Search Results for author:
Found 12 papers, 4 papers with code
Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
Directly generating scenes from satellite imagery offers exciting possibilities for integration into applications like games and map services.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Surgical Skill Assessment via Video Semantic Aggregation
Automated video-based assessment of surgical skills is a promising task in assisting young surgical trainees, especially in poor-resource areas.
CompNVS: Novel View Synthesis with Scene Completion
We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage.
Neural Routing by Memory
However, they use the same procedure sequence for all inputs, regardless of the intermediate features. This paper proffers a simple yet effective idea of constructing parallel procedures and assigning similar intermediate features to the same specialized procedures in a divide-and-conquer fashion.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Spatio-Temporal Perturbations for Video Attribution
The attribution method provides a direction for interpreting opaque neural networks in a visual way by identifying and visualizing the input regions/pixels that dominate the output of a network.
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view.
Towards Visually Explaining Video Understanding Networks with Perturbation
''Making black box models explainable'' is a vital problem that accompanies the development of deep learning networks.
Manipulation-skill Assessment from Videos with Spatial Attention Network
Recent advances in computer vision have made it possible to automatically assess from videos the manipulation skills of humans in performing a task, which breeds many important applications in domains such as health rehabilitation and manufacturing.
Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions
In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context.
Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition
We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks.
