In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
We thus define a new domain adaptation setting called Few-shot One-class Domain Adaptation (FODA), where adaptation only relies on a limited number of target bonafide samples.
Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
Simulations show that underwater disturbances have a large impact on the system considering communication delay.
Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework.
In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference.
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i. e., texts and images) for depression detection.
We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif.
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches.
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.
Most of the characteristics learned by the deep learning models have summarized the detection rules that can be recognized by the experienced pathologists, whereas there are still some features may not be intuitive to domain experts but discriminative in classification for machines.
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services.
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
In realistic scenarios, a user profiling model (e. g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media.
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
Recently, correlation filter-based trackers have received extensive attention due to their simplicity and superior speed.
This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.