Attention moduleFeature droppingFeature diversityOccluded person re-identificationAttention mechanism is widely employed in Person Re-Identification task to allocate the weight of features. However, most of the existing attention-based methods focus on the region of interest but ignore other potential ...
Yes. Different sums are expected due to dropout, but dropout shouldn't change the values that are already there. Since we are dropping out a softmaxed tensor over dim=-1, we would expect that the value after dropout to still be between 0 and 1 and sum over dim=-1 to an amount smal...
The Laptop14 and Restaurant15 datasets showed particularly severe performance degradation, with the accuracy and F1 metrics dropping by 4.25% and 4.74%, respectively, for the Laptop14 dataset, and the F1 metric dropping by 4.04% for the Restaurant15 dataset, indicating that the interaction between ...
The results show significant improvements, with the RMSE dropping to 0.004223°, MAE to 0.003021°, ADE to 0.002436°, and FDE to 0.003946°. Compared to G9, the RMSE is reduced by 3.5%, and ADE by 4.7%, demonstrating that the gating mechanism helps the model better utilize spatial ...
From Figure 19, we observed that the bounding box_loss, classification_loss, and objectness_loss of the training set exhibits a dropping state initially and then stabilized after certain epochs. Among the object detection models, the proposed MAFFN_YOLOv5 model’s loss value was found to be ...
Figure 7. Visualization results of the detection of five defects in the adhesive line of a cell phone frame: (a) Single Tip Wall Climbing; (b) Glue Dropping; (c) Broken Glue; (d) Wall Climbing; (e) Collapsed Glue. The YOLO-FIX model demonstrates significant strengths in overcoming cha...
Figure 7. Visualization results of the detection of five defects in the adhesive line of a cell phone frame: (a) Single Tip Wall Climbing; (b) Glue Dropping; (c) Broken Glue; (d) Wall Climbing; (e) Collapsed Glue. The YOLO-FIX model demonstrates significant strengths in overcoming cha...
Figure 7. Visualization results of the detection of five defects in the adhesive line of a cell phone frame: (a) Single Tip Wall Climbing; (b) Glue Dropping; (c) Broken Glue; (d) Wall Climbing; (e) Collapsed Glue. The YOLO-FIX model demonstrates significant strengths in overcoming cha...
Figure 5. Dropping a low-scoring detection frame causes a trajectory break; (a) 77th frame; (b) 91st frame; (c) 95th frame. In this paper, we set the confidence threshold of the detection boxes to 0.01 to keep the low-scoring detection boxes, and for the high-scoring detection boxe...
Figure 5. Dropping a low-scoring detection frame causes a trajectory break; (a) 77th frame; (b) 91st frame; (c) 95th frame. In this paper, we set the confidence threshold of the detection boxes to 0.01 to keep the low-scoring detection boxes, and for the high-scoring detection boxe...