Transferability Metrics for Object Detection

• URL: http://arxiv.org/abs/2306.15306v1
• Date: Tue, 27 Jun 2023 08:49:31 GMT
• Title: Transferability Metrics for Object Detection
• Authors: Louis Fouquet, Simona Maggio, L\'eo Dreyfus-Schmidt
• Abstract summary: Transfer learning aims to make the most of existing pre-trained models to achieve better performance on a new task in limited data scenarios. We extend transferability metrics to object detection using ROI-Align and TLogME. We show that TLogME provides a robust correlation with transfer performance and outperforms other transferability metrics on local and global level features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning aims to make the most of existing pre-trained models to achieve better performance on a new task in limited data scenarios. However, it is unclear which models will perform best on which task, and it is prohibitively expensive to try all possible combinations. If transferability estimation offers a computation-efficient approach to evaluate the generalisation ability of models, prior works focused exclusively on classification settings. To overcome this limitation, we extend transferability metrics to object detection. We design a simple method to extract local features corresponding to each object within an image using ROI-Align. We also introduce TLogME, a transferability metric taking into account the coordinates regression task. In our experiments, we compare TLogME to state-of-the-art metrics in the estimation of transfer performance of the Faster-RCNN object detector. We evaluate all metrics on source and target selection tasks, for real and synthetic datasets, and with different backbone architectures. We show that, over different tasks, TLogME using the local extraction method provides a robust correlation with transfer performance and outperforms other transferability metrics on local and global level features.

Related papers

• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Mean-AP Guided Reinforced Active Learning for Object Detection [31.304039641225504]
  This paper introduces Mean-AP Guided Reinforced Active Learning for Object Detection (MGRAL) MGRAL is a novel approach that leverages the concept of expected model output changes as informativeness for deep detection networks. Our approach demonstrates strong performance, establishing a new paradigm in reinforcement learning-based active learning for object detection.
  arXiv  Detail & Related papers  (2023-10-12T14:59:22Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Towards Estimating Transferability using Hard Subsets [25.86053764521497]
  We propose HASTE, a new strategy to estimate the transferability of a source model to a particular target task using only a harder subset of target data. We show that HASTE can be used with any existing transferability metric to improve their reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that HASTE modified metrics are consistently better or on par with the state of the art transferability metrics.
  arXiv  Detail & Related papers  (2023-01-17T14:50:18Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)
• Transformer-based assignment decision network for multiple object tracking [0.0]
  We introduce Transformer-based Assignment Decision Network (TADN) that tackles data association without the need of explicit optimization during inference. Our proposed approach outperforms the state-of-the-art in most evaluation metrics despite its simple nature as a tracker.
  arXiv  Detail & Related papers  (2022-08-06T19:47:32Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• Transferability Estimation using Bhattacharyya Class Separability [37.52588126267552]
  Transfer learning is a popular method for leveraging pre-trained models in computer vision. It is difficult to quantify which pre-trained source models are suitable for a specific target task. We propose a novel method for quantifying transferability between a source model and a target dataset.
  arXiv  Detail & Related papers  (2021-11-24T20:22:28Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)
• Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization [53.99850033746663]
  We study the problem of learning localization model on target classes with weakly supervised image labels. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function.
  arXiv  Detail & Related papers  (2020-03-18T17:53:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.