Krishna Srinivasan @krishna2
I code stuff. I build models. I work in NLP/DL at Google Research. krishna2.com Union City, CA Joined March 2007-
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Doing a startup and organizing taxes ... couldn't help but contemplate between AGI [1] and AGI [2]. AGI [1] = Artificial General Intelligence AGI [2] = Adjusted Gross Income
"WikiDO: A New Benchmark Evaluating Cross-Modal Retrieval for Vision-Language Models", consisting of 384K image-text pairs from Wikipedia with domain labels, along with human-verified in/out distribution test sets. (Kalyan et al, 2024) openreview.net/pdf?id=EqaSEbU…
We will be presenting our work at a poster session during #SIGIR2023! Can't wait to connect with everyone in person 😎. A fantastic journey collaborating with the brilliant minds of @cadurosar @rafasamrez @krishna2 @mad_astronaut @sclincha @lintool 👉 dl.acm.org/doi/10.1145/35…
We also have @mattjustram presenting AToMiC our cross modal resource paper at #SIGIR2023 in collaboration with @lintool @mad_astronaut @krishna2 @rafasamrez and @sclincha
For more information the full guidelines are available trec-atomic.github.io/annoucements/T… and organisers will be more than happy to help with any problem / answer any question (just tweet or send an email to [email protected])
The final test topics will be released soon, but there’s a lot of material to start playing with" such: embeddings of openCLIP models + dev topics with human judgements over the baselines (trec-atomic.github.io/annoucements/d…).
Interested in Cross-Modal retrieval? Feel that evaluation on caption datasets is limited and noisy? The @TREC_AToMiC task is here: a real, large scale problem (retrieve illustration for @Wikipedia sections), with human judgements. Deadline in 45 days! @krishna2 @lintool
A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding abs: arxiv.org/abs/2305.03668 paper page: huggingface.co/papers/2305.03…
WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset abs: arxiv.org/abs/2305.05432 paper page: huggingface.co/papers/2305.05… github: github.com/google-researc…
"AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation" hierarchical structures, diverse domains of texts/images, and large scale image-document associations, all from @Wikipedia paper arxiv.org/abs/2304.01961 data github.com/TREC-AToMiC/AT… @lintool
In non-ChatGPT news... AToMiC (Authoring Tools for Multimedia Content) is a new take image-text search led by @mattjustram that will debut at TREC 2023! Check out our preprint arxiv.org/abs/2304.01961 and the track website: github.com/trec-atomic/at…
Looking forward to making this TREC track come to life alongside @mattjustram @lintool @sclincha @mad_astronaut @rafasamrez @krishna2 ! If you are attending TREC we will be live at 3PM ET with the introduction and preliminary discussion
Looking forward to making this TREC track come to life alongside @mattjustram @lintool @sclincha @mad_astronaut @rafasamrez @krishna2 ! If you are attending TREC we will be live at 3PM ET with the introduction and preliminary discussion
Please check out our TREC Proposal AToMiC!
"Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia" Automatically captioning @Wikipedia images by integrating contextual knowledge, based on the WIT dataset. (Nguyen et al, 2022) arxiv.org/pdf/2209.10474…
Thanks a lot for the invite @WikiResearch - looking forward to it and the discussions!
Thanks a lot for the invite @WikiResearch - looking forward to it and the discussions!
We are excited to share our work on our Pathways Autoregressive Text-to-Image model, Parti! #Parti achieves high-fidelity photorealistic image generation and supports content-rich synthesis involving complex compositions and world knowledge. parti.research.google
Learn more about the top solutions of the Wikipedia Image/Caption Matching Competition in our next session! 🏅🤩 #WikiM3L More info about the competition: diff.wikimedia.org/2021/09/13/the…
Get ready for the panel about "Multilinguality in multimodal research and open data" with five amazing speakers! 🎉 Moderated by @frimelle
How can the #ML community contribute to #Wikipedia, solving huge challenges in vision and language? What is the importance of open and unbiased data? Do not miss the first #WikiM3L workshop tomorrow, April 29th at @iclr_conf ! Program👉meta.wikimedia.org/wiki/Wiki-M3L/…
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