Astuti Sharma

Android XR is officially here! 🚀 Our new OS for next-gen headsets & glasses debuts on Samsung’s Galaxy XR. It’s the first platform built for the Gemini era, integrating a helpful AI assistant from the ground up to change how you get things done. Huge congrats to the teams at @Android, @SamsungMobile and @Qualcomm on this launch! Learn more here: blog.google/products/andro…

We’re excited to share our preparedness report on Code World Model (CWM), FAIR’s latest open-weight model for code generation and reasoning. This report was developed by the SEAL team and the AI Security team, marking our first external publication since part of SEAL joined Meta just 1.5 months ago. It reflects our ongoing commitment to safety and alignment as we look ahead to future open-source and frontier models. We’re actively working to broaden our evaluation coverage and improve our processes, and we welcome collaboration and feedback from the broader research community. For more details, please read the full report: ai.meta.com/research/publi…

Introducing SANPO, a multi-attribute video dataset for outdoor human egocentric scene understanding composed of both real-world and synthetic data, including depth maps and video panoptic masks with a wide variety of semantic class labels. Read more → goo.gle/3ZISInU

PolyMaX: General Dense Prediction with Mask Transformer paper page: huggingface.co/papers/2311.05… Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks.

@tarun_05 Congrats Tarun!

🗓️ 10/27 Poster 27 at 11:00 AM (Hall B). MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation @tarun_05 @AstutiSharma @manu_chandraker Cross-domain sample consistency approach to scale domain adaptation to large-scale datasets. tarun005.github.io/MemSAC/

Great resource for learning the basics of glibc heap exploitation (credits @dhaval_kapil) heap-exploitation.dhavalkapil.com #exploit #informationsecurity #Cybersecurity #hacking #heap #glibc #learning

This is a good intro to (glibc) heap exploitation for beginners by @dhaval_kapil heap-exploitation.dhavalkapil.com

Excited to share that our work MemSAC has been accepted in #ECCV2022. A novel domain adaptation approach for robust transfer between domains which also addresses the limitations while learning in large-scale datasets. @tarun_05 @manu_chandraker arxiv.org/abs/2207.12389

Attending #CVPR2021. Delighted to share our work ILA-DA.

This is resolved. Thank you!

Got some leads. Thank you.

Teach girls bravery, not perfection ted.com/talks/reshma_s…

Thanks @droidcon @droidconIN for Female Developer Sponsorship. Excited to be there at #droidcon this year !