Summary
This paper presents a method for stress-testing visual classifiers. The key idea is to use language to generate counterfactual images. Their approach can be summarized into four steps: 1) take an image input; 2) get its caption from a captioning model (BLIP-2); 3) perturb the caption by changing some words using ChatGPT and fine-tuned LLAMA; 4) generate new images based on perturbed captions using text inversion of Stable Diffusion. On the ImageNet dataset, they showed that various models such as ResNet-50 and ViT-B consistently achieve much lower performance on their generated new ImageNet compared to the original ImageNet, and the generated ImageNet enables surfacing model biases.
Strengths
1. This paper targets a novel direction --- stress-test visual models, which is important in real-world vision applications.
2. The proposed method is technical sound, and the generated images from their pipeline look good and realistic.
3. The paper conducted experiments on the realistic ImageNet dataset instead of simple synthetic datasets, showing the practical values of their method in realistic scenarios.
4. This paper is generally well-written and easy to understand.
Weaknesses
1. There is no baseline to compare, and the numbers are hard to justify their method's effectiveness. The main quantitive results from this paper showed that various vision models achieve much lower performance on their generated ImageNet dataset. However, it is unclear whether the generated images are 100% correct. If the text perturbation happens to change the class or introduce new classes to the image, the model prediction is expected to change and the performance will drop. Therefore, a human study showing the label validity of generated images is particularly necessary.
2. There are some existing works proposing very similar directions. For example, [1] is almost the same as this work, which uses textual reversion to convert an image into a token and compose this token with natural language and generate new images to test vision models. [2] uses off-the-shelf image captioning and generation models to discover model bugs. While [1] can be viewed as concurrent work, it would be helpful if the authors could discuss the similarity and differences between this work and [1-2].
3. From Figure 6 last row, changing the caption from "car" to "bus" generates almost the same images, but model predictions change a lot. The change may be because generated images have specific patterns that are not perceptible to humans, which cannot reflect the actual errors using real images.
4. The stress test will be limited by language itself, as language is hard to represent certain properties of objects, such as orientation and lighting. However, this is also pointed out by the authors and is minor considering the advantage of using language.
[1] Dataset interfaces: Diagnosing model failures using controllable counterfactual generation. https://arxiv.org/pdf/2302.07865.
[2] Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning. https://arxiv.org/pdf/2208.08831.
Questions
- Algorithm 1:
- Line 7 duplicate definition of "c" (previously used as the class, now used as the caption)
- Line 13: ">" should be "<"
- Equation 2: "|" in the denominator should be "||"
- Line 282: what is the percentage for the generated counterfactuals to be semantically inconsistent with the ground truth label?
Rating
7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
See weaknesses. I'm happy to increase my rating if these are well addressed, especially 1 and 2.