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Found 32 papers, 12 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
The deepfake threats to society and cybersecurity have provoked significant public apprehension, driving intensified efforts within the realm of deepfake video detection.
Exploring Straighter Trajectories of Flow Matching with Diffusion Guidance
First, we propose a coupling strategy to straighten trajectories, creating couplings between image and noise samples under diffusion model guidance.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification
Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance.
Rumor Detection with Diverse Counterfactual Evidence
Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results.
Cross Architecture Distillation for Face Recognition
Transformers have emerged as the superior choice for face recognition tasks, but their insufficient platform acceleration hinders their application on mobile devices.
Grouped Knowledge Distillation for Deep Face Recognition
Therefore, we seek to probe the target logits to extract the primary knowledge related to face identity, and discard the others, to make the distillation more achievable for the student network.
Masked Relation Learning for DeepFake Detection
A relation learning module masks partial correlations between regions to reduce redundancy and then propagates the relational information across regions to capture the irregularity from a global view of the graph.
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns.
Unsupervised Domain Adaptation GAN Inversion for Image Editing
In this paper, we resolve this problem by introducing Unsupervised Domain Adaptation (UDA) into the Inversion process, namely UDA-Inversion, for both high-quality and low-quality image inversion and editing.
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement
Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images.
Action Shuffling for Weakly Supervised Temporal Localization
Weakly supervised action localization is a challenging task with extensive applications, which aims to identify actions and the corresponding temporal intervals with only video-level annotations available.
Stereo Plane SLAM Based on Intersecting Lines
Adding such plane features in stereo SLAM system reduces the drift error and refines the performance.
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations
We also propose an anomaly detection method to identify the target class in a Trojaned network.
Ranked #1 on
Adversarial Defense
on TrojAI Round 1
On Deep Unsupervised Active Learning
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.
Object-oriented SLAM using Quadrics and Symmetry Properties for Indoor Environments
As objects are often observed locally, the proposed algorithm uses the symmetrical properties of indoor artificial objects to estimate the occluded parts to obtain more accurate quadric parameters.
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization
The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance.
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis.
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
The key of this task is to model feature interactions among different feature fields.
Ranked #9 on
Click-Through Rate Prediction
on Avazu
Coarse-To-Fine Visual Localization Using Semantic Compact Map
Robust visual localization for urban vehicles remains challenging and unsolved.
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
To achieve the alignment, we minimize the distances between distributions with unsupervised adversarial learning, and also the distances between some annotated compatible items which play the role of anchor points to help align.
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions
However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views.
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
Robotics I.2.9; I.2.0
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit".
Ranked #1 on
Recommendation Systems
on Polyvore
(Accuracy metric)
Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision
Action recognition in videos has attracted a lot of attention in the past decade.
Recurrent Calibration Network for Irregular Text Recognition
In this paper, we propose a novel Recurrent Calibration Network (RCN) for irregular scene text recognition.
Socially Aware Kalman Neural Networks for Trajectory Prediction
Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles.
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
To handle the problem of embedding secret messages into the specific medium, we design a novel adversarial modules to learn the steganographic algorithm, and simultaneously train three modules called generator, discriminator and steganalyzer.
SSGAN: Secure Steganography Based on Generative Adversarial Networks
Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images.
A Self-Paced Regularization Framework for Multi-Label Learning
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime.
