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Found 12 papers, 10 papers with code
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks (e. g., object detection, depth estimation, etc).
Ranked #1 on
Surface Normals Estimation
on Taskonomy
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)
Which Tasks Should Be Learned Together in Multi-task Learning?
Many computer vision applications require solving multiple tasks in real-time.
An Information-Theoretic Metric of Transferability for Task Transfer Learning
An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images.
On Evaluation of Embodied Navigation Agents
Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence.
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited.
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2. 5D and 3D domains, with instance-level semantic and geometric annotations.
Feedback Networks
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer.
3D Semantic Parsing of Large-Scale Indoor Spaces
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos.
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
Ranked #4 on
Skeleton Based Action Recognition
on CAD-120
