Cross-lingual adaptation has proven effective in spoken language
underst...
The main challenge for fine-grained few-shot image classification is to ...
Recently, community has paid increasing attention on model scaling and
c...
As fine-grained visual classification (FGVC) being developed for decades...
Compositional Zero-Shot Learning (CZSL) aims to recognize novel composit...
Audio captioning aims at describing the content of audio clips with huma...
Data out-of-distribution is a meta-challenge for all statistical learnin...
Despite great strides made on fine-grained visual classification (FGVC),...
As powerful as fine-grained visual classification (FGVC) is, responding ...
Fine-grained visual classification (FGVC) aims to classify sub-classes o...
With the complexity of the network structure, uncertainty inference has
...
In this work, we address the task of referring image segmentation (RIS),...
Classifying the sub-categories of an object from the same super-category...
Few-shot image classification is a challenging problem which aims to ach...
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...
Fine-grained visual classification is a challenging task that recognizes...
Recently, graph neural networks (GNNs) have shown powerful ability to ha...
Fine-grained visual classification (FGVC) is becoming an important resea...
Fine-grained visual classification aims to recognize images belonging to...
Fine-grained visual classification (FGVC) aims to distinguish the sub-cl...
Object counting aims to estimate the number of objects in images. The le...
Few-shot learning for fine-grained image classification has gained recen...
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...
The actor and action semantic segmentation is a challenging problem that...
The loss function is a key component in deep learning models. A commonly...
Due to lack of data, overfitting ubiquitously exists in real-world
appli...
Recently, context reasoning using image regions beyond local convolution...
Domain adaptive person re-identification (re-ID) is a challenging task d...
Despite achieving state-of-the-art performance, deep learning methods
ge...
Few-shot meta-learning has been recently reviving with expectations to m...
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...
Traditional video captioning requests a holistic description of the vide...
Existing sketch-analysis work studies sketches depicting static objects ...
Key for solving fine-grained image categorization is finding discriminat...
Classifying the sub-categories of an object from the same super-category...
Sketch-based image retrieval (SBIR) is a challenging task due to the lar...
The development of deep convolutional neural network architecture is cri...
We introduce a novel problem of scene sketch zero-shot learning (SSZSL),...
Variational inference (VI) is a widely used framework in Bayesian estima...
The smart vehicles construct Vehicle of Internet which can execute vario...
In this paper we proposed an end-to-end short utterances speech language...
In this paper, we continue our previous work on the Dirichlet mixture mo...
Using artificial neural network for the prediction of heat demand has
at...