Rafael S Rezende

Research scientist @naverlabseurope. Here to signal boost interesting work in CV/ML.

[Fabien Baradel @fabienbaradel]

Multi-HMR has been accepted to #ECCV2024🤸.
With Multi-HMR, we won the ROBIN challenge on 3D human reconstruction at #CVPR2024.
Today, we're releasing the training code on top of the existing demo code: https://t.co/gCnYhJGL9o
See you in Milano 👋

[Miriam Redi @mad_astronaut]

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

[Rafael S Rezende @rafasamrez]

@wightmanr CUB200, Cars196, and Stanford online products are probably the most used benchmarks for deep metric learning/image retrieval. Are they the type of dataset you're looking for?

[Rafael S Rezende @rafasamrez]

@TylerLHayes @naverlabseurope Welcome aboard!

[Rafael S Rezende @rafasamrez]

Happy to be recognized as an outstanding reviewers for #ECCV2022

[European Conference on Computer Vision #ECCV2026 @eccvconf]

List of #ECCV2022 Outstanding Reviewers. Thank you all for your service! 👏

https://t.co/PHt4mVIoof

[Deb Raji @rajiinio]

A recurring mis-conception in ML is that setting standards for ethical research practice is the same as censorship. It's not - it's about meeting community norms / expectations. This would be like saying a reviewer's request for reporting ablations or uncertainty was censorship.

[Rafael S Rezende @rafasamrez]

@chriswolfvision Which should serve as an example of how fundamentally broken this idea is. It turns out, when one "tries" to select "the smartest and most driven", they just select a set of smart wealthy kids. Related read: https://t.co/n4PwPypcGb

[Carlos Lassance @cadurosar]

Happy to finally announce the release of our models! Improvements due to the data from: https://t.co/YiJDNQE3Ff and the PLM from https://t.co/ZhQgB7OOB0. We also added the needed code to index and retrieve using Anserini!

[Aleksander Madry @aleks_madry]

You can start using FFCV today: check out the repo (https://t.co/6RPIThoGdo) and docs (https://t.co/sYUtGq1f6d)---we even have a Slack! Stay tuned for a blog post, and a paper explaining the details. w/ @gpoleclerc @andrew_ilyas @logan_engstrom @smsampark @hadisalmanx (3/3)

[Sebastian Raschka @rasbt]

Vision Transformers (ViTs) are an interesting development in computer vision. One downside: they are more data hungry than CNN. Presumably because they have less of a spatial / locality inductive bias so they require more data to obtain acceptable visual representations 🧵 [1/7]

[Rafael S Rezende @rafasamrez]

@mribeirodantas Basta lembrar que há pelo menos 7 anos nos dizem que "em 5 anos teremos carros que se dirigem sozinhos", ou ver empresas superestimando serviços médiocres/antiéticos sob a faixada de ser "IA".

[Rafael S Rezende @rafasamrez]

@mribeirodantas Não discordo do sentimento em si, mas se tem uma coisa que IA não precisa hoje é de mais gente fazendo overhype.

[SanghyukChun @SanghyukChun]

We finally have the final (live) invited talk today.
ImageNets of "x": ImageNet's Infrastructural Impact
by @cephaloponderer and @alexhanna
See you at the live Zoom!
https://t.co/6TiAtcAmuQ

https://t.co/C5zcIJTzHr

[SanghyukChun @SanghyukChun]

3rd session of our WS has begun

@SharonYixuanLi Uncovering the Deep Unknowns of ImageNet Model: Challenges and Opportunties

@wightmanr ImageNet models from the trenches

@dawnsongtweets @hendrycks Using ImageNet to Measure Robustness and Uncertainty

https://t.co/sGfPxW6b4y

[SanghyukChun @SanghyukChun]

Oops I forgot announcing the second track!
"Are we done with ImageNet?" by
@__kolesnikov__ is now live and other talks by
@BeccaRoelofs Is ImageNet Solved? Evaluating Machine Accuracy
@ShibaniSan From ImageNet to Image Classification
are also available

https://t.co/K7hEHBSfLM

[Rafael S Rezende @rafasamrez]

@michalwols @SanghyukChun @HugoTouvron The recording should be available in the workshop virtual place in around 1 month, according to NeurIPS guidelines.

[Diane Larlus @dlarlus]

After three great talks, our first panel on "the future of ImageNet" is about to start w @orussakovsky @KaiyuYang4 @VittoFerrariCV @MatthiasBethge
#NeurIPS2021 ImageNet: past, present, future

[Rafael S Rezende @rafasamrez]

First session is starting NOW!

[SanghyukChun @SanghyukChun]

We start the workshop from the future of ImageNet:

Mon 04:30-5:00
Olga Russakovsky & Kaiyu Yang
- Fairness and privacy aspects of ImageNet

Mon 5:00-5:30
Vittorio Ferrari
- OpenImages: one dataset for many computer vision tasks

(4/N)

[Rafael S Rezende @rafasamrez]

In about 3 hours, we going LIVE on the #NeurIPS2021 workshop on ImageNet: past, present and future. Come check the amazing talks, panel and poster sessions! You can check the schedule 👇 or here: https://t.co/P06goe50D7

[SanghyukChun @SanghyukChun]

What can we learn from the decade of history of ImageNet? Are we done with ImageNet? What will be the future of ImageNet-based vision researches?

We organize the workshop on ImageNet: past, present, and future for answering the questions!
https://t.co/W8o5bNSmxT

Speakers:
(1/N)

[RSIP Vision @RSIPvision]

This will be a fascinating workshop! Want to learn more before you go? Read the preview on Computer Vision News with @orussakovsky ==> https://t.co/kPm9WZYfTR
@naverlabseurope @dlarlus #NeurIPS2021 @RSIPvision