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Found 10 papers, 4 papers with code
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks.
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Ranked #4 on
RGB-D Salient Object Detection
on LFSD
Contextually Plausible and Diverse 3D Human Motion Prediction
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses.
Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention
Given an untrimmed video and a sentence as the query, the goal is to determine the starting, and the ending, of the relevant visual moment in the video, that corresponds to the query sentence.
Learning Variations in Human Motion via Mix-and-Match Perturbation
In this paper, we introduce an approach to stochastically combine the root of variations with previous pose information, which forces the model to take the noise into account.
VIENA2: A Driving Anticipation Dataset
Action anticipation is critical in scenarios where one needs to react before the action is finalized.
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Our approach builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently.
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Our experiments demonstrate the benefits of our classifier heatmaps and of our two-stream architecture on challenging urban scene datasets and on the YouTube-Objects benchmark, where we obtain state-of-the-art results.
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation
We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network.
Encouraging LSTMs to Anticipate Actions Very Early
In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos.
