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Found 10 papers, 6 papers with code
xT: Nested Tokenization for Larger Context in Large Images
Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping.
PaReprop: Fast Parallelized Reversible Backpropagation
We present PaReprop, a fast Parallelized Reversible Backpropagation algorithm that parallelizes the additional activation re-computation overhead in reversible training with the gradient computation itself in backpropagation phase.
TryOnDiffusion: A Tale of Two UNets
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person.
A Rigorous Evaluation of Real-World Distribution Shifts
Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data.
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data
Additionally, we present our model's 3D surface normal predictions on the MSCOCO dataset that lacks any real 3D surface normal labels.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
Ranked #29 on
Domain Generalization
on ImageNet-R
BlazePose: On-device Real-time Body Pose tracking
We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices.
Ranked #1 on
3D Pose Estimation
on Google-Yoga
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples.
Ranked #7 on
Few-Shot Image Classification
on Meta-Dataset Rank
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model.
Ranked #8 on
Multi-Person Pose Estimation
on COCO test-dev
Towards Accurate Multi-person Pose Estimation in the Wild
Trained on COCO data alone, our final system achieves average precision of 0. 649 on the COCO test-dev set and the 0. 643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Ranked #6 on
Keypoint Detection
on COCO test-challenge
