Correlation of Object Detection Performance with Visual Saliency and
  Depth Estimation
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.
672b05326a68baea5e5605c1
Matthias Bartolo
extracted_confirmed
2024-11-06T05:57:22.713Z
664ae70790135abe9ba76c28
https://cdn-avatars.huggingface.co/v1/production/uploads/664ae70790135abe9ba76c28/S9j90H0hjdJwf4x1kXIV0.png
Matthias Bartolo
user
mbar0075
672b05326a68baea5e5605c2
Dylan Seychell
Correlation of Object Detection Performance with Visual Saliency and
  Depth Estimation
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.
Visual saliency prediction shows stronger correlations with object detection accuracy compared to depth prediction, varying significantly across object categories.
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