Coreset Selection for Object Detection: Addressing the Challenges of Multi-Object and Multi-Label Scenarios

Core Concepts

A novel coreset selection method, CSOD, that addresses the challenges of multi-object and multi-label scenarios in object detection tasks, outperforming random selection and existing coreset selection methods designed for image classification.

Abstract

The paper introduces a new approach called Coreset Selection for Object Detection (CSOD) to address the limitations of traditional coreset selection methods, which were designed under the assumption of a single object per image. CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image and adopts submodular optimization to consider both representativeness and diversity when selecting a subset.

The key highlights and insights are:

1. CSOD recognizes the presence of numerous objects within each image and tackles the compounded uncertainties by considering objects' spatial information such as size and location.
2. The 'imagewise-classwise vector' serves as a comprehensive representation to enable informed decision-making when addressing the complexities of multi-object images.
3. CSOD employs a greedy approach to select individual data points sequentially by class order, ensuring the most pertinent selections for each class are made, enhancing the representativeness and diversity of the coreset.
4. Submodular optimization is introduced to select the most informative subset based on the imagewise-classwise average features for each category.
5. Empirical evaluations, particularly in scenarios involving the detection of multiple objects, demonstrate the effectiveness of CSOD. For instance, when selecting 200 images from the Pascal VOC dataset, CSOD achieved an impressive improvement of +6.4% point in AP50 compared to random selection.
6. CSOD's effectiveness is further validated on the BDD100k and MS COCO2017 datasets, outperforming random selection.
7. Experiments show that CSOD's performance advantage holds across different network architectures, including Faster R-CNN, RetinaNet, and FCOS.

Stats

The number of annotations in the 200 images selected by CSOD is 1,032, while the number of annotations in the 200 images selected by random selection is 806.
The size ratio (small, medium, large) of the 200 images selected by CSOD is (7.3%, 38.2%, 54.5%), which is closer to the size ratio of the entire training data (10.5%, 32.0%, 57.5%) compared to the random selection (10.2%, 37.2%, 52.6%).

Quotes

"CSOD not only recognizes the presence of numerous objects within each image but also tackles the compounded uncertainties by considering objects' spatial information such as size and location."
"The 'imagewise-classwise vector' serves this purpose by averaging the features of objects of the same class within an image. This comprehensive representation allows for informed decision-making when addressing the complexities of multi-object images."
"When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP50 when selecting 200 images."

Key Insights Distilled From

Coreset Selection for Object Detection

by Hojun Lee,Su... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09161.pdf

Deeper Inquiries

How can the CSOD method be extended to handle images with varying numbers of objects across different classes

To handle images with varying numbers of objects across different classes, the CSOD method can be extended by incorporating a dynamic selection process. Instead of selecting a fixed number of images per class, the method can adaptively adjust the number of images selected based on the distribution of objects in each class. This can be achieved by introducing a weighting mechanism that considers the imbalance in object counts across classes. By assigning different weights to classes based on the number of objects present, the selection process can prioritize classes with fewer objects to ensure a more balanced representation in the coreset. Additionally, the method can incorporate a mechanism to dynamically adjust the selection criteria based on the object distribution in each image, ensuring that images with a higher number of objects are adequately represented in the coreset.

What are the potential implications of the CSOD approach for dataset distillation in the context of object detection tasks

The implications of the CSOD approach for dataset distillation in object detection tasks are significant. Dataset distillation aims to synthesize a smaller, more informative dataset from a larger dataset, thereby improving model training efficiency. By leveraging the CSOD method for dataset distillation, it is possible to select a subset of images that effectively capture the diversity and representativeness of the original dataset. This can lead to the creation of a distilled dataset that retains the essential characteristics of the original data while reducing redundancy and noise. The CSOD approach can enhance dataset distillation by ensuring that the selected subset contains a diverse range of objects across different classes, leading to improved model generalization and performance.

How might the CSOD method be adapted to leverage background features in addition to the object-level features to further enhance the coreset selection process

To adapt the CSOD method to leverage background features in addition to object-level features, the selection process can be modified to consider the contextual information present in the background of images. This can be achieved by incorporating background feature extraction techniques into the feature representation process. By extracting features from both the objects of interest and the background regions, the method can create a more comprehensive representation of each image. The selection criteria can then be adjusted to prioritize images with informative background features that complement the object-level features. This integration of background features can enhance the coreset selection process by capturing contextual information that may influence object detection performance.