The human visual system processes images with varied degrees of resolution,
with the fovea, a small portion of the retina, capturing the highest acuity
region, which gradually declines toward the field of view's periphery. However,
the majority of existing object localization methods rely on images acquired by
image sensors with space-invariant resolution, ignoring biological attention
mechanisms.
As a region of interest pooling, this study employs a fixation prediction
model that emulates human objective-guided attention of searching for a given
class in an image. The foveated pictures at each fixation point are then
classified to determine whether the target is present or absent in the scene.
Throughout this two-stage pipeline method, we investigate the varying results
obtained by utilizing high-level or panoptic features and provide a
ground-truth label function for fixation sequences that is smoother,
considering in a better way the spatial structure of the problem.
Finally, we present a novel dual task model capable of performing fixation
prediction and detection simultaneously, allowing knowledge transfer between
the two tasks. We conclude that, due to the complementary nature of both tasks,
the training process benefited from the sharing of knowledge, resulting in an
improvement in performance when compared to the previous approach's baseline
scores.

研究了利用仿生学注意机制的物体识别方法，通过采用集成注意力、分类和定位的双重任务模型，利用人类注意力系统分辨图像中的感兴趣区域。