SlowGuess/NeurIPS_2023_OCR huggingface dataset:
As shown in Table 7, the results from SSL are highly dependent on both the model architecture and whether the abstention method is distance-based or not. For example, distance-based methods perform better on Must-Abstain categories when paired with Swin Transformer. Unlike other abstention methods, Deep Ensemble generally performs better in all groups regardless of the model architecture. Note that even if the same abstention method is used, the effects on the performance are reversed depending on the model architecture used. As an example, when TAPUDD is combined with Swin Transformer, the performance increases on all Must-Abstain categories and decreases on all Must-Act categories, but the performance difference is reversed when TAPUDD is combined with DenseNet instead.
Overall, Deep Ensemble helps increase visual alignment performance in both ImageNet pre-training and SSL. However, other abstention functions did not show noticeable performance increases in both cases. In short, the same findings in previous studies on robustness and OOD detection can not be directly applied to visual alignment. This implies visual alignment has its unique challenges that differentiate from robustness and OOD detection tasks, and there is much room for developing new methods for better visual alignment. In general, KNN shows the best visual alignment score in all three tables (Table 3, Table 6, Table 7). This may be due to using detailed features when calculating abstention probability. However, it is hard to find a consistency for optimal model architecture. For example, in Table 3, Swin Transformer and DenseNet, which have different architectures, have the
best performance on average across all seven abstention functions. Therefore, more research on finding the optimal model architecture in visual alignment is needed.
I Discussion on Uncertainty
I.1 Continuity of Uncertainty
In this section, we will discuss a critical aspect of uncertainty which is continuity. Uncertainty is continuous and it is challenging to draw clear distinctions among classes (i.e., as we mentioned in the main paper that it is hard to distinguish between "car" and "truck") and between clear and uncertain images (i.e., if at least one person claims an image as "uncertain", then it becomes an uncertain image). However, as our ultimate goal is to construct a universal testing benchmark that quantitatively measures visual perception alignment between models and humans, our classes should have clear definitions so that model developers can easily prepare their models and training strategy. Therefore, after careful consideration, we used the taxonomy classification in biology which is the meticulous product of decades of efforts by countless domain experts to hierarchically distinguish each species as accurately as possible with clear definitions. Also, in order to comprehensively measure the visual perception alignment between models and humans, the models should be tested under various conditions including clear in-class images (Must-Act), clear out-of-class images (Must-Abstain) and confusing images (Uncertain). As there is no clear boundary between clear and uncertain images, the best scenario would be to survey all images in our dataset to 134 people per image to obtain numerically reliable annotations. However, surveying all images is not always feasible as it requires tremendous amount of time and money considering that there are 1800 images (900 each in the open and closed test sets) in our dataset. Therefore, due to the realistic reasons, we put significant effort to include only clear images that anyone can agree on in Must-Act and Must-Abstain and obtained human annotations on Uncertain images. Nevertheless, we also recognize that continuity is an essential characteristic of uncertainty that should be carefully considered and there is always a possibility of corner cases that may be disagreeable by at least one person. We have done our best to remove those corner case samples and cross-validated our final selection. Further detailed analysis and benchmark dataset on the continuity of uncertainty is highly needed and we will leave this as a future work.
I.2 Coverage of Uncertainty
"Uncertainty" is a broad concept and it is hard to define with one clear line and list all possible cases. In this paper, we chose 15 different types of corruptions to generate uncertainty in various ways following a concrete previous work [22]. Furthermore, to better represent the continuity of uncertainty explained in Appendix I.1, we apply the corruptions varying severity ranging from 1 to 10. Many types of corruption we used resemble the reality in their own way. For example, adjusting the brightness of the image is certainly realistic, and changing its resolution is similar to viewing an object beyond a filter (e.g., semi-transparent glass), and weather changes are also certainly realistic. These corruptions result in some of realistic uncertain images, precisely  $8.5\%$  in the case of the open test set, where MTurk survey participants were struggling with differentiating between two or more animals (rather than being confused between one animal and abstention). Despite our meticulous effort, we are well aware that those corruptions certainly do not cover all possible uncertainties that arise in the real world. However, "uncertainty" is too broad to specify and difficult to collect or generate, and hence for now we use corruptions (but sufficiently diverse types of corruptions).
J Detailed Comparisons with Previous Works
In this section, we will explain in detail how our work differs from related previous works. Our ultimate goal is to create a rigorous test (similar to tests that humans take such as college entrance exams) to quantitatively measure the visual perception gap between the models and humans across various categories. Our main interest does not lie in training but on rigorously testing the visual perception alignment. For that purpose, a dataset should satisfy the four requirements we mentioned in Section 3 and use a proper metric that reflects the visual perception alignment.
Peterson et al. [52] and Schmarje et al. [65] utilized their datasets mainly for training and did not thoroughly verify whether the model actually achieved visual alignment. Peterson et al. [52] only tested their models on in-class samples (in our case, Category 1) and out-of-class samples (in our case, Category 4 and Category 6) and they showed only accuracy and cross entropy, which is analogous to KL divergence. Therefore, they did not test their models on various possible scenarios and did not use proper measurement, as KL divergence is not an optimal choice for visual perception alignment as we described in Section 4.1. Schmarje et al. [65] only tested their models on in-class samples (in our case, Category 1) and showed accuracy and KL divergence. Therefore, although previous works trained their models with the goal of achieving visual perception alignment, none of the works have thoroughly verified how much the models have actually achieved visual perception alignment under diverse situations with an appropriate measurement.
Zhang et al. [76] and Bomatter et al. [5] are similar to our work since they show that both models and humans have better object recognition when given more contextual information, but it is difficult to say that they have comprehensively evaluated visual perception alignment. These two works only tested their models on partial aspects (in our case, Category 1, Category 2, and Category 8). Thus, these works did not test on Must-Abstain samples, which makes it difficult to claim that they "comprehensively" evaluated visual perception alignment. Zhang et al. [76] and Bomatter et al. [5] simply showed that both models and humans exhibit similar performance trends based on context (i.e., when given more context, both human's and model's visual recognition performance increases), and they provided human-model correlations to describe their relative trends across conditions. However, our study on visual perception alignment is not about following human trends, but about measuring how well the model replicates human perception sample-wise. Hence, considering our research scope and criteria, it's challenging to assert that Zhang et al. [76] and Bomatter et al. [5] rigorously measured visual perception alignment.
In contrast, we quantitatively measured visual perception alignment across various scenarios with multiple human annotations on uncertain images. In addition, we borrowed Hellinger distance to precisely calculate the visual perception alignment after careful consideration of other distance-based metrics such as KL divergence and Total Variation distance. Furthermore, we incorporated specialized elements (sampling theory, statistical theories related to survey design, and experts in the related fields) in creating our dataset.
There are three key differences that distinguish our dataset compared to existing datasets that also focus on uncertainty in object recognition. First, we applied corruption and cropping with different intensities ranging from 1 to 10 to reflect the continuity of uncertainty mentioned in Appendix I.1. Uncertainty is continuous and it is critical to test models on samples where uncertainty may increase in stages. In this sense, we tested models visual perception alignment on varying degrees of uncertainty. Second, we obtained 134 human annotations per image to accurately estimate the ground truth visual perception distribution. We borrowed statistical sampling theory to achieve an error bound of lower than  $5\%$ , of which the details are in Section 3.3. Third, while our uncertain samples include uncertainty that confuses between classes, refer refer to as "inter-class uncertainty" (soft labels distributed only among target classes), we also include "recognizability uncertainty" (soft labels distributed among classes + abstention), namely whether an image itself is recognizable or not. If an image is moderately brightened (i.e., intermediate phase between a clear image and a complete white image), then the object itself may or may not be recognizable. Visual perception includes not only object identification (predicting that it is an elephant) but also object recognizability (the object itself is recognizable). In this sense, we cover broader scenarios compared to previous works as we include object recognizability uncertainty in our uncertain category.
We also want to highlight that VisAlign does only contain Uncertain but also Must-Act and Must-Abstain to cover diverse scenarios as possible. In order to evaluate a model's visual perception alignment, a model should be tested under Must-Act (whether it predicts a correct class with high confidence), Must-Abstain (whether it abstains out-of-class samples with high confidence), and Uncertain (whether it reflects the human uncertainty). However, previous works are limited in that they test their model on partial cases (Category 1 and Category 4 in Peterson et al. [52], and Category 1 in Schmarje et al. [65]) which does not truly reflect visual perception alignment on various situations. It is especially important to test models on samples from out of distributions (i.e., Category 5 and Category 7), but previous works overlook these samples thus did not quantitatively evaluate from visual perception alignment. Therefore, their dataset cannot be utilized as a benchmark to evaluate visual perception alignment. While previous papers and our work have in common with handling uncertainty,
in our case, uncertain samples are a subset of our final dataset and we cover more diverse necessary situations, which previous works do not, as possible to measure the visual perception alignment.