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Found 31 papers, 6 papers with code
Single Domain Dynamic Generalization for Iris Presentation Attack Detection
To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images.
Object Segmentation by Mining Cross-Modal Semantics
In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation
Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface.
NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation
Gaze estimation is the fundamental basis for many visual tasks.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.
Multi-Scale Wavelet Transformer for Face Forgery Detection
To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.
Point Cloud Upsampling via Cascaded Refinement Network
In this manner, the proposed cascaded refinement network can be easily optimized without extra learning strategies.
FBNet: Feedback Network for Point Cloud Completion
The rapid development of point cloud learning has driven point cloud completion into a new era.
Semi-supervised Ranking for Object Image Blur Assessment
Based on this dataset, we propose a method to obtain the blur scores only with the pairwise rank labels as supervision.
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression
In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
Few-shot One-class Domain Adaptation Based on Frequency for Iris Presentation Attack Detection
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.
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition
Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
Learning Multiple Explainable and Generalizable Cues for Face Anti-spoofing
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay
Simulations show that underwater disturbances have a large impact on the system considering communication delay.
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition
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.
Self-Domain Adaptation for Face Anti-Spoofing
In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference.
Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition
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.
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection
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.
Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif.
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
Multivariate Triangular Quantile Maps for Novelty Detection
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.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
General Classification
Hierarchical Reinforcement Learning
+4
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
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.
Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer
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.
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
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.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
Cross-media User Profiling with Joint Textual and Social User Embedding
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 with both Word-level and Clause-level AttentionNetworks
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
Correlation Tracking via Robust Region Proposals
Recently, correlation filter-based trackers have received extensive attention due to their simplicity and superior speed.
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.
