Search Results for author:
Found 10 papers, 2 papers with code
Making Large Multimodal Models Understand Arbitrary Visual Prompts
Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain.
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.
Self-Supervised Object Detection via Generative Image Synthesis
We present SSOD, the first end-to-end analysis-by synthesis framework with controllable GANs for the task of self-supervised object detection.
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process.
Self-Supervised Viewpoint Learning From Image Collections
Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets.
Geometric Image Synthesis
The task of generating natural images from 3D scenes has been a long standing goal in computer graphics.
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e. g., at texture-less or reflective surfaces.
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes.
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort.
