activity
20162025
most citedCAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions

126 citations · 424 across the 70 of their papers we have counts for

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53 papers · 1 filter

cs.CV20221 cited

DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation

Azade Farshad, Yousef Yeganeh, Helisa Dhamo +2

Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or…

cs.CV2022

What can we learn about a generated image corrupting its latent representation?

Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic +2

Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can tr…

cs.CV20222 cited

RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation

Ruida Zhang, Yan Di, Zhiqiang Lou +3

Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories. Recent methods harness shape pr…

cs.CV2022

PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects

Pengyuan Wang, HyunJun Jung, Yitong Li +6

Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape…

cs.CV202221 cited

SoftPool++: An Encoder-Decoder Network for Point Cloud Completion

Yida Wang, David Joseph Tan, Nassir Navab +1

We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not requir…

cs.CV2022

Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis

Mojtaba Bahrami, Mahsa Ghorbani, Nassir Navab

The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled d…