title: Correlation of Object Detection Performance with Visual Saliency and
    Depth Estimation
summary: As object detection techniques continue to evolve, understanding their
  relationships with complementary visual tasks becomes crucial for optimising
  model architectures and computational resources. This paper investigates the
  correlations between object detection accuracy and two fundamental visual
  tasks: depth prediction and visual saliency prediction. Through comprehensive
  experiments using state-of-the-art models (DeepGaze IIE, Depth Anything,
  DPT-Large, and Itti's model) on COCO and Pascal VOC datasets, we find that
  visual saliency shows consistently stronger correlations with object detection
  accuracy (mArho up to 0.459 on Pascal VOC) compared to depth prediction
  (mArho up to 0.283). Our analysis reveals significant variations in these
  correlations across object categories, with larger objects showing correlation
  values up to three times higher than smaller objects. These findings suggest
  incorporating visual saliency features into object detection architectures
  could be more beneficial than depth information, particularly for specific
  object categories. The observed category-specific variations also provide
  insights for targeted feature engineering and dataset design improvements,
  potentially leading to more efficient and accurate object detection systems.
paper_authors._id: 672b05326a68baea5e5605c1
paper_authors.name: Matthias Bartolo
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-11-06T05:57:22.713Z
paper_authors._id: 664ae70790135abe9ba76c28
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/664ae70790135abe9ba76c28/S9j90H0hjdJwf4x1kXIV0.png
paper_authors.fullname: Matthias Bartolo
paper_authors.type: user
paper_authors.user: mbar0075
paper_authors._id: 672b05326a68baea5e5605c2
paper_authors.name: Dylan Seychell
paper_title: Correlation of Object Detection Performance with Visual Saliency and
    Depth Estimation
paper_summary: As object detection techniques continue to evolve, understanding their
  relationships with complementary visual tasks becomes crucial for optimising
  model architectures and computational resources. This paper investigates the
  correlations between object detection accuracy and two fundamental visual
  tasks: depth prediction and visual saliency prediction. Through comprehensive
  experiments using state-of-the-art models (DeepGaze IIE, Depth Anything,
  DPT-Large, and Itti's model) on COCO and Pascal VOC datasets, we find that
  visual saliency shows consistently stronger correlations with object detection
  accuracy (mArho up to 0.459 on Pascal VOC) compared to depth prediction
  (mArho up to 0.283). Our analysis reveals significant variations in these
  correlations across object categories, with larger objects showing correlation
  values up to three times higher than smaller objects. These findings suggest
  incorporating visual saliency features into object detection architectures
  could be more beneficial than depth information, particularly for specific
  object categories. The observed category-specific variations also provide
  insights for targeted feature engineering and dataset design improvements,
  potentially leading to more efficient and accurate object detection systems.
paper_ai_summary: Visual saliency prediction shows stronger correlations with object detection accuracy compared to depth prediction, varying significantly across object categories.
paper_ai_keywords: object detection depth prediction visual saliency prediction DeepGaze IIE Depth Anything DPT-Large Itti's model COCO Pascal VOC mA$\rho$ feature engineering dataset design improvements