Venue: NeurIPS 2022 Track Datasets and Benchmarks

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: Hard-ImageNet is a dataset with a formulized a task and metrics aimed towards solving an image recognition model unwanted behavior referred to as reliance on spurious cues. This phenomena happens when class labels, in a classification problem, happen to have significant correlation with background features, lightning, textures, and other characteristics. The problem arises when the object appears elsewhere on test/production inference data and that's where the generalization degrades.

The metrics suggested here leverage noise, ablation, and saliency methods to quantify model's resiliency to spurious cues on a given dataset.

Demonstration on convolutional neural nets as well as transformers is provided.

Analysis on spatial localization of such class labels vs. other ImageNet objects.
strengths: I believe that this work would help understand neural nets better and I find the concept very interesting and impactful for many domains at this field.

The paper illustrates the phenomena and methods with good visualizations.

The comparison of top spurious feature presence vs. non-presence on ImageNet shown in Figure[10] is strong. I'd highlight it, perhaps a teaser image could be derived from it.

The project website along the dedicated GitHub repo provides great support to reproducible science.
weaknesses: To me the paper was hard to follow. I believe that with some text editing, without changing the content, it would be much easier to read.
It begins with the introduction where a definition in simple words would help any reader understand the challenge the authors try to solve better. In terms  of storytelling I'd try to lead the reader from motivation in each chapter to the details. Some sentences where unnecessarily complex.
correctness: Solid
clarity: There's room for improvement in the clarity of this manuscript. As mentioned in the weaknesses, I found it hard to follow due to over complication of sentences and non-intuitive paper structuring.
The storytelling in this paper is sub-optimal which could be easily improved as the content of the paper is very interesting.
relation to prior work: Looks ok.
additional feedback: Other than the class-labels 'Horizontal bar' and 'Balance beam', no two classes share background visual features. Taking multiple objects from the same habitat would inherently minimize the spurious cue factor. It might be interesting to quantify and illustrate on a graph. Y axis would be one evaluation metric while X would be the number of objects that share the same background in the training data.
review_title: Very interesting topic with potential impact on many domains. The paper's clarity is not high.

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: - The authors have found fascinating observations of 15 classes in the ImageNet dataset (namely - Dog Sled, Howler Monkey, Seat Belt, Ski, Sunglasses, Swimming Cap, Balance Beam, Gymnastic, Horizontal Bar, Patio, Hockey Puck, Miniskirt, Keyboard Space Bar, Volleyball, Baseball Player, and Snorkel) that rely on spurious features which lead to a decrease in generalization of the model. These objects are generally less centred and smaller as they contain much background in the image. For this, the authors introduced segmentation masks for these images of Imagenet, which are hard-imagenet objects to be recognized by the model.

- They compared Finetuned and pre-trained resnet50 and vision transformer (small DeiT) model for Hard Imaenet objects, RIVAL10, RIVAL20 by taking cases for input image to the model like the original image, grayed object so that it hides the object in the image to be predicted, bbox grayed by creating gray bounding box to even don’t let the model predict through the shape of the object, and bounding box tiled to retain as much background as possible. Also, the saliency maps shown in the paper indicate that the model focuses on background regions, not the object.

- They annotate each image in the training set with a class-wise rank corresponding to the strength of spurious features present in the image based on the neural features of each class. Samples are classified with low accuracy if their spurious rank is low. 

strengths: - This data can help the classification models focus on even smaller objects in the image that are hardly distinguishably visible with a large amount of background.
- It can lead to robust models in future research without being much affected by extensive backgrounds and focus precisely on objects to be classified.

weaknesses: - The paper shows baselining on very few models. Have authors tried other models also which were trained on Imagenet, like versions of VGG and Densenet? Do they also do a similar thing, like not focusing on an object, as shown in this paper?
- No segmentation maps for other classes, just 15 classes of 1000 classes is a tiny chunk of a dataset compared to ImageNet. Do other class samples like kite, snake and many more also deviate from focus, is this checked?


- If we use pre-trained segmentation models, then it can generate segmentation masks like this paper can be used: 
                       “Segmentation propagation in ImageNet”, D Kuettel, M Guillaumin, V Ferrari, ECCV-2012.
- No Conclusion in the paper.
- Paper just mentioned about ObjectNet at starting, but no results were shown for ObjectNet images. 
          
correctness: - Interesting observations were noted in the models trained on the ImageNet dataset and a good attempt to create a segmentation mask for objects in the image to deal with the problem of not focussing on smaller and less centred objects.
- Claims made in the paper seem correct; the proof was given by showing saliency maps indicating the model's focus in images.

clarity: - The paper is written well and clearly. The claims made are appropriate.
relation to prior work: 
 - It is mentioned in the paper how 15 classes show less-centred and very small objects with extensive backgrounds in the ImageNet dataset.
- Segmentation models have not been explored to create segmentation masks for the ImageNet dataset classes.

additional feedback: - Conduct more experiments on other models trained on ImageNet to show baselining results.
- Explore segmentation models for annotating masks.
- Write Conclusion in the paper.
- Show experimental results on the ObjectNet dataset also.
review_title: An interesting observation for models to focus on less-centred and significantly smaller objects

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper proposes a novel dataset, Hard ImageNet, which is comprised of a curation of 15 classes from ImageNet with very strong spurious cues, as well as their segmentation masks. The dataset is the first of its kind to provide samples containing natural spurious cues in general image domains. The classes are selected using an up-to-date automated saliency method and annotations are carefully collected based on a stringent protocol. The paper also provides a curated suite of evaluations methods for spurious cues in datasets where segmentation masks are available. Experimental results strongly suggest that the proposed dataset contains a high level of spurious cues compared to a typical image dataset counterpart–RIVAL10. Furthermore, the authors present a clear case of how Hard ImageNet can be used to train models to be more robust to spurious cues, going as far as to present experimental results in the Appendix. 
strengths: - The proposed dataset provides a curation of class samples with strong spurious cues selected using up-to-date empirical methods, thus relevant in the current literature.
- Hand-annotated foreground segmentation is provided which can serve as an important tool for analysis and method development regarding spurious cues.
- Extensive documentation on the dataset is provided and the soundness of the data collection procedure is very convincing.
- The quality standards for the data collection procedure is very high and stringent measures were employed to enforce these standards, such as qualification exams and intermediate attention checks.
- The experimental results using the curated evaluation suite are very consistent and clearly support the need for the proposed dataset.
- The paper is very well-written.
weaknesses: - Justification for using the method from [28] for ranking spurious cues is not provided in relation to alternative methods. Is the method used in the paper the best choice compared to other methods?
- In line 139, the authors state that they *compile* suite of evaluation metrics, suggesting that the metrics have been proposed in previous works. However, there is no citation provided for the Ablation method in Section 4.1.
correctness: All claims appear to be correct.

The construction of the dataset is thoroughly documented in Section 3 and Appendix D and is convincingly sound. The authors impose a high standard of data quality for their collection procedure. There are no noticeable issues in the experiment design.
clarity: The writing of this paper is excellent and easy to follow.
relation to prior work: The paper provides thorough discussion of related works, and motivation for the proposed dataset as well as differences with previous contributions are clear.
additional feedback: - Typo in Line 190
- Writing is *mildly* hard to follow in lines 83-85 (indirect connection between l2 adversarial training and spurious neural feature) and line 86 (warrants clarification that vast majority of class-features pairs were deemed core *in general*).
- The justification for the use of the method from [28] to measure the spuriousness of the dataset could be tested by comparing the class ranking derived from [28] to a class ranking derived from the proposed evaluation metrics.
- Discussion regarding existing object detection datasets containing object segmentation annotations may be relevant to the scope of the paper. Possible avenues include:
  - Comparison with HardImageNet segmentations for improving models as in Appendix C. I.e., could segmentations from typical non-spurious datasets achieve the same level of spurious feature reliance?
  - Use of additional datasets for justification of the spurious class-ranking method based on [28], in the aforementioned manner (third bullet point).

- It was pleasantly surprising to see the care devoted to the working environment of data annotators. Not only does this improve the well-being of workers, but such a transparent and open environment would arguably make a crucial difference in the quality of the resulting data.
review_title: Important contribution and good paper

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary and contributions: The authors propose 1) a dataset consisting of 15 imageNet classes for which they have collected object segmentations and established a ranking based on the strength of spurious signals present as well as 2) an evaluation suite of ablation-, noise-, and saliency-based analyses that exploit the object segmentation to assess the extent to which models rely on spurious features.

The annotated examples were chosen because they are, according to the authors, typical examples of images whose class prediction made by a model will be based on spurious cues and not on the real object.
strengths: The paper focuses on an interesting problem consisting in better understanding the predictions made by a model and proposes to annotate a part of a classical vision dataset to facilitate research on this topic.
weaknesses: The weaknesses of this work intersect for me with the points listed in section "Correctness"
correctness: - I have some reservations about my ability to see if the dataset is built in a sound way. Indeed, the selection of images to annotate is based on a previous work [28] available only on arxiv. This criterion is from what I understand the only criterion taken into account to define which are examples with very strong spurious clues. 
- Regarding the suite of evaluation metrics, the authors state l.173 that "Hard ImageNet images are predicted to their class over 50% of the time under all ablations, despite the fact that the object essential to the class label is actually missing, rendering the predictions incorrect". My impression is that there are not enough arguments to justify that the prediction should be considered incorrect. Indeed, by combining the description made of the boxes with the images given in Figure 1, I have the intuition that for some images given the limited possibility of classes that can be predicted the classification would not necessarily be wrong. In other words, what would have been the correct classification in the case where the space bar is hidden on the 2nd figure from the left in the last row of Figure 1? 
clarity: The paper is well written. A conclusion section would have been a nice addition.
relation to prior work: Yes, the authors discussed how their work differ from previous contributions
additional feedback: N/A
review_title: Dataset to evaluate model predictions on examples with strong spurious cues

final_score: rating: null
review_content: Dear Authors, 

The link indicated for the dataset - [mmoayeri.github.io/HardImagenet](mmoayeri.github.io/HardImagenet) - returns me a 404 error. As a reviewer, do we have to do anything special to access it? 

Best
review_title: mmoayeri.github.io/HardImagenet returns a 404 error

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: In general, this is an interesting paper that tackles the challenge in learning with strong spurious cues. It is known that deep classifiers can rely on spurious features, which jeopardize the generalization ability. A well-collected dataset is necessary for formally tackling the problem. This paper identifies 15 classes in ImageNet with strong spurious cues and collect high-quality segmentation map for the images. The dataset not only contains images for validation. With 19097 images for training, it further opens the venue for more advanced methods that can leverage such information during training to reduce reliance on spurious cues. The author provides a detailed description of data collection, and benchmarking metrics and the results are solid and convincing. The author also shed some light on the characteristic of the hard ImageNet objects (i.e. smaller and less centered). To this end, ranking images by spurious feature presence are conducted and further used in the methods described in the appendix to achieve improved performance. 

strengths: 1. The challenge in learning with strong spurious cues is a important topic to tackle
2. The dataset annotation collection is convincing and of high quality, the sufficient training set size would also enable developing better training methods
3.  Comprehensive benchmarks metrics and experiments are conducted to compared different models, the results are convincing;
4. A analysis of the segmentation masks to reveal how the shape, size and location influence the sensitivity;
5.  Class-wise rankings for each image in the Hard ImageNet training set based on the strength  of spurious cues are provided, which can be useful for analysis and developing more advanced methods.
weaknesses: The author mentioned, "While these ablated images are out of distribution, accuracy drop due to ablation can still inform the degree to which the model relies on spurious background features". However, it has been pointed out in [1] that " Without re-training, it is unclear whether the degradation in model performance comes from the distribution shift or because the features that were removed are truly informative". In their experiment, they found " It turns out that removing information is quite hard. With 90% of the inputs removed the network still achieves 63.53% accuracy compared to 76.68% on clean data. This implies that a strong performance degradation without re-training might be caused by a shift in distribution instead of removal of information. " Though the author used the finetuned version via a lightweighted last-layer training, it would be interesting to see whether the gap narrows down when re-training is conducted as in [1]. Don't take me wrong, I think the finetuning setting is still interesting, it certainly reflects that the Spurious information are easier to be used by the model to some extent. Nevertheless, re-training can provide additional information


It would be better if the main paper and the appendix can be reorganized so that the main paper itself is in a self-contained manner. It appears to me that some important content that should be included in the main paper is put into the appendix. 





[1]Hooker, Sara, et al. "A benchmark for interpretability methods in deep neural networks." Advances in neural information processing systems 32 (2019).
correctness: The claims look fine to me. The description of data collection is complete and detailed. Experimental details and evaluation metrics are also well documented. 
clarity: This paper is easy to follow, though I would suggest a reorganization of the main paper and appendix. It occurs to me that some important content is put in the appendix, which makes the main paper not quite self-contained. 
relation to prior work: The description of the comparison to related work are sufficient . 
additional feedback: So the comments above.
review_title: Nice dataset about spurious Cues

Meta-review: metareview: The paper has gone through the ethics review. As per the suggestion of the ethics reviewer, the authors confirmed with the IRB of their institute that IRB approval is not needed for the study. The paper thus does not have serious ethical concerns.

After the discussion period, the paper obtains an average rating of 6.2 from 5 reviewers. Most reviewers like this paper. They find the analysis provided in this paper interesting and novel, highlighting the implications of spurious features in recognition. They also appreciate the new subset of 15 ImageNet classes that are labeled with segmentation masks. The dataset is documented well.

Reviewers such as RHtp and NpX5 asked for an improvement in clarity. The authors improved the paper accordingly. The authors also clarified the selection of the 15 classes and the ablation. Reviewers are happy with the rebuttal and revised content.

Reviewer 7A1M is the only reviewer who does not recommend acceptance (rating = 5) after the discussion period. After checking the rebuttal, the AE believes the authors have addressed the concerns of Reviewer 7A1M, by including the additional experiments and missing conclusion. The current dataset and analysis of the paper are still meaningful without the segmentation annotations on other ImageNet classes and the results on ObjectNet. 
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept