## 4 Experimental Validation
### Results and Discussion
#### 4.4.2 Outdoor RGB Canopy Images  
Table 5 shows the foreground IoU for various methods on the outdoor RGB canopy image dataset under different manual annotation times. Here, the manual annotation time denotes the time needed to annotate the training set used to learn the pseudo-label generator. It includes both the time to manually correct the errors affecting the current model prediction, and the (computational) time to update the model. Table 6 presents the corresponding pixel accuracy. The results indicate that the data-reinforced CNN* consistently outperforms the methods that are solely trained on manually labeled data. This demonstrates that leveraging  
Figure 3: Qualitative results on pollen test dataset. See Table 4 for model names definition. All models are trained using 1 labeled image. Red contour is ground truth. unlabeled data through pseudo-labels is beneficial. Note that the quality of pseudo-labels is measured by the test performance obtained by models trained from manually labeled data. Without surprise, for a given pseudo-label generator model, the higher the pseudo-label quality, the higher the CNN* quality. A similar observation can be made in Figure 4a, where each CNN* model achieves test performance that is generally superior to that obtained by the model from which the CNN* training pseudo-labels have been predicted. Hence, we conclude that resorting to pseudo-labels helps in reducing the human annotation workload. This is confirmed by Figure 4b, which compares the pixel accuracy on the test set between the CNN* model and its corresponding pseudo-label generator. The points in the figure are consistently located above the diagonal, emphasizing that leveraging unlabeled data through pseudo-labels improves the model quality compared to a model trained only on manually labeled samples. Figure 4a also clearly reveals that the boost obtained by the CNN* compared to the model used to predict the pseudo-labels is much larger for CNN(iGBT) and sCNN than for CNN(sRP) and sRF. For example, when the model trained on manually labeled samples reaches about 83%, CNN*_CNN(iGBT) reaches 94.5%, while CNN*_sRF only reaches 88.6%. This observation was not expected, and is thus quite interesting. This unexpected discrepancy is attributed to the combination of two factors. First, compared to CNN(sRP), the sRF, CNN(iGBT), and sCNN models benefit from more diverse manually labeled samples since those samples are not randomly selected but instead chosen interactively as image parts where the current state of the model fails. Second, compared to sRF, CNN(iGBT) and sCNN build on a CNN model, which is known to have sufficient expressivity to capture a large annotation diversity without regularizing it [54], i.e., without aggregating the annotation cues through average probabilities, as done by Bayesian models like random ferns or random forests. While the enriched representation captured by a CNN model does not necessarily translate into improvements on the test images (e.g., 83% test accuracy both for the CNN(iGBT) and sRF models), it proves to be beneficial when  
Figure 4: (**a**) Test accuracy vs. manual annotation time on RGB canopy images, for the models defined in Table 1. (**b**) CNN* test accuracy vs. test accuracy obtained by its corresponding pseudo-label generator on RGB canopy images, for the models defined in Table 1.  
that are representative of typical simple (no occlusion, reasonably stable appearance) and complex (shape variations and occlusions) object image segmentation problems. Through experiments across two datasets, we discover that integrating unlabeled data via pseudo-labels emerges as a potent strategy, markedly enhancing model efficacy compared to models trained solely on manually labeled samples. We extensively investigate the relation between (i) the strategy adopted to collect manual annotations and turn them into a pseudo-label generator, and (ii) the benefit drawn from pseudo-labels. Our study underscores the significance of pseudo-label diversity.