most citedOrthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning

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cs.CV2024

Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds

Yanbiao Ma, Licheng Jiao, Fang Liu +5

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is p…

cs.CV2024

Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification

Yanbiao Ma, Licheng Jiao, Fang Liu +3

Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the generalization ability of the model. Although numerous approaches of class re-bal…

cs.CV2023

Data-Centric Long-Tailed Image Recognition

Yanbiao Ma, Licheng Jiao, Fang Liu +3

In the context of the long-tail scenario, models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to impr…

cs.CV2023

SoccerNet 2023 Challenges Results

Anthony Cioppa, Silvio Giancola, Vladimir Somers +99

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vi…

cs.CV2023

Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds

Yanbiao Ma, Licheng Jiao, Fang Liu +6

To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are w…