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Found 11 papers, 1 papers with code
Large Sequence Representation Learning via Multi-Stage Latent Transformers
We created a dataset with 11, 926 images depicting food product labels entitled TREAT dataset, with fully detailed annotations.
Bidirectional Long-Range Parser for Sequential Data Understanding
The transformer is a powerful data modelling framework responsible for remarkable performance on a wide range of tasks.
Watermark Text Pattern Spotting in Document Images
Watermark text spotting in document images can offer access to an often unexplored source of information, providing crucial evidence about a record's scope, audience and sometimes even authenticity.
Reconstructing Three-Dimensional Models of Interacting Humans
Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling.
CONSENT: Context Sensitive Transformer for Bold Words Classification
We present CONSENT, a simple yet effective CONtext SENsitive Transformer framework for context-dependent object classification within a fully-trainable end-to-end deep learning pipeline.
Learning Complex 3D Human Self-Contact
Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling.
Human Synthesis and Scene Compositing
Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression.
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
The final stage of 3d pose and shape prediction is based on a learned attention process where information from different human body parts is optimally integrated.
Human Appearance Transfer
We propose an automatic person-to-person appearance transfer model based on explicit parametric 3d human representations and learned, constrained deep translation network architectures for photographic image synthesis.
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
We propose a deep multitask architecture for \emph{fully automatic 2d and 3d human sensing} (DMHS), including \emph{recognition and reconstruction}, in \emph{monocular images}.
Ranked #21 on
3D Human Pose Estimation
on HumanEva-I
Parametric Image Segmentation of Humans with Structural Shape Priors
The figure-ground segmentation of humans in images captured in natural environments is an outstanding open problem due to the presence of complex backgrounds, articulation, varying body proportions, partial views and viewpoint changes.
