Search Results for author:
Found 43 papers, 24 papers with code
Blockchain-based Federated Recommendation with Incentive Mechanism
The experimental results show that our proposed incentive mechanism can attract clients with superior training data to engage in the federal recommendation at a lower cost, which can increase the economic benefit of federal recommendation by 54. 9\% while improve the recommendation performance.
ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems
The escalating prevalence of encryption protocols has led to a concomitant surge in the number of malicious attacks that hide in encrypted traffic.
Joint-Motion Mutual Learning for Pose Estimation in Videos
Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model.
Multi-threshold Deep Metric Learning for Facial Expression Recognition
Each embedding slice corresponds to a sample threshold and is learned by enforcing the corresponding triplet loss, yielding a set of distinct expression features, one for each embedding slice.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Do As I Do: Pose Guided Human Motion Copy
Human motion copy is an intriguing yet challenging task in artificial intelligence and computer vision, which strives to generate a fake video of a target person performing the motion of a source person.
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
In this paper, we try to tackle these challenges through three designs: (1) We present a novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature.
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.
Red Teaming Visual Language Models
Additionally, we simply apply red teaming alignment to LLaVA-v1. 5 with Supervised Fine-tuning (SFT) using RTVLM, and this bolsters the models' performance with 10% in RTVLM test set, 13% in MM-Hal, and without noticeable decline in MM-Bench, overpassing other LLaVA-based models with regular alignment data.
Rethinking Human Motion Prediction with Symplectic Integral
We further present the Symplectic Temporal Aggregation module in the light of the symplectic temporal splitting algorithm which splits the long-term prediction into multiple accurate short-term predictions generated by a symplectic operator to secure modeling stability.
Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval
Instead of crafting a new method pursuing further improvement on accuracy, in this paper we propose a multi-teacher distillation framework Whiten-MTD, which is able to transfer knowledge from off-the-shelf pre-trained retrieval models to a lightweight student model for efficient visual retrieval.
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Deepfake has taken the world by storm, triggering a trust crisis.
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
However, the accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution.
Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization
We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature.
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection
Early detection of dysplasia of the cervix is critical for cervical cancer treatment.
Masked Diffusion Models Are Fast Distribution Learners
In the pre-training stage, we propose to mask a high proportion (e. g., up to 90\%) of input images to approximately represent the primer distribution and introduce a masked denoising score matching objective to train a model to denoise visible areas.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
To mitigate these limitations, we introduce the LargeST benchmark dataset.
Ranked #1 on
Traffic Prediction
on LargeST
Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization
However, the vanilla Euclidean space is not efficient for modeling important motion characteristics such as the joint-wise angular acceleration, which reveals the driving force behind the motion.
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it.
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training
Source-free domain adaptive semantic segmentation has gained increasing attention recently.
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts.
Invariant Feature Learning for Generalized Long-Tailed Classification
In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes.
Ranked #1 on
Long-tail Learning
on ImageNet-GLT
Copy Motion From One to Another: Fake Motion Video Generation
3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.
2D Human Pose Estimation: A Survey
Briefly, existing approaches put their efforts in three directions, namely network architecture design, network training refinement, and post processing.
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings
To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.
Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation
State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).
3D Human Motion Prediction: A Survey
3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors.
Motion Prediction via Joint Dependency Modeling in Phase Space
In this paper, we introduce a novel convolutional neural model to effectively leverage explicit prior knowledge of motion anatomy, and simultaneously capture both spatial and temporal information of joint trajectory dynamics.
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results.
Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence
In this paper, we consider defense methods from the general effect of adversarial attacks that take on neurons inside the model.
TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors
Phishing detectors direct their efforts in hunting phishing addresses.
Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction
Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation.
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways.
Deep Dual Consecutive Network for Human Pose Estimation
Multi-frame human pose estimation in complicated situations is challenging.
Ranked #1 on
Multi-Person Pose Estimation
on PoseTrack2017
(using extra training data)
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Motion Prediction Using Trajectory Cues
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.
Zero-Shot Multi-View Indoor Localization via Graph Location Networks
In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization.
Towards Natural and Accurate Future Motion Prediction of Humans and Animals
Anticipating the future motions of 3D articulate objects is challenging due to its non-linear and highly stochastic nature.
A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers
Next, we prove that our mechanism is an FPTAS, i. e., it can be approximated within $1 + \epsilon$ for any given $\epsilon > 0$, while the running time of our mechanism is polynomial in $n$ and $1/\epsilon$, where $n$ is the number of tenants in the datacenter.
Computer Science and Game Theory
NAIS: Neural Attentive Item Similarity Model for Recommendation
As such, the key to an item-based CF method is in the estimation of item similarities.
