Search Results for author:
Found 9 papers, 6 papers with code
Recognizing Emotions evoked by Movies using Multitask Learning
In this paper, we model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach.
SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation
Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation.
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers.
Ranked #1 on
Referring Expression Segmentation
on A2Dre test
Curriculum Learning for Recurrent Video Object Segmentation
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks.
Recurrent Instance Segmentation using Sequences of Referring Expressions
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions).
RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence.
Ranked #1 on
One-shot visual object segmentation
on YouTube-VOS
Iterative Deep Learning for Road Topology Extraction
This paper tackles the task of estimating the topology of road networks from aerial images.
Iterative Deep Learning for Network Topology Extraction
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks.
Improving Spatial Codification in Semantic Segmentation
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.
