlsruhe #digitalhunmanities #culturalheritage #embeddings

Due to requests at #ECCV2022 and to make our #MapFreeReloc dataset useful for more tasks, we make the SfM reconstructions of our train set publicly available.

🔥460 SfM models of outdoor scenes all around the world 🔥
https://research.nianticlabs.com/mapfree-reloc-benchmark/dataset

Want to train 460 NeRFs? Go ahead.

Each scene was captured by non-expert users with two independent scans, sometimes months apart. We reconstructed them with COLMAP and aligned them to the original phone trajectories.

Thus, all models are in metric scale.

The most impactful paper that I was (co)first author on was “VQGAN-CLIP: Open domain image generation and editing with natural language guidance.” This paper was about a methodology that @rivershavewings @Adverb and others developed in the summer of 2021, but a paper never got written up about it. I performed the systematic experiments that never happened when it came out and wrote most of the text itself. I had a blast presenting it at #ECCV2022 this October

https://arxiv.org/abs/2204.08583

Did you miss #ECCV2022 & #DIRA2022? Did you go, but want to relive the experience?

Thanks to the Web Science and Digital Libraries Research Group blog for posting my trip report that covers keynotes, some interesting papers, and my work at #ECCV2022 & the #DIRA2022 workshop.

https://ws-dl.blogspot.com/2022/12/2022-12-23-eccv-2022-and-dira-2022-trip.html

#ComputerVision #InformationRetrieval #ComputerScience #Conference

#ECCV2022 #DIRA2022 Many reasons exist for users to conduct image searches: protecting intellectual property, building datasets, providing evidence, or justifying funding. That abstract images are at a disadvantage hurts users leveraging search engines for these use cases.

#ECCV2022 #DIRA2022 Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54% between images in these categories. These results indicate a clear difference in capability among search engines.

Because Wikipedia is well indexed by search engines, we acquired abstract (diagrams) and natural (photos) images from Wikimedia Commons. We submitted these 380 images to each search engine and recorded how often the search engine returned the same image back. #ECCV2022 #DIRA2022

The major search engines Baidu, Bing, Google, and Yandex support "reverse image search" -- where the user can upload an image and view pages that contain that image or pages that have similar images. #ECCV2022 #DIRA2022

Our preprint of "Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine" from at #ECCV2022 #DIRA2022 is available. We find Google's and Yandex's reverse image search engines favor finding natural images over abstract ones.

Preprint: https://arxiv.org/abs/2211.02115