Despite the broad application of Machine Learning models as a Service
(M...
This paper considers a novel and challenging problem: unsupervised long-...
Adversarial attacks are valuable for evaluating the robustness of deep
l...
The main challenge for fine-grained few-shot image classification is to ...
Compositional Zero-Shot Learning (CZSL) aims to recognize novel composit...
The heavy reliance on data is one of the major reasons that currently li...
While deep neural networks (DNNs) have strengthened the performance of
c...
We investigate unsupervised person re-identification (Re-ID) with clothe...
Despite great strides made on fine-grained visual classification (FGVC),...
As powerful as fine-grained visual classification (FGVC) is, responding ...
Previous dialogue summarization datasets mainly focus on open-domain chi...
Unsupervised person re-identification (Re-ID) aims to match pedestrian i...
In this paper, we present a neural model for joint dropped pronoun recov...
RGB-infrared person re-identification is a challenging task due to the
i...
Unsupervised person re-identification (re-ID) has become an important to...
Polyp segmentation is of great importance in the early diagnosis and
tre...
Deep neural networks (DNNs) have achieved remarkable performance across ...
Fine-grained visual classification aims to recognize images belonging to...
We study a heterogeneous wireless sensor network (WSN) where N heterogen...
Fine-grained visual classification (FGVC) aims to distinguish the sub-cl...
Whether what you see in Figure 1 is a "labrador" or a "dog", is the ques...
This paper proposes a dual-supervised uncertainty inference (DS-UI) fram...
We study a heterogeneous two-tier wireless sensor network in which N
het...
Due to lack of data, overfitting ubiquitously exists in real-world
appli...
Pronouns are often dropped in Chinese conversations and recovering the
d...
Domain adaptive person re-identification (re-ID) is a challenging task d...
Despite achieving state-of-the-art performance, deep learning methods
ge...
For object detection, how to address the contradictory requirement betwe...
A deep neural network of multiple nonlinear layers forms a large functio...
Channel attention mechanisms, as the key components of some modern
convo...
In this paper, we propose a dual-attention guided dropblock module, and
...
Fine-grained visual classification (FGVC) is much more challenging than
...
Unsupervised domain adaptation aims to leverage labeled data from a sour...
The topic of object detection has been largely improved recently, especi...
Existing sketch-analysis work studies sketches depicting static objects ...
Key for solving fine-grained image categorization is finding discriminat...
Neural architecture search (NAS) is an emerging topic in machine learnin...
To provide a reliable wireless uplink for users in a given ground area, ...
The item cold-start problem seriously limits the recommendation performa...
Collaborative filtering, a widely-used recommendation technique, predict...
In this paper, we propose a salient-context based semantic matching meth...
Subspace clustering methods based on data self-expression have become ve...
Next basket recommendation, which aims to predict the next a few items t...
We study a wireless ad-hoc sensor network (WASN) where N sensors gather
...
Variational inference (VI) is a widely used framework in Bayesian estima...
We study a heterogeneous two-tier wireless sensor network in which N
het...
In many quantization problems, the distortion function is given by the
E...
Using artificial neural network for the prediction of heat demand has
at...
Heat demand prediction is a prominent research topic in the area of
inte...
A Bayesian approach termed BAyesian Least Squares Optimization with
Nonn...