🇪🇺 PhD student at the Max Planck Institute for Informatics, and Institute of Science & Technology - Austria.
💻🏃🏻♂️🚴🏻🏋🏻🏊⛷️🎸🎹📚
Webpage: https://sidgairo18.github.io/
See exciting keynote talks from leading researches!
See accepted papers & posters here: cv4dc.github.io/2025/
#AI4Good
See exciting keynote talks from leading researches!
See accepted papers & posters here: cv4dc.github.io/2025/
#AI4Good
I rely on these often, and after sharing them with a few folks in my group who found them useful, I thought it could be of use to the broader community 🧵👇 (1/n)
I rely on these often, and after sharing them with a few folks in my group who found them useful, I thought it could be of use to the broader community 🧵👇 (1/n)
📣 We're pleased to announce that the deadline for non-proceeding track #CV4DC at @iccv.bsky.social has been extended to August 15, 2025
Looking forward to your submissions! cv4dc.github.io/2025/
📣 We're pleased to announce that the deadline for non-proceeding track #CV4DC at @iccv.bsky.social has been extended to August 15, 2025
Looking forward to your submissions! cv4dc.github.io/2025/
Congratulations to all the authors! To know more, visit us in the poster sessions!
A 🧵with more details:
@icmlconf.bsky.social @mpi-inf.mpg.de
Congratulations to all the authors! To know more, visit us in the poster sessions!
A 🧵with more details:
@icmlconf.bsky.social @mpi-inf.mpg.de
🎉 Congratulations to all the authors whose papers were accepted. We can't wait to meet you at @iccv.bsky.social in Hawaii on Oct 19th.
⏰ Our non-proceeding track is still accepting submissions until July 20th! Details in the comments
🎉 Congratulations to all the authors whose papers were accepted. We can't wait to meet you at @iccv.bsky.social in Hawaii on Oct 19th.
⏰ Our non-proceeding track is still accepting submissions until July 20th! Details in the comments
One of the prettiest and liveliest cities I’ve traveled to ✨ 🚤
One of the prettiest and liveliest cities I’ve traveled to ✨ 🚤
Best test accuracy: 71.20% at Epoch 10, for Learning Rate = 0.01 Makes a hell of a lot of difference! Almost 10% 🚨
Best test accuracy: 71.20% at Epoch 10, for Learning Rate = 0.01 Makes a hell of a lot of difference! Almost 10% 🚨
www.globalplayer.com
www.globalplayer.com
We’re proud to announce that we have 5 papers accepted to the main conference and 7 papers accepted at various CVPR workshops this year!
We’re looking forward to sharing our research with the community in Nashville!
Stay tuned for more details! @mpi-inf.mpg.de
We’re proud to announce that we have 5 papers accepted to the main conference and 7 papers accepted at various CVPR workshops this year!
We’re looking forward to sharing our research with the community in Nashville!
Stay tuned for more details! @mpi-inf.mpg.de
Pretty relaxing and a nice rejuvinating break from research✨
P.S.: Once you are used to driving road bikes, MTBs seem too slow 🤪
Pretty relaxing and a nice rejuvinating break from research✨
P.S.: Once you are used to driving road bikes, MTBs seem too slow 🤪
Lot of great interactions, meeting amazing people and a very well organised conference 🌟.
It was great to catch up with old colleagues and meet new folks.
For more detail see: arxiv.org/abs/2503.00641
Lot of great interactions, meeting amazing people and a very well organised conference 🌟.
It was great to catch up with old colleagues and meet new folks.
For more detail see: arxiv.org/abs/2503.00641
We answer how exactly does "What you explain depend on how you train". 🤓
We will be @ Hall 3 + Hall 2B #492 (26th April, 3PM - 5:30 PM)
Please reach out via dm if interestd in a chat or ☕️
#ICLR2025
We answer how exactly does "What you explain depend on how you train". 🤓
We will be @ Hall 3 + Hall 2B #492 (26th April, 3PM - 5:30 PM)
Please reach out via dm if interestd in a chat or ☕️
#ICLR2025
In his thesis, "Improving Trustworthiness of Deep Learning via Inspectable and Robust Representations", he explores transparency and robustness of deep networks. We wish him the best!
In his thesis, "Improving Trustworthiness of Deep Learning via Inspectable and Robust Representations", he explores transparency and robustness of deep networks. We wish him the best!
Source: “Fully Convolutional Networks for Semantic Segmentation”
arxiv.org/abs/1411.403...
Source: “Fully Convolutional Networks for Semantic Segmentation”
arxiv.org/abs/1411.403...
#computervision #machinelearning #research
#computervision #machinelearning #research
Infact, (soonish) the reviewing process could be supported with pipelines that can authenticate claims and results made in papers. 🤓
openai.com/index/paperb...
Infact, (soonish) the reviewing process could be supported with pipelines that can authenticate claims and results made in papers. 🤓
openai.com/index/paperb...