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Found 8 papers, 7 papers with code
Learning to Regress Bodies from Images using Differentiable Semantic Rendering
For Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight match between a rendered SMPL body and the minimally-clothed regions of the image.
Ranked #14 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PA-MPJPE metric)
BABEL: Bodies, Action and Behavior with English Labels
To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences.
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior.
Ranked #10 on
3D Human Pose Estimation
on MPI-INF-3DHP
Cross-topic distributional semantic representations via unsupervised mappings
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation.
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations
Neural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations.
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction".
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
