This paper presents a framework for efficient 3D clothed avatar
reconstr...
The main challenge of monocular 3D object detection is the accurate
loca...
Unsupervised learning of vision transformers seeks to pretrain an encode...
Weakly supervised person search aims to jointly detect and match persons...
Semantic face image manipulation has received increasing attention in re...
Recently, great progress has been made in single-image super-resolution
...
This paper presents a novel framework for planning in unknown and occlud...
In this paper, we review adversarial pretraining of self-supervised deep...
Image restoration algorithms such as super resolution (SR) are indispens...
Most state-of-the-art instance-level human parsing models adopt two-stag...
With the development of deep learning, single image super-resolution (SI...
Dark environment becomes a challenge for computer vision algorithms owin...
Recently, deep convolution neural networks (CNNs) steered face
super-res...
As a representative self-supervised method, contrastive learning has ach...
It is critical to obtain high resolution features with long range depend...
Image restoration algorithms such as super resolution (SR) are indispens...
Representation learning has significantly been developed with the advanc...
Cross-resolution face recognition (CRFR), which is important in intellig...
Model inversion (MI) attacks in the whitebox setting are aimed at
recons...
In this paper, we propose the K-Shot Contrastive Learning (KSCL) of visu...
Along with the development of the modern smart city, human-centric video...
Traffic forecasting has emerged as a core component of intelligent
trans...
Deep neural networks have been successfully applied to many real-world
a...
Recent advances in Graph Convolutional Neural Networks (GCNNs) have show...
Self-supervised learning by predicting transformations has demonstrated
...
Existing methods for person re-identification (Re-ID) are mostly based o...
Human motion prediction aims to generate future motions based on the obs...
Differentiable architecture search (DARTS) provided a fast solution in
f...
Learning Transformation Equivariant Representations (TERs) seeks to capt...
The Long Short-Term Memory (LSTM) recurrent neural network is capable of...
Small data challenges have emerged in many learning problems, since the
...
The learning of Transformation-Equivariant Representations (TERs), which...
Recent advancements in recurrent neural network (RNN) research have
demo...
The success of deep neural networks often relies on a large amount of la...
Hashing has been widely applied to multimodal retrieval on large-scale
m...
In this paper, we aim to address the problem of human interaction recogn...
Asking effective questions is a powerful social skill. In this paper we ...
Meta-learning approaches have been proposed to tackle the few-shot learn...
In this paper, we formalize the idea behind capsule nets of using a caps...
Hashing has attracted increasing research attentions in recent years due...
In this paper, we present novel sharp attention networks by adaptively
s...
In this paper, we study the problem of designing efficient convolutional...
Due to the special gating schemes of Long Short-Term Memory (LSTM), LSTM...
In this paper, we present a novel localized Generative Adversarial Net (...
In this paper, we present a simple and modularized neural network
archit...
Recently, Long Short-Term Memory (LSTM) has become a popular choice to m...
In this paper, we present a label transfer model from texts to images fo...
*New Theory Result* We analyze the generalizability of the LS-GAN, showi...
With the prevalence of the commodity depth cameras, the new paradigm of ...
The long short-term memory (LSTM) neural network is capable of processin...