Advances in Image Classification and Object Detection

The field of computer vision is moving towards addressing issues of bias and uncertainty in image classification and object detection. Researchers are developing innovative methods to analyze and mitigate intersectional biases in image classification, which can lead to significant improvements in accuracy and fairness. Another area of focus is the development of techniques for quantifying and reducing uncertainty in vision-based perception, particularly in safety-critical applications such as autonomous driving. Additionally, there is a growing interest in applying computer vision to real-world problems, such as intelligent traffic signaling and traffic surveillance. Notable papers in this area include:

  • A paper that presents a data-driven framework for analyzing and mitigating intersectional biases in image classification, which improves accuracy for underrepresented class-environment intersections by up to 24 percentage points.
  • A paper that introduces ObjectTransforms, a technique for quantifying and reducing uncertainty in vision-based object detection, which yields notable accuracy improvements and uncertainty reduction across all object classes.

Sources

Data-Driven Analysis of Intersectional Bias in Image Classification: A Framework with Bias-Weighted Augmentation

ObjectTransforms for Uncertainty Quantification and Reduction in Vision-Based Perception for Autonomous Vehicles

Towards Intelligent Traffic Signaling in Dhaka City Based on Vehicle Detection and Congestion Optimization

A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance

Built with on top of