Visual Data Diagnosis and Debiasing with Concept Graphs

We thank the reviewer for the positive comments.

Clarification on the ablation studies: Since the concept graph phase in ConBias is not a learning mechanism, we circumvent the need for initialization or training strategies. This is beneficial as we achieve both controllability and interpretability of the bias diagnosis process. The imbalanced cliques provide an intuitive mechanism to diagnose the spurious correlations in the data. With regard to ablation studies, since the augmented data generation phase is a learning mechanism (we use stable diffusion with a CLIP based filter), here we present ablation results (Table 3). We show that using a different generative model such as IP2P still results in significant improvements over the baselines. This result demonstrates that it is actually the bias diagnosis and clique balancing strategy of ConBias that proves to be the true novelty.

Graph structure initialization: We thank the reviewer for referring to the Ultra-KD paper. Broadly, such an approach excites us immensely, where a knowledge graph encapsulates deep information about the data. For instance, in lines 317 and lines 324, we mention the potential of future work in developing more novel graph structures that can capture diverse biases beyond object co-occurrences. Foundational knowledge graphs with rich relational information may prove to be the next logical step in dataset diagnosis and debiasing. We will add this discussion to the final version, and thank the reviewer for raising this idea.

Thank you for the encouraging review and the detailed feedback.

Extension to multi-class tasks and scalability: ConBias can be conveniently extended to the multi-class setup. For this, we note the definitions in line 142 and line 146. While the common clique computation remains the same, for the imbalanced clique computation, instead of taking the pairwise absolute difference between classes, we can use a different metric such as variance and entropy to estimate the degree of imbalance among multiple classes. Functionally, the imbalanced set would contain the same information, i.e. how disproportionately is one concept combination (1-clique, 2-clique, etc.) represented with respect to a particular class relative to the other classes? In addition, in the attached PDF for the rebuttal (Figure 1), we also show how ConBias can diagnose biases in a complex multi-class dataset such as Imagenet-1k. We will add this discussion to the final version and thank the reviewer for the insightful question.

For scalability, we invite the reviewer to refer to Section H in the Supplementary section for details on runtime. In general, given $K$ classes and $C$ concepts, the graph clique enumeration is expected to grow in $O(exp(|K+C|)$. However, in practice, we find that constraining clique sizes to $k<=4$ leads to interpretable bias combinations, with no significant effect of the exponential runtime. We agree that this is a heuristic, and more efficient clique enumeration methods can be developed (lines 314-315). We will update the paper to reflect this point raised by the reviewer.

Comparison to debiasing methods based on the feature space: ConBias is a data-centric method rather than a model-centric method. We wish to intervene in the data directly rather than using a specific model as a proxy (lines 80-83). Once the biases are diagnosed, we can controllably and reliably generate fair data that improves model debiasing and generalization capabilities on downstream tasks.

A direct benchmarking against feature based methods would not be feasible since it is difficult to control for the effect of the augmented dataset. This is why our baselines, such as ALIA, also do not compare against feature-based debiasing methods. To ensure a fair comparison for data debiasing techniques, we need to ensure that the effect of adding data to the training set is adjusted for. In a pure feature debiasing based setup, this would be hard to control. However, the reviewer raises an important point about the dependence between data and feature debiasing. We agree that these two are not mutually exclusive. In fact, this is why we present results in Figure 6 where we visualize the classifier feature heatmaps after retraining. We demonstrate that the features learned by the classifier are indeed the core features, and not spurious features. On each dataset in Figure 6, we show that the classifier focuses on the object of interest (the core features), and not the background/gender/co-occurring object - which are precisely the spurious cues.

Evaluation metrics: In Table 4, we evaluate the multi-shortcut abilities of ConBias. Multiple shortcut mitigation is a recent robustness problem introduced by Li, Zhiheng, et al. "A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. UrbanCars is the standard dataset for evaluating multiple shortcut robustness, with BG-Gap, CoObj-Gap, and BG+CoObj-Gap as the evaluation metrics. Both Waterbirds and COCO-GB are single shortcut datasets, and as such, these metrics do not apply to these datasets.

Upsampling: The rebalance sampling algorithm receives the concept graph as a concept co-occurrence matrix. The algorithm iterates through all cliques with an order of decreasing clique sizes to make sure we would not double compensate for the imbalance, e.g., 3-cliques would impact the already-balanced 2-cliques if we operate in a bottom-up fashion. For each iteration, it retrieves all cliques of concepts of size k along with their corresponding frequencies with each class. The algorithm identifies the maximum co-occurrence count among all classes for each combination and checks if any class is under-represented by comparing its count with the maximum. If a class is under-represented, the algorithm computes the number of synthetic samples needed to balance the representation and adds this information to the results list. This process continues for all combinations and classes until all clique sizes have been processed. The output of the algorithm is a list of queries specifying the class, concept combination, and the number of samples needed to balance the dataset. We will include a pseudo-code in the final version for better readability.

We thank the reviewer for the valuable comments.

Metadata: As we discuss in the main paper (lines 324-325), we assume the availability of reliable ground truth concept sets. Such annotations already exist for the datasets we investigate - Waterbirds, UrbanCars, and COCO-GB. We agree that unreliable ground truth concept sets would hinder generalization abilities, but this assumption is not dissimilar to the assumption of reliable ground truth labels in classification tasks. Moreover, the reliance on ground truth concept sets, sometimes referred to as concept banks, have also been considered in [A]. Ground truth concept sets serve as auxiliary knowledge bases and provide human level interpretability to the task at hand. We look forward to using partially available concept sets in future work (lines 324-325). We will update the paper to reflect this important point raised by the reviewer.

[A] Wu, Shirley, et al. "Discover and cure: Concept-aware mitigation of spurious correlation." In ICML, 2023.

LLMs: With regard to the reviewer’s first point, we do not argue in the paper that LLMs are not reliable for metadata, and we agree with the reviewer that LLMs are useful for a variety of tasks. The metadata that we use comes from ground truth concepts that are available in all the datasets tested in our work. We do not use LLMs to extract metadata for graph construction. Instead, we argue that LLMs are unreliable in the generation of fair, balanced data (lines 36-39). This is an issue that has been acknowledged by the authors of ALIA as well (please refer to Section 6 in the ALIA paper). This is the issue we overcome with ConBias: Instead of using an off-the-shelf LLM to generate diverse image data, we use a controllable and interpretive knowledge graph that encodes the class-concept imbalances in the data.

Novelty: The core intuition of ConBias is that we want a controllable and interpretable way to generate debiased data. In a learning based framework, these two requirements are compromised since a gradient based optimization technique relies on parameters that may deem the model to be a black box.

ConBias is not simply a statistical co-occurrence counting mechanism. In fact, in lines [87-90], we mention that there are diagnostic tools that exist today that compute such object co-occurrence statistics. The novelty of our method is in the creation of a graph that encapsulates multiple class-concept co-occurrences. The analysis of the class-concept graph cliques is crucial to the graph construction. In Table 2, we show the benefit of using the graph structure as opposed to a simple counting mechanism based on statistical co-occurrences. Additionally, our method is novel as to the best of our knowledge, no existing works leverage a concept co-occurrence based graph to simultaneously diagnose and debias datasets. Our results, demonstrated in Table 1-4, confirm the usefulness of the graph clique balancing strategy.

Broadly, we believe that if we wish to debias datasets, we need human-level intuition on what concepts are biased, and what concepts need to be debiased in a principled fashion, corresponding to human-level intuition. ConBias provides this intuition (please refer to Figure 3 and Figure 4 in the main paper, and Section D in the supplementary section). A black-box learning based approach, on the other hand, would hinder these capabilities.

Extension to multi-class tasks:

Since the reviewer raised the example of Imagenet, in the attached PDF above, we present new results on diagnosing multiple classes in Imagenet-1k. We discovered some interesting spurious correlations uncovered by ConBias. As such, ConBias can be seamlessly used for datasets of increasing complexity. We will update the paper to include these results.

Waterbirds and UrbanCars, while binary, are the de facto datasets used in the literature for evaluating single-shortcut and multi-shortcut learning in classifiers. These are the most commonly used datasets for benchmarking purposes. COCO-GB is a subset of the MS-COCO dataset that includes real-world data consisting of humans and everyday objects in the wild.

We include the motivations behind the datasets and other details in Section 4.1 and Supplementary Section B. For the reviewer’s convenience, we also include three references here that may clarify the use of these datasets:

• Li, Zhiheng, et al. "A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others." In CVPR. 2023.

• Sagawa, Shiori, et al. "Distributionally Robust Neural Networks." In ICLR 2020.

• Tang, Ruixiang, et al. "Mitigating gender bias in captioning systems." In WWW. 2021.

Results of ALIA: We invite the reviewer to refer to Section G of the Supplementary material, where we present the confidence intervals in addition to the averaged results in Table 1. The results presented are within the confidence intervals as presented in the ALIA paper, who also average results over three runs.

Computational complexity: We invite the reviewer to refer to Section H in the Supplementary section for details on runtime. In general, given $K$ classes and $C$ concepts, the graph clique enumeration is expected to grow in $O(exp(|K+C|)$. However, in practice, we find that constraining clique sizes to $k<=4$ leads to interpretable bias combinations, with no significant effect of the exponential runtime. We agree that this is a heuristic, and more efficient clique enumeration methods can be developed (lines 314-315). We will update the paper to reflect this point raised by the reviewer.

Total number of graph nodes and edges: For Waterbirds, there are 66 nodes and 865 edges in the graph. For UrbanCars, the graph contains 19 nodes and 106 edges. For COCO-GB, there are 81 nodes and 2326 edges in the graph. We will update the paper to include these additional details.

We thank the reviewer for the comments. Here, we address the points on metadata and LLMs.

Metadata

In the absence of ground truth metadata, one can leverage large multimodal models (LMMs), for instance in segmentation or open-vocabulary detection, and generate such concepts. However, there remains the possibility of noisy metadata that may contain unreliable artefacts, in addition to the biases within these models themselves. In this work, we restrict ourselves to available, high-quality, ground truth concepts, to showcase the usefulness of our framework. Pursuing open-vocabulary models to generate metadata is an interesting direction of future work. In general, ConBias is not constrained by how the metadata is obtained, but in the quality of metadata obtained. Future developments in LLMs/LMMs that can generate high quality concept metadata can be seamlessly integrated into the ConBias framework. We will be sure to update the paper with this discussion.

Our core contribution with ConBias is not the metadata stage, which we assume to be available and high-quality, similar to other works in the past [A, B]. Our core contribution is the diagnosis and debiasing of datasets with ConBias, which leads to significant improvements on multiple datasets with respect to the current state-of-the-art. As requested by the reviewer, we have also provided diagnosis results on a more complex dataset such as Imagenet-1k.

[A] Wu, Shirley, et al. "Discover and cure: Concept-aware mitigation of spurious correlation." In ICML, 2023.

[B] Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E Gonzalez, and Trevor Darrell. Diversify your vision datasets with automatic diffusion-based augmentation. Advances in Neural Information Processing Systems, 36, 2024

LLMs

We reiterate that, as mentioned in lines 36-39, relying on LLMs to generate diverse, unbiased descriptions is problematic since LLMs themselves may be biased, and such generation is not controllable. This issue of relying on LLMs has also been addressed in the ALIA paper (Section 6) [A], and it is precisely this issue that we fix with ConBias. By leveraging the concept graph, ConBias can generate a debiased dataset in a controlled and interpretable manner.

[A] Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E Gonzalez, and Trevor Darrell. Diversify your vision datasets with automatic diffusion-based augmentation. Advances in Neural Information Processing Systems, 36, 2024

We thank the reviewer for the positive feedback.

Intuition and advantage of constructing knowledge graph: ConBias is not simply a statistical co-occurrence counting mechanism. In fact, in lines [87-90], we mention that there are diagnostic tools that exist today which compute such object co-occurrence statistics. The novelty of our method is in the creation of a graph that encapsulates multiple class-concept co-occurrences. The analysis of the class-concept graph cliques is crucial to the graph construction. In Table 2, we show the benefit of using the graph structure as opposed to a simple counting mechanism based on statistical co-occurrences. Additionally, our method is novel as to the best of our knowledge, no existing works leverage a concept co-occurrence based graph to simultaneously diagnose and debias datasets. The advantages over previous methods is in the controllability and interpretability of debiased data generation. Our results, as demonstrated in Table 1 and Table 4, show significant advantages in generalization over previous approaches.

Details of concept set annotations: We described the details for all the datasets used in Section B and Section C of the appendix and the supplementary materials. In Table A2 in appendix, we detailed the list of concept sets: the Waterbirds dataset has 64 unique concepts; the UrbanCars dataset has 17 unique concepts; the COCO-GB dataset has 81 unique concepts. All the concepts are from the MS-COCO dataset. We will incorporate these details into the main paper in the final version.

Effect of the quality of the annotations: We assume the concept sets to be available for graph construction. One can interpret this availability as the same as the assumption that ground truth labels are useful for classification tasks. Just as unreliable ground truth labels can hinder classifier performance, the concept sets also need to be of high quality and reliability to ensure strong generalization. We mention this assumption in the final section of the main paper, and are looking forward to future work that investigates unreliable, partially available concept sets. We will update the paper to clarify this important point raised by the reviewer.

Results of ALIA: We invite the reviewer to refer to Section G of the appendix, where we present the confidence intervals in addition to the averaged results in Table 1. The results presented are within the confidence intervals as presented in the ALIA paper, who also average results over three runs.

Typos in L198: Thank you and we will correct it in the final version.