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Found 9 papers, 3 papers with code
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings.
Coaching a Teachable Student
We propose a novel knowledge distillation framework for effectively teaching a sensorimotor student agent to drive from the supervision of a privileged teacher agent.
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SelfD: Self-Learning Large-Scale Driving Policies From the Web
However, it is difficult to directly leverage such large amounts of unlabeled and highly diverse data for complex 3D reasoning and planning tasks.
Learning by Watching
When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed.
X-World: Accessibility, Vision, and Autonomy Meet
We tackle inherent data scarcity by leveraging a simulation environment to spawn dynamic agents with various mobility aids.
Multiple Object Tracking by Flowing and Fusing
Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID).
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking
The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames.
Temporal Unet: Sample Level Human Action Recognition using WiFi
In this task, every WiFi distortion sample in the whole series should be categorized into one action, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition.
SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
In this paper, we propose a simple yet effective Siamese Edge-Enhancement Network (SE2Net) to preserve the edge structure for salient object detection.
