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Found 12 papers, 1 papers with code
Diffusion Deepfake
To address this critical issue, we investigate the impact of enhancing training data diversity on representative detection methods.
Present-Biased Lobbyists in Linear Quadratic Stochastic Differential Games
We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm.
Structure-Aware Surface Reconstruction via Primitive Assembly
We propose a novel and efficient method for reconstructing manifold surfaces from point clouds.
Dont Even Look Once: Synthesizing Features for Zero-Shot Detection
Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable.
Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts
In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are required.
Generalized Zero-Shot Recognition based on Visually Semantic Embedding
To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is "visually semantic."
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
We consider the problem of fine-grained classification on an edge camera device that has limited power.
Person Re-Identification in Identity Regression Space
Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time.
Deep Reinforcement Learning Attention Selection for Person Re-Identification
Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping.
Human-In-The-Loop Person Re-Identification
Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate.
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available.
