In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed
recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to …

This paper tackles the cross-modality person re-identification (re-ID) problem by
suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images …

In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

Despite the success of the deep neural networks, it remains challenging to effectively build a
system for long-tail visual recognition tasks. To address this problem, we first investigate the …

Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …