Search Results for author:
Found 20 papers, 8 papers with code
MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification
The central problem of sentence classification is to extract multi-scale n-gram features for understanding the semantic meaning of sentences.
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning
Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists.
Cross-domain Named Entity Recognition via Graph Matching
Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario.
EgoBody: Human Body Shape, Motion and Social Interactions from Head-Mounted Devices
However, research in this area is currently hindered by the lack of data.
The Power of Points for Modeling Humans in Clothing
The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically.
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification
We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification
Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear.
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.
Embedding-based Product Retrieval in Taobao Search
We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests.
Learning Representations for Incomplete Time Series Clustering
Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner.
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements
We demonstrate the efficacy of our surface representation by learning models of complex clothing from point clouds.
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks
We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar.
PLACE: Proximity Learning of Articulation and Contact in 3D Environments
To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world.
Learning Representations for Time Series Clustering
When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge.
Learning to Dress 3D People in Generative Clothing
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images
Currently, most deep models for multimodal segmentation rely on paired registered images.
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.
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry
The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework
As an efficient recurrent neural network (RNN) model, reservoir computing (RC) models, such as Echo State Networks, have attracted widespread attention in the last decade.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
