Original: Doubly Right Object Recognition
Revised: Doubly Right Object Recognition

Original: Abstract
Revised: Abstract

Original: Existing deep neural networks are optimized to predict the right thing, yet they may rely on the wrong evidence.
Revised: Existing deep neural networks are optimized to predict the right thing, yet they may rely on the wrong evidence.

Original: Using the wrong evidence for prediction undermines out-of-distribution generalization, underscoring the gap between machine perception and human perception.
Revised: Using the wrong evidence for prediction undermines out-of-distribution generalization, underscoring the gap between machine perception and human perception.

Original: In this paper, we introduce an overlooked but important problem: “doubly right object recognition,” which requires the model not only to predict the right outcome, but also to use the right reasons that are aligned with human perception.
Revised: In this paper, we introduce an overlooked but important problem: “doubly right object recognition,” which requires the model not only to predict the right outcome, but also to use the right reasons that are aligned with human perception.

Original: The existing benchmark fails to learn or evaluate the doubly right object recognition task, because both the right reason and spurious correlations are predictive of the final outcome.
Revised: The existing benchmarks fail to learn or evaluate the doubly right object recognition task, because both the right reason and spurious correlations are predictive of the final outcome.

Original: Without additional supervision and annotation for what is the right reason for recognition, doubly right object recognition is impossible.
Revised: Without additional supervision and annotation for what is the right reason for recognition, doubly right object recognition is impossible.

Original: To address this, we collect a dataset, which contains annotated right reasons that are aligned with human perception and train a fully interpretable model that only uses the attributes from our collected dataset for object prediction.
Revised: To address this, we collect a dataset, which contains annotated right reasons that are aligned with human perception and train a fully interpretable model that only uses the attributes from our collected dataset for object prediction.

Original: Through empirical experiments, we demonstrate that our method can train models that are more likely to predict the right thing with the right reason, providing additional generalization ability on ObjectNet, and demonstrating zero-shot learning ability.
Revised: Through empirical experiments, we demonstrate that our method can train models that are more likely to predict the right thing with the right reason, providing additional generalization ability on ObjectNet, and demonstrating zero-shot learning ability.

Original: 1. Introduction
Revised: 1. Introduction

Original: Deep neural networks focus on predicting the right “what” in the image, yet they often ignore the correctness of “why” for the predictions
Revised: Deep neural networks focus on predicting the right “what” in the image, yet they often ignore the correctness of “why” for the predictions

Original: (He et al., 2016; Simonyan & Zisserman, 2015; Dosovitskiy et al., 2021; Tan & Le, 2019).
Revised: (He et al., 2016; Simonyan & Zisserman, 2015; Dosovitskiy et al., 2021; Tan & Le, 2019).

Original: Using the right reason for the prediction is important, especially in critical applications, such as medical imaging (Singh et al.
Revised: Using the at the ICML 2022 Workshop on Spurious Correlations,

Original: , 1 Anonymous Institution, Anonymous City, Anonymous Region,
Revised: Baltimore

Original: Anonymous Country.
Revised: Maryland USA.

Original: Correspondence to: Anonymous Author < anon.email@domain.com > . Preliminary work. Under review by the SCIS workshop.
Revised: Copyright 2022 by the author(s).

Original: [Figure]
Revised: [Figure]

Original:  2020; Ancona et al., 2018; Eitel & Ritter, 2019; Pereira et al., 2018).
Revised: right reason for the prediction is important, especially in critical applications, such as medical imaging (Singh et al., 2020; Ancona et al., 2018; Eitel & Ritter, 2019; Pereira et al., 2018).

Original: Existing methods investigate how to get the reasons that contribute to the models’ prediction (Nguyen et al., 2016; Olah et al., 2017; Simonyan et al., 2014; Shrikumar et al., 2017; Zeiler & Fergus, 2014; Smilkov et al., 2017;
Revised: Existing methods investigate how to get the reasons that contribute to the models’ prediction (Nguyen et al., 2016; Olah et al., 2017; Simonyan et al., 2014; Shrikumar et al., 2017; Zeiler & Fergus, 2014; Smilkov et al., 2017;

Original: Selvaraju et al., 2016).
Revised: Selvaraju et al., 2016).

Original: However, due to the spurious correlations in the dataset, not every evidence is right (Singla &
Revised: However, due to the spurious correlations in the dataset, not every evidence is right (Singla &

Original: Feizi, 2022).
Revised: Feizi, 2022).

Original: Existing classifiers have no knowledge to discriminate which evidence is right or not, which causes the model to often predict the right thing for the wrong reason.
Revised: Existing classifiers have no knowledge to discriminate which evidence is right or not, which causes the model to often predict the right thing for the wrong reason.

Original: While using the wrong evidence can help in-distribution accuracy, generalization gets difficult when the distribution shifts (Mao et al., 2021).
Revised: While using the wrong evidence can help in-distribution accuracy, generalization gets difficult when the distribution shifts (Mao et al., 2021).

Original: In this paper, we introduce the doubly right object recognition problem.
Revised: In this paper, we introduce the doubly right object recognition problem.

Original: As shown in figure 1, besides predicting the correct “what” is the image, we also desire the prediction to be based on the correct “why”.
Revised: As shown in figure 1, besides predicting the correct “what” is the image, we also desire the prediction to be based on the correct “why”.

Original: We propose a dataset benchmark evaluating doubly right object recognition.
Revised: We propose a dataset benchmark evaluating doubly right object recognition.

Original: Existing attribute datasets (Pham et al., 2021;
Revised: Existing attribute datasets (Pham et al., 2021;

Original: Bau et al., 2017) contain untrimmed attributes, which include nuisance concepts that are spuriously correlated with the prediction.
Revised: Bau et al., 2017) contain untrimmed attributes, which include nuisance concepts that are spuriously correlated with the prediction.

Original: To address this, we collect a new attribute prediction dataset (section 2) that annotates the right attributes, which are aligned with human perception, for object classification.
Revised: To address this, we collect a new attribute prediction dataset (section 2) that annotates the right attributes, which are aligned with human perception, for object classification.

Original: To ensure that the model only bases the prediction on the obtained reasons, our model first linearly transforms the learned representation to attributes that are aligned with human perception, and then learns a linear read-out layer using the transformed feature.
Revised: To ensure that the model only bases the prediction on the obtained reasons, our model first linearly transforms the learned representation to attributes that are aligned with human perception, and then learns a linear read-out layer using the transformed feature.

Original: The model’s prediction is totally interpretable because it can only be based on the attribute features that correspond to a semantic concept.
Revised: The model’s prediction is totally interpretable because it can only be based on the attribute features that correspond to a semantic concept.

Original: Empirical experiments and visualizations show that our approach can predict the right thing for the right reasons.
Revised: Empirical experiments and visualizations show that our approach can predict the right thing for the right reasons.

Original: On our annotated attributes of the validation set images, we obtain a recall@7 for the correct attribute of 68.78% when the prediction is correct.
Revised: On our annotated attributes of the validation set images, we obtain a recall@7 for the correct attribute of 68.78% when the prediction is correct.

Original: In addition, with the right prediction reason, we can improve generalization ability on the outof-distribution benchmark as well as conducting zero-shot classification.
Revised: In addition, with the right prediction reason, we can improve generalization ability on the outof-distribution benchmark as well as conducting zero-shot classification.

Original: We will release our data, code, and model.
Revised: We will release our data, code, and model.

Original: The major contributions of our paper are: 1) we propose a new benchmark task “doubly right object recognition” and a new dataset that allows training and evaluation on our proposed task, 2) demonstrate the additional advantage that “doubly right object recognition” can improve the generalization in out-of-distribution generalization and zero-shot learning setting.
Revised: The major contributions of our paper are: 1) we propose a new benchmark task “doubly right object recognition” and a new dataset that allows training and evaluation on our proposed task, 2) demonstrate the additional advantage that “doubly right object recognition” can improve the generalization in out-of-distribution generalization and zero-shot learning setting.

Original: 2. Doubly Right object recognition: a new dataset and benchmark
Revised: 2. Doubly Right Object Recognition: Training Set and Benchmark

Original: Relying on spurious attributes can harm generalization when the spurious correlations are changed in a new environment.
Revised: Relying on spurious attributes can harm generalization when the spurious correlations are changed in a new environment.

Original: However, without additional assumptions or constraints, the model will learn to use spurious correlations for the prediction.
Revised: However, without additional assumptions or constraints, the model will learn to use spurious correlations for the prediction.

Original: In other words, it is theoretically impossible to learn the right attribute for the prediction rather than the spurious ones without additional knowledge (Mao et al., 2022).
Revised: In other words, it is theoretically impossible to learn the right attribute for the prediction rather than the spurious ones without additional knowledge (Mao et al., 2022).

Original: Our goal is to use minimal human effort to enable the model to learn the right ones.
Revised: Our goal is to use minimal human effort to enable the model to learn the right ones.

Original: For this, we first list the core attributes for each class, and then automatically collect images containing the attributes using Google image search.
Revised: For this, we first list the core attributes for each class, and then automatically collect images containing the attributes using Google image search.

Original: Dataset collection details.
Revised: Dataset collection details.

Original: We manually annotated a few descriptive attributes for each of the 40 object classes in our classification categories.
Revised: We manually annotated a few descriptive attributes for each of the 40 object classes in our classification categories.

Original: Using our prior human knowledge, we ensured that we only annotate those visual attributes for an object that actually describe the object thus avoiding spurious attributes.
Revised: Using our prior human knowledge, we ensured that we only annotate those visual attributes for an object that actually describe the object thus avoiding spurious attributes.

Original: For example, for ‘bike’ the annotated attributes are wheel, seat, beam, handlebar, metal, etc..
Revised: For example, for ‘bike’ the annotated attributes are wheel, seat, beam, handlebar, metal, etc.

Original: Unlike general attributes like tree, ground, road , which could be spuriously correlated with ‘bike’ in images, our small set of causal attributes only describe the object and are not spurious.
Revised: Unlike general attributes like tree, ground, road , which could be spuriously correlated with ‘bike’ in images, our small set of causal attributes only describe the object and are not spurious.

Original: In total, we have 135 causal attributes with 6.
Revised: In total, we have 135 causal attributes with 6.

Original: attributes on average per object class.
Revised: attributes on average per object class.

Original: Once we have the lists of causal attributes for object classes, we do image search over  internet to collect attribute training images.
Revised: Once we have the lists of causal attributes for object classes, we do image search over the internet to collect attribute training images.

Original: For example, we have tire attribute for ‘bike’ object class.
Revised: For example, we have tire attribute for ‘bike’ object class.

Original: We search ‘bike tire’ images over  internet and collect  top 50 image results.
Revised: We search ‘bike tire’ images over the internet and collect the top 50 image results.

Original: These images are then used to train attribute prediction model to predict the attribute tire .
Revised: These images are then used to train attribute prediction model to predict the attribute tire .

Original: Similarly, for a ‘car’ too, we collect images by searching ‘car tire’ and add them to  tire attribute training set.
Revised: Similarly, for a ‘car’ too, we collect images by searching ‘car tire’ and add them to the tire attribute training set.

Original: This way we collect a total of 11,335 images for 135 attributes.
Revised: This way we collect a total of 11,335 images for 135 attributes.

Original: Some of the example training images are shown in figure 4.
Revised: Some of the example training images are shown in figure 4.

Original: Benchmark Different images, even if they are from the same category, contain different sets of attributes.
Revised: Benchmark Different images, even if they are from the same category, contain different sets of attributes.

Original: To validate the precision of the predicted reason from the model, we annotate eachindividual image in the validation set with attributes.
Revised: To validate the precision of the predicted reason from the model, we annotate each image in the validation set with attributes.

Original: We use Amazon Mechanical Turk to crowdsource the attribute annotation.
Revised: We use Amazon Mechanical Turk to crowdsource the attribute annotation.

Original: We provide image - attribute pairs to the turkers and for each pair, we ask if the given attribute is applicable to the given object in the image or not.
Revised: We provide image-attribute pairs to the turkers and for each pair, we ask if the given attribute is applicable to the given object in the image or not.

Original: Note that, in the image-attribute pairs we show to turkers, we only use those causal attributes (from the lists we created earlier) that are useful for describing the object in the image.
Revised: Note that, in the image-attribute pairs we show to turkers, we only use those causal attributes (from the lists we created earlier) that are useful for describing the object in the image.

Original: For example, when we give an image of a ‘bike’, we only ask for attributes (like wheel, handlebar ) that describe a ‘bike’.
Revised: For example, when we give an image of a ‘bike’, we only ask for attributes (like wheel, handlebar ) that describe a ‘bike’.

Original: We do not ask turkers to annotate attributes like headlight/windshield for a ‘bike’ image.
Revised: We do not ask turkers to annotate attributes like headlight/windshield for a ‘bike’ image.

Original: This way, we get a list of attributes for each image that actually describe the object in the image.
Revised: This way, we get a list of attributes for each image that actually describe the object in the image.

Original: We use annotations from 3 turkers for each image-attribute pair and take a majority vote to decide if the attribute is present in the image.
Revised: We use annotations from 3 turkers for each image-attribute pair and take a majority vote to decide if the attribute is present in the image.

Original: Finally, we haveimages belonging to our 40 classification object categories annotated with causal attributes having an average of 5.
Revised: Finally, we haveimages belonging to our 40 classification object categories annotated with causal attributes having an average of 5.

Original: attributes per image (some examples are shown in Fig. 4).
Revised: attributes per image (some examples are shown in Fig. 4).

Original: We use this benchmark dataset to verify if our method is actually predicting right for the right reasons or is it using the wrong reasons to predict (see Fig. 3).
Revised: We use this benchmark dataset to verify if our method is actually predicting right for the right reasons or is it using the wrong reasons to predict (see Fig. 3).

Original: Evaluation metric.
Revised: Evaluation metric.

Original: We use 3 attribute recall based metrics to find if a classification model is able to predict  right reasons for object classification.
Revised: We use 3 attribute recall-based metrics to find if a classification model is able to predict the right reasons for object classification.

Original: Average Recall@k: calculated as  fraction of annotated attributes in top-k predicted attributes for an image and averaged over all test set images 2.
Revised: Average Recall@k: calculated as the fraction of annotated attributes in top-k predicted attributes for an image and averaged over all test set images.

Original: Strict DoublyRight score : calculated as (cid:80) Recall R / N , where Recall R is attribute recall for rightly predicted image and N is the number of images in the test set.
Revised: Strict DoublyRight score : calculated as (cid:80) Recall R / N , where Recall R is attribute recall for rightly predicted image and N is the number of images in the test set.

Original: Relaxed DoublyRight score : calculated as ( (cid:80) Recall R + (cid:80) (1 − Recall W ))
Revised: Relaxed DoublyRight score : calculated as ( (cid:80) Recall R + (cid:80) (1 − Recall W ))

Original: / N where Recall W is  attribute recall for wrongly predicted images.
Revised: / N where Recall W is the attribute recall for wrongly predicted images.

Original: Both DoublyRight score metrics favor methods that predict both correct objects and their correct reasons.
Revised: Both DoublyRight score metrics favor methods that predict both correct objects and their correct reasons.

Original: Strict DoublyRight score favors more accurate methods as it completely penalizes wrong predictions, while Relaxed DoublyRight score favors more interpretable methods because counting (1-recall) for wrongly predicted images tells that the method predicted wrong because it found wrong reasons in the image.
Revised: Strict DoublyRight score favors more accurate methods as it completely penalizes wrong predictions, while Relaxed DoublyRight score favors more interpretable methods because counting (1-recall) for wrongly predicted images tells that the method predicted wrong because it found wrong reasons in the image.

Original: 3. Experiments
Revised: 3. Experiments

Original: We train an interpretable object classification model on our attribute prediction dataset that first learns to rotate the representation in a neural network into the space of interpretable attributes, and then trains a linear classifier using the interpretable concepts.
Revised: We train an interpretable object classification model on our attribute prediction dataset that first learns to rotate the representation in a neural network into the space of interpretable attributes, and then train a linear classifier using the interpretable concepts.

Original: We first evaluate our method’s performance on our proposed doubly right object recognition task. We then validate on two established generalization task: out-
Revised: We first evaluate our method’s perfor-

Original: [Figure]
Revised: [Figure]

Original: Method
Revised: handlebarroundtirebeamrubbermovingwindshieldhandle [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: makes choices
Revised: Model

Original: [Table]
Revised: [Table]

Original: DoublyRight
Revised: DoublyRight

Original: [Table]
Revised: [Table]

Original: of-distribution generalization and zero-shot learning, where we demonstrate that our approach improves generalization by capturing the right reason.
Revised: mance on our proposed doubly right object recognition task. We then validate on two established generalization tasks: out-of-distribution generalization and zero-shot learning, where we demonstrate that our approach improves generalization by capturing the right reason.

Original: 3.0.1. I NTERPRETABILITY
Revised: 3.1. Interpretability

Original: Once the object classification linear model is trained, we can analyze the learned attribute weights to find ‘what does the model think about the object class?’ 
Revised: Once the object classification linear model is trained, we can analyze the learned attribute weights to find ‘what does the model think about the object class?’ By looking at the

Original: By looking at the predicted attributes and the learned weights for the predicted object class, we can explain why the model has made its choice.
Revised: [Figure] predicted attributes and the learned weights for the predicted object class, we can explain why the model has made its choice.

Original: As seen in figure 2, the model classifies the image[Figure] as a ‘mountain bike’.
Revised: As seen in figure 2, the model classifies the image as a ‘mountain bike’.

Original: As we can see the learned attribute weights (in red) for ‘mountain bike’, the model thinks that a mountain bike image should have the following attributestwo wheels, seat, handlebar, tire, riding, beam .
Revised: As we can see the learned attribute weights (in red) for ‘mountain bike’, the model thinks that a mountain bike image should have the following attributestwo wheels, seat, handlebar, tire, riding, beam .

Original: It predicts attributes like - riding, two wheels, handlebar, round, beam in the image.
Revised: It predicts attributes like - riding, two wheels, handlebar, round, beam in the image.

Original: Since these attributes match the description of ‘mountain bike’ learned by the model weights, the model predicts the image is of ‘mountain bike’.
Revised: Since these attributes match the description of ‘mountain bike’ learned by the model weights, the model predicts the image is of ‘mountain bike’.

Original: This way, the model gives its reasons for its decision in terms of the predicted attributes which makes the model interpretable and its predictions explainable. 
Revised: This way, the model gives its reasons for its decision in terms of the predicted attributes which makes the model interpretable and its predictions explainable. [Table]

Revised: [Figure]

Original: Right reasons lead to right predictions.
Revised: Right reasons lead to right predictions.

Original: Wrong reasons lead to wrong predictions.
Revised: Wrong reasons lead to wrong predictions.

Original: Using the benchmark dataset of annotated attributes for the validation set images, we

Original: [Figure] [Table] [Figure] show that our model gives right predictions when it predicts right attributes and gives wrong predictions when it predicts wrong attributes.
Revised: Using the benchmark dataset of annotated attributes for the validation set images, we show that our model gives right predictions when it predicts right attributes and gives wrong predictions when it predicts wrong attributes.

Original: We plot attribute recall@7, calculated by finding the number of annotated attributes in top-7 predicted attributes, for both correctly and incorrectly classified images in figure 3.
Revised: We plot attribute recall@7, calculated by finding the number of annotated attributes in top-7 predicted attributes, for both correctly and incorrectly classified images in figure 3.

Original: We find that, for most of the images that are correctly classified, the correct attribute recall is high.
Revised: We find that, for most of the images that are correctly classified, the correct attribute recall is high.

Original: While for most of the images that are incorrectly classified, the correct attribute recall is low.
Revised: While for most of the images that are incorrectly classified, the correct attribute recall is low.

Original: We also evaluate ourDoublyRight method on the evaluation metrics defined in section 2 and find that it performs well (table 1).
Revised: We also evaluate our method on the evaluation metrics defined in section 2 and find that it performs well (table 1).

Original: This shows that our model is 
Revised: This shows that our model is actually Doubly Right- gives right predictions when its reasons for the predictions are right.

Original: 3.0.2. B ETTER GENERALIZABLE : O BJECT N ET PERFORMANCE
Revised: 3.2. Improved Generalization: ObjectNet Performance

Original: Visual attributes of objects do not change much with changes in orientation, viewpoint and background.
Revised: Visual attributes of objects do not change much with changes in orientation, viewpoint and background.

Original: For example, a ‘cup’ would still be made of ceramic material, have a cylindrical shape and have a handle to hold even if it is placed upside down in an unusual surrounding like  bathroom.
Revised: For example, a ‘cup’ would still be made of ceramic material, have a cylindrical shape and have a handle to hold even if it is placed upside down in an unusual surrounding like a bathroom.

Original: Since our model uses attributes for object classification, it is able to generalize on out-of-distribution dataset ObjectNet (Barbu et al., 2019) which has test images of objects with diverse viewpoints and backgrounds.
Revised: Since our model uses attributes for object classification, it is able to generalize on out-of-distribution dataset ObjectNet (Barbu et al., 2019) which has test images of objects with diverse viewpoints and backgrounds.

Original: We evaluate the performance of our method on 2,723 ObjectNet images belonging toobject classes that are common between ObjectNet and our 40 object classes.
Revised: We evaluate the performance of our method on 2,723 ObjectNet images belonging to 16 object classes that are common between ObjectNet and our 40 object classes.

Original: We found that our method outperforms baseline Resnet50 (trained to predict our 40 classes) by 2.6% in terms of top-1 classification accuracy, thus showing better generalizability (figure 2).
Revised: We found that our method outperforms baseline Resnet50 (trained to predict ourclasses) by 2.6% in terms of top-1 classification accuracy, thus showing better generalizability (figure 2).

Original: This shows that an interpretable model using right attributes for classification could help in generalizability on out-of-distribution images.
Revised: This shows that an interpretable model using right attributes for classification could help in generalizability for out-of-distribution images.

Original: 3.0.3. Z ERO SHOT LEARNING ABILITY
Revised: 3.3. Zero-Shot Learning

Original: As our object classification model is able to learn attributes for objects, we can manually provide attributes for unseen objects and our classification model can identify the object without even a single training example, thus having zeroshot learning ability.
Revised: As our object classification model is able to learn attributes for objects, we can manually provide attributes for unseen objects and our classification model can identify the object without even a single training example, thus having zeroshot learning ability.

Original: To enable our model to classify a new object class, we manually describe the new object class using our existing set of learned attributes, and add a new set of attribute weights for it to the existing model (see Fig. 5).
Revised: To enable our model to classify a new object class, we manually describe the new object class using our existing set of learned attributes, and add a new set of attribute weights for it to the existing model (see Fig. 5).

Original: For example, We manually provide attributes of a ‘green apple’ (unseen category not in the 40 training classes) as a list of attributes =
Revised: For example, we manually provide attributes of a ‘green apple’ (an unseen category not in the 40 training classes) as a list of attributes =

Original: [ green, spherical, round, shiny ].
Revised: [ green, spherical, round, shiny ].

Original: We then add a new weight vector for ‘green apple’ to the already trained weights of the classifier such that the weights of the attributes not in the above list are zero and the weights of the attributes present in the above list are equal to the mean of the weights of the top-7 attributes of the existing 40 categories.
Revised: We then add a new weight vector for ‘green apple’ to the already trained weights of the classifier such that the weights of the attributes not in the above list are zero and the weights of the attributes present in the above list are equal to the mean of the weights of the top-7 attributes of the existing 40 categories.

Original: Thus the values in the new weight vector describe the attributes of ‘green apple’ numerically.
Revised: Thus the values in the new weight vector describe the attributes of ‘green apple’ numerically.

Original: We evaluated our approach on validation set images of a few of the unseen ImageNet classes (in table 3) and found that it performs well even with minimal information provided in terms of visual attributes.
Revised: We evaluated our approach on validation set images of a few of the unseen ImageNet classes (in table 3) and found that it performs well even with minimal information provided in terms of visual attributes.

Original: This shows that the in-built interpretability of our model could be easily adapted for zero-shot classification.
Revised: This shows that the in-built interpretability of our model could be easily adapted for zero-shot classification.

Original: 4. Conclusion
Revised: 4. Conclusion

Original: We study the problem of doubly right object recognition, where the classifier not only need to classify the image correctly, but also need to use the right reason to make the prediction.
Revised: We study the problem of doubly right object recognition, where the classifier not only needs to classify the image correctly, but also needs to use the right reason to make the prediction.

Original: We collect a new dataset that allows the training and benchmarking on the doubly right prediction task.
Revised: We collect a new dataset that allows the training and benchmarking on the doubly right prediction task.

Original: Our work is the first one that shows the importance of doubly right prediction, and connects models’ better interpretation ability to improved generalization.
Revised: Our work is the first one that shows the importance of doubly right prediction, and connects models’ better interpretation ability to improved generalization.