Vision-language pre-training models (VLP) are vulnerable, especially to
...
Cross-lingual image captioning is confronted with both cross-lingual and...
Incremental few-shot semantic segmentation (IFSS) aims to incrementally
...
In this paper, we present our solutions to the two sub-challenges of
Aff...
Data-free quantization (DFQ) recovers the performance of quantized netwo...
We propose to perform video question answering (VideoQA) in a Contrastiv...
Data-free quantization (DFQ) recovers the performance of quantized netwo...
Lipreading refers to understanding and further translating the speech of...
Stereo images, containing left and right view images with disparity, are...
Low-light stereo image enhancement (LLSIE) is a relatively new task to
e...
In most E-commerce platforms, whether the displayed items trigger the us...
Graph Convolution Networks (GCNs), with their efficient ability to captu...
Images collected in real-world low-light environment usually suffer from...
Online Knowledge Distillation (OKD) improves the involved models by
reci...
Depression is one of the most common mental disorders, which imposes hea...
Network quantization has emerged as a promising method for model compres...
Most modern recommender systems predict users preferences with two
compo...
Blind image deblurring (BID) remains a challenging and significant task....
Depression is one of the most prevalent mental disorders, which seriousl...
Despite the success that metric learning based approaches have achieved ...
The cold start problem in recommender systems is a long-standing challen...
As users often express their preferences with binary behavior data (impl...
In this paper, we propose a new challenging task named as partial
multi-...
This paper strives to predict fine-grained fashion similarity. In this
s...
Few-shot classification studies the problem of quickly adapting a deep
l...
As a key application of artificial intelligence, recommender systems are...
Deep multi-view clustering methods have achieved remarkable performance....
This paper presents one-bit supervision, a novel setting of learning fro...
Graph Convolution Network (GCN) has attracted significant attention and
...
In many recommender systems, users and items are associated with attribu...
With the increasing availability of videos, how to edit them and present...
Metric-based few-shot learning methods concentrate on learning transfera...
Person re-identification (Re-ID) in real-world scenarios usually suffers...
In recent years, cross-modal hashing (CMH) has attracted increasing
atte...
Recommender systems are embracing conversational technologies to obtain ...
Graph Convolutional Networks (GCNs) are state-of-the-art graph based
rep...
Deep convolutional neural networks have largely benefited computer visio...
Single image deraining task is still a very challenging task due to its
...
We propose a novel and unsupervised representation learning model, i.e.,...
We propose a joint subspace recovery and enhanced locality based robust
...
Grounding natural language in images, such as localizing "the black dog ...
Pruning filters is an effective method for accelerating deep neural netw...
Precise user and item embedding learning is the key to building a succes...
Person re-identification (re-ID) is a task of matching pedestrians under...
Matrix factorization (MF) has been widely used to discover the low-rank
...
Item-based Collaborative Filtering(short for ICF) has been widely adopte...
Collaborative Filtering (CF) is one of the most successful approaches fo...
Image based social networks are among the most popular social networking...
Movies provide us with a mass of visual content as well as attracting
st...
In this paper, we study the problem of designing efficient convolutional...