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Found 12 papers, 3 papers with code
Uncertainty-Aware Deep Video Compression with Ensembles
Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation between successive frames and then compress the residual error.
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
In this work, we introduce the task of 3D-aware VQA, which focuses on challenging questions that require a compositional reasoning over the 3D structure of visual scenes.
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on
Animal Pose Estimation
on Animal3D
Generating Images with 3D Annotations Using Diffusion Models
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios.
Robust Category-Level 3D Pose Estimation from Synthetic Data
In this work, we aim to narrow the performance gap between models trained on synthetic data and few real images and fully supervised models trained on large-scale data.
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization.
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
We consider the problem of category-level 6D pose estimation from a single RGB image.
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships
A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system.
