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Found 10 papers, 5 papers with code
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices.
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation
In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme.
Depthwise Separable Temporal Convolutional Network for Action Segmentation
Recent temporal convolution based approaches either use encoder-decoder(ED) architecture or dilations with doubling factor in consecutive convolution layers to segment actions in videos.
Ranked #21 on
Action Segmentation
on Breakfast
Generating 3D People in Scenes without People
However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e. g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions.
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form.
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls
Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone.
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period.
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set.
Temporal Human Action Segmentation via Dynamic Clustering
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring.
An audiovisual political speech analysis incorporating eye-tracking and perception data
Additionally, we compare the gaze behavior of the human subjects to evaluate saliency regions in the multimodal and visual only conditions.
