VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance

Authors: divyansh  srivastava
ge  yan
tsui-wei  weng
u c san
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 07:19:29

Abstract: Concept Bottleneck Models (CBMs) provide interpretable prediction by introducing an intermediate Concept Bottleneck Layer (CBL), which encodes human-understandable concepts to explain models' decision. Recent works proposed to utilize Large Language Models (LLMs) and pre-trained Vision-Language Models (VLMs) to automate the training of CBMs, making it more scalable and automated. However, existing approaches still fall short in two aspects: First, the concepts predicted by CBL often mismatch the input image, raising doubts about the faithfulness of interpretation. Second, it has been shown that concept values encode unintended information: even a set of random concepts could achieve comparable test accuracy to state-of-the-art CBMs. To address these critical limitations, in this work, we propose a novel framework called Vision-Language-Guided Concept Bottleneck Model (VLG-CBM) to enable faithful interpretability with the benefits of boosted performance. Our method leverages off-the-shelf open-domain grounded object detectors to provide visually grounded concept annotation, which largely enhances the faithfulness of concept prediction while further improving the model performance. In addition, we propose a new metric called Number of Effective Concepts (NEC) to control the information leakage and provide better interpretability. Extensive evaluations across five standard benchmarks show that our method, VLG-CBM, outperforms existing methods by at least 4.27\% and up to 51.09\% on accuracy at NEC=5, and by at least 0.45\% and up to 29.78\% on average accuracy across different NECs, while preserving both faithfulness and interpretability of the learned concepts as demonstrated in extensive experiments.

Main paper: 
 introduction: 
 As deep neural networks become popular in real-world applications, it is crucial to understand the decision of these black-box models. One approach to provide interpretable decisions is the Concept Bottleneck Model (CBM) [5] , which introduced an intermediate concept layer to encode humanunderstandable concepts. The model makes final predictions based on these concepts. Unfortunately, one major limitation of this approach is that it requires concept annotations from human experts, making it expensive and less applicable in practice as concept labels may not always be available.Recently, a line of works utilized the powerful Vision-Language Models (VLMs) to replace manual annotation [14, 27, 25] . They used Large Language Models (LLMs) to generate set of concepts, and then trained the models in a post-hoc manner under the guidance of VLMs or neuron-level interpretability tool [13] . By eliminating the expensive manual annotations, some of these CBMs [14] could be scaled to large datasets such as ImageNet [18] . However, these CBMs [14, 27, 25] still face two critical challenges:1. Challenge #1: Inaccurate concept prediction. The concept predictions in these CBMs often contain factual errors i.e. the predicted concepts do not match the image. Moreover, as concepts are generated by LLMs, there are some non-visual concepts, for example "loud music" or "location" used in LF-CBM [14] , which further hurt the faithfulness of concept prediction.2. Challenge #2: Information leakage. Recently, [12, 11] observed the information leakage in CBMs through empirical experiments -they found that the concept prediction encodes unintended information for downstream tasks, even if the concepts are irrelevant to the task.In this paper, we propose a new framework called Vision-Language-Guided Concept Bottleneck Model (VLG-CBM) to address these two major challenges. Our contributions are summarized below:1. To address Challenge #1, we propose to use the open-domain grounded object detection model to generate localized, visually recognizable concept annotations in Section 3. This approach automatically filters the non-visual concepts. Furthermore, the location information is utilized to augment the data. As far as we know, our VLG-CBM is the first end-to-end pipeline to build CBM with vision guidance from open-vocabulary object detectors.2. To address Challenge #2, we provide the first rigorous theoretical analysis which proves that CBMs have serious issues on information leakage in Section 4.1, whereas previous study on information leakage [11, 25] only provides empirical explanations. Building on our theory, we further propose a new metric called the Number of Effective Concepts (NEC) in Section 4.2, which facilitates fair comparison between different CBMs. We also show that using NEC can help to effectively control information leakage and enhance interpretability in our VLG-CBM.3. We conduct a series of experiments in Section 5 and demonstrate that our VLG-CBM outperforms existing methods across 5 standard benchmarks by at least 4.27% and up to 51.09% on accuracy at NEC=5, and by at least 0.45% and up to 29.78% on average accuracy across different NECs. Our learned CBM achieves a high sparsity of 0.2% in the final layer even on large datasets including Places365, preserving interpretability even with a large number of concepts. Additionally, we qualitatively demonstrate that our method provides more accurate concept attributions compared to existing methods.Figure 1 : We compare the decision explanation of VLG-CBM with existing methods by listing top-5 contributions for their decisions. Our observations include: (1) VLG-CBM provides concise and accurate concept attribution for the decision; (2) LF-CBM [14] frequently uses negative concepts for explanation, which is less informative; (3) LM4CV [25] attributes the decision to concepts that do not match the images, a reason for this is that LM4CV uses a limited number of concepts, which hurts CBM's ability to explain diverse images; △ ✓ ✓ △ × △ LaBo[27] × ✓ × △ × △ LM4CV[25] ✓ × × △ △ △ This work: VLG-CBM ✓ ✓ ✓ ✓ ✓ ✓Table 1 : Comparative analysis of methods based on evaluation, flexibility, and interpretability.Here, ✓ denotes the method satisfies the requirement, △ denotes the method partially satisfies the requirement, and × denotes the method does not satisfy the requirement. We compare with SOTA methods including LF-CBM [14] , Labo [27] and LM4CV [25] . 
related work: 
 Concept Bottleneck Model (CBM). The Concept Bottleneck Model [5] introduces an intermediate concept bottleneck layer (CBL), where each neuron represents a human-understandable concept. CBL is followed by a linear prediction layer, which maps concepts to classes, enabling interpretable final decisions. Formally, let feature representation generated by a frozen backbone represented by z = ϕ(x), CBL concept prediction as g(z) = W c z, and the final prediction layer as h(•) = W F g(z) + b F .The final class prediction of the CBM is given by ŷ = h(g(z)) = h • g • ϕ(x).Under this setting, the key in training a CBM is obtaining an annotated {(image, concept)} paired dataset for training concept bottleneck layer g. In [5] , the authors used human-specified labels to train the CBL in a supervised way. However, obtaining labels with human annotators could be very tedious and costly. Recently, [14] , [25] , and [27] proposed to utilize Large Language Models (LLM) to generate a set of concepts S, then train CBL by aligning image and concepts with the guidance of vision language models (e.g. CLIP). For example, Oikarinen et al. [14] proposed LF-CBM to train CBM by directly learning a mapping from the embedding space of backbone to concept values in the CLIP space using cosine cubed loss function with the neuron interpretability tool [13] , and then mapping concepts to classes using sparse linear layer. [25] proposed LM4CV, a task-guided concept searching method that learns text embeddings in the CLIP space, and then maps the learned embeddings to concepts obtained from LLM using nearest neighbor. Yang et al. [27] proposed LaBo, using submodular optimization to reduce the concept set, followed by using CLIP backbone for obtaining concept values. However, as we show in Sections 5.1 and 5.3 , these methods suffer from multiple issues: (i) The concept prediction is often incorrect and does not capture the visual attributes required for downstream class prediction (e.g. see Fig. 1 )(ii) VLMs like CLIP suffer from modality gap between image and text embeddings [8] resulting in encoding unintended information, and even random concepts can achieve high accuracy [25] . To address these issues, we explicitly ground the concepts on the training dataset using an open-domain object detection model and then using the obtained concepts for learning CBL -this can ensure a more faithful representation of fine-grained concepts and avoids the modality gap issues introduced by VLMs. Table 1 demonstrates the superiority of VLG-CBM over existing methods [14, 25, 27] on properties including controlling information leakage, flexibility to use any backbone, and accurate concept prediction.There are some recent works aim at addressing the challenges of CBMs. Similar to us, Pham et al. [15] uses an open-vocabulary object detection model to provide an explainable decision. However, their model is directly adapted from an OWL-ViT model, while our VLG-CBM uses an open-vocabulary object detection model to train a CBL over any base model, providing more flexibility. Additionally, their model requires pretraining to get best performance, while our VLG-CBM could be applied post-hoc to any pretrained model. Kim et al. [3] proposed to filter non-visual concepts by adding a vision activation term to the concept selection step, whereas VLG-CBM uses an open-vocabulary object detectors in multiple stage of CBM pipeline: for filtering non-visual concepts and the guiding training of concept bottleneck layer. Sun et al. [20] aims at eliminating the information leakage, and the authors evaluate the information leakage by measuring the performance drop speed after removing top-contributing concepts. This metric can be controlled by our proposed NEC metric, because the performance reach minimum after removing all contributing concepts. Roth et al. [17] demonstrate that random words and characters achieve comparable CLIP zero-shot performance on visual classification tasks. However, their work does not address information leakage problem and is a very different setting from our work. To date, most of the CBMs focused on vision domains, including this work. There are some recent work applying CBM approach to different domains and different tasks, e.g. interpretable language models for text classifications [21, 22, 10] and for continual learning [26] . We refer the interested readers to their papers for more details.Open Domain Language Grounded Object Detection. Recent works, including GLIP [7] , GLIPv2 [29] , and GroundingDINO [9] detect objects in images in an open-vocabulary manner conditioned on natural language queries. In this work we propose to utilize open-vocabulary object detectors for automatically generating grounded concept dataset for training CBMs. This removes the need for human labelers, which is costly, tedious, and does not scale to large datasets. Further, the detected objects provide necessary vision-guidance for CBMs training as demonstrated in our experiments.To evaluate the trade-off between interpretability and performance, we also calculate the average accuracy under different NECs. In general, higher NEC indicates a more complex model, which may achieve better performance but also hurt interpretability.We choose six different levels: 5, 10, 15, 20, 25, 30 and measure the average accuracy.Controlling NEC. As we discussed in Section 4, there are two approaches to control NEC: (1) using a dense final layer and directly controlling the number of concepts and ( 2) training a sparse final layer with appropriate sparsity. LM4CV [25] used the first approach, where the number of concepts could be directly set as target NEC. For LF-CBM [14] and our VLG-CBM, the second approach is utilized: To achieve target sparsity, we control the regularization strength λ in GLM-SAGA. GLM-SAGA provides a regularization path, which allows us to gradually reduce regularization strength and get a series of weight matrices with different sparsity. Specifically, we start with λ 0 = λ max which gives the sparsest weight. Then, we gradually reduce the λ by λ t+1 = αλ t until we achieve the desired NEC. We choose the weight matrix with the closest NEC to our target and prune the weights from smallest magnitude to largest to enforce accurate NEC. LaBo [27] did not provide a NEC control method. Hence, we apply sparse final layer training of LaBo's concept prediction to control NEC.Results. The test results are summarized in Table 2 with the following observations:1. The accuracy at NEC = 5 provides a good metric for evaluating the semantic information in CBL: As shown in the table, the accuracy of random CBL is much lower with NEC = 5, which implies the information leakage is controlled and the accuracy could better reflect the useful semantic information learned in the CBL.2. The performance of LM4CV is even worse than random CBL. An explanation to this is LM4CV utilizes a dense final layer, which is intrinsically inefficient to interpret as each class is connected to all the concepts, including the irrelevant ones. When limiting the NEC to a small value to provide a concise explanation, the model has to largely reduce the concept number which sacrifices the prediction power.3. Our method significantly outperforms all the baselines at least 4.27% and up to 51.09% on accuracy at NEC=5, and by at least 0.45% and up to 29.78% on average accuracy across different NECs, illustrating both high performance and good interpretability.As we discussed in Table 2 , the LaBo and LM4CV methods are limited to CLIP visual encoder as the backbone. In Table 2 , we follow the backbone choice of Oikarinen et al. [14] , which uses non-CLIP backbone for CUB, Places365 and ImageNet. Thus, LM4CV and LaBo are not applicable to those benchmarks. To further compare the performance, in this section, we use CLIP-RN50 backbone on ImageNet and CUB datasets which are supported for LM4CV and LaBo (Places365 models are not provided in these two works). The performances are listed below. From the results, we could see that our method still outperforms all the baselines on both datasets and both metrics. From the above properties of Q and R, the expectation term in the RHS of Eq. (A.9) can be derived as:E (R,Q) min w ∥R w -Q ⊤ w∥ 2 2 = E Q ∥(Q ⊤ w) k+1:d ∥ 2 2 . (A.12)This is because range(R) = span(e 1 , e 2 , • • • , e k ), and k < d. Thus, min w ∥R w -Q ⊤ w∥ 2 2 is the squared distance from Q ⊤ w to subspace span(e 1 , e 2 , • • • , e k ), which equals to the squared sum of last d -k coordinates of Q ⊤ w.Because Q is a random rotation, Q ⊤ w is uniformly distributed on a sphere with radius ∥w∥ 2 . Denote v = Q ⊤ w. From symmetricity, we haveE v 2 1 = E v 2 2 = • • • = E v 2 d . (A.13) Furthermore, ∥v∥ 2 2 = ∥w∥ 2 2 gives d i=1 v 2 i = ∥w∥ 2 2. Take expectation of both sides givesd i=1 E v v 2 i = ∥w∥ 2 2 , thus E v 2 1 = E v 2 2 = • • • = E v 2 d = ∥w∥ 2 2 /d.The target quantity becomesE Q ∥(Q ⊤ w) k+1:d ∥ 2 2 = E v d i=k+1 v 2 i = d i=k+1 E v v 2 i = d -k d ∥w∥ 2 2 (A.14)In conclusion, we derive an upper bound of approximation error for any linear function f :E Wc min ( w, b) E z |f (z) -f (z)| 2 ≤ λ 2 max (1 - k d )∥w∥ 2 2 (A.15)Look at the bound in Eq. (A.15): λ 2 max is a constant regarding the scale of data; ∥w∥ 2 2 is a constant regarding the norm of weight vector we want to approximate; (1 -k d ) is a linear term shows that the expected square error goes down linearly when we increase the number of concepts k, and achieves zero when k = d.Case (II): k ≥ d. For the case that k ≥ d, it could be derived from our main results that E(k) = 0. Additionally, with probability 1 we could find f (x) = f (x) as will be derived below. As we discussed, with probability 1, W c has full rank. Given that, we have W + c W c z = z,→ R C , f : R d → R C , w ∈ R d , w ∈ R k , the expected error upper-bound is E(k) ≤ Cλ max (1 - k d )∥w∥ 2 2 .(A. 16 )Here E(k) = E Wc min ( w, b) E z ∥f (z) -f (z)∥ 2denotes the average square error.Remark A.2. The statement could be verified by applying Theorem 4.1 to each f i and fi output, then summing up the error.Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: We discuss the limitations in Section 6.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
method: 
 In this section, we describe our novel automated approach to train a CBM with both Vision and Language Guidance to ensure faithfulness, which is currently lacking in the field. Our approach, abbreviated as VLG-CBM in the paper, generates an auxiliary dataset grounded on fine-grained concepts present in images for training a sequential CBM. Section 3.1 describes our approach to generating an auxiliary dataset used in training CBM, Section 3.2 describes our approach to training concept bottleneck layer, and Section 3.3 describes the training of sparse layer to obtain class labels from concepts in an interpretability-preservable manner. The overall pipeline is shown in Fig. 2 .Here we describe our novel automated approach for generating labeled datasets for training CBMs. Let f : X → Y be the neural network mapping images to corresponding class labels, where X = R H×W ×3 denotes the input image space and Y = {1, 2, . . . , C} denotes the label space, C is the number of classes. Denote D = {(x i , y i )}, x i ∈ X , y i ∈ Y the dataset used for training f , where x i is the i-th image and y i is the corresponding label. Let S be a set of natural-language concepts describing the fine-level visual details from which classes are composed. We propose to generate a modified and auxiliary dataset D ′ from D such that each image contains finer-grained concepts that are useful in predicting the classes, along with the target class. The overall process of obtaining the modified dataset D ′ can be divided into two steps:• Language supervision from LLMs to generate a set of candidate concepts: We follow the steps proposed in LF-CBM [14] for generating candidate concepts S c for each class c by prompting LLM to obtain visual features describing the class. • Vision supervision from Open-domain Object Detectors to ground candidate concepts to spatial information: We propose using Grounding-DINO [9] Swin-B, current state-ofthe-art grounded object detector, for obtaining bounding boxes of candidate concepts in the dataset. For each image x i with class label c and candidate concepts S c , we prompt Grounding DINO model with S c and obtain K i bounding boxes:EQUATIONwhere b j ∈ R 4×2 is the j-th bounding box coordinates, t j ∈ R is the corresponding confidence given by the model and s j ∈ S c is the concept of this bounding box. We define a confidence threshold T and remove bounding boxes with confidence less than T to get filtered bounding boxes for each image:EQUATIONAfter collecting bounding boxes for every image, we filter out the concepts that do not appear in any bounding box, and get our final concept set S:EQUATIONThe one-hot encoded concept label vector o i ∈ {0, 1} | S| for image x i is thus defined as:EQUATIONOur final concept-labeled dataset D ′ for training CBM can be written as:EQUATIONAfter constructing the concept-labeled dataset D ′ , we now define our approach to train the concept bottleneck layer for predicting the fine-grained concepts in the input image in a multi-label classification setting. Let ϕ : X → R d be a backbone that generates d-dimensional embeddings z = ϕ(x) for input image x. Note that ϕ(x) can be a pre-trained backbone or trained from scratch. Define g to be the Concept Bottleneck Layer (CBL) which maps embeddings to concept logits. We train a sequential CBM [5, 12] g(ϕ(x)) to predict concepts in an image using Binary Cross Entropy (BCE) loss for multi-label prediction. Additionally, to improve the diversity of the concept-labeled dataset D ′ , we augment the training dataset by cropping images to a randomly selected bounding box and modifying the target one-hot vector to predict the concept corresponding to the bounding box. Our optimization objective in terms of BCE loss can be written as:EQUATIONIn this section, we define our approach to training a sparse linear layer to obtain class labels from concepts in an interpretability-preservable manner. Let h : R d → R C be the sparse linear layer with weight matrix W F and bias b F , which maps concept logits to class logits. We train the sparse layer using the original dataset D by first obtaining concept logits from the trained CBL(frozen), normalizing the concept logits with the mean and variance on training set, and then using them to predict class logits. Our optimization objective in terms of Cross Entropy (CE) loss can be written as:EQUATIONwhere [31] on weight matrix W F , λ is a hyperparameter controlling regularization strength. We use GLM-SAGA [24] solver to solve this optimization problem.R α = (1 -α) 1 2 ∥W F ∥ 2 2 + α∥W F ∥ 1 is the elastic-net regularizationlong been a challenging problem, as different models use different numbers of concepts and different styles of final layers (sparse/dense). NEC considers both, thus providing a fair metric to compare different models.Given these benefits, we suggest to control the NEC when comparing the performance of CBMs. In Section 5, we provide experiments with controlled NEC, where we observed our VLG-CBM outperforms other baselines. From the results, we could see that (1) for NEC large enough, even a random CBL could achieve near-optimal accuracy, supporting the existence of information leakage; (2) when NEC decreases, the accuracy of LF-CBM and random weights begin to drop, while our VLG-CBM does not have significant decrease.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [Yes]Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: The link to the project webpage is provided in the abstract and the code will be released by poster deadline. Guidelines:• The answer NA means that paper does not include experiments requiring code. Justification: We provide computational resources in Appendix B.Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage. • The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. • The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). 
unifying cbm evaluation with number of effective concepts (nec): 
 Besides training, another important challenge for CBM is: how to evaluate the semantic information learned in the CBL? Conventionally, the classification accuracy for final class labels is an important metric for evaluating CBMs, with the intuition that a good classification accuracy indicates that useful semantic information is learned in the CBL. However, purely using accuracy as the evaluation metric could be problematic, as it has been shown that information leakage exists in jointly or sequentially trained CBM [12, 11] . That is to say, the CBL could contain unintended information that could be used for downstream classification hence achieving high classification accuracy, even if the concept is irrelevant to the task. In fact, recently [25] showed that, when increasing the number of concepts, a randomly selected concept set could even approach the accuracy of the concept set chosen with sophistication, supporting the existence of information leakage.To better understand this phenomenon, in section 4.1, we conduct a first theoretical analysis to investigate random CBL and its capability. To the best of our knowledge, this is the first formal analysis of random CBL. Next, inspired by our theoretical result, we propose a new evaluation metric for CBM, named NEC in section 4.2. NEC provides a way to control information leakage and enhance the interpretability of model decisions.We start by defining the notations. Denote k the number of concepts in CBL. We assume that the CBL g consists of a single linear layer: g(z) = W c z, where W c ∈ R k×d and z ∈ R d , and the final layer h is also linear:h • g(z) = W F g(z) + b F , where W F ∈ R C×k , b F ∈ R C .This is the common setting for CBMs. The following theorem suggests a surprising conclusion: a linear classifier upon random (i.e. untrained) CBL could accurately approximate any linear classifier trained directly on the representation, as the number of concepts in the CBL goes up.Theorem 4.1. Suppose Σ ∈ R d×d is the variance matrix of the representation z which is positive definite, λ max is the largest eigenvalue of Σ, and the weight matrix W c ∈ R k×d is sampled i.i.d from a standard Gaussian distribution. For any linear classifier f which is built directly on the representation z, i.e. f : R d → R, f (z) = w ⊤ z + b, it could be approximated by another linear classifier f on concept logits g(z) = W c z, i.e. f (z) ≈ f (z) = w⊤ g(z) + b, with the expected square error E(k) upper-bounded byEQUATIONHereE(k) = E Wc min ( w, b) E z |f (z) -f (z)| 2 denotes the average square error, w ∈ R d , w ∈ R k , k is the number of concepts in CBL.Remark 4.1. In Theorem 4.1, we consider a 1-D regression problem where we use a linear combination of concept bottleneck neurons to approximate any linear function. The multi-class classification result could be derived by applying Theorem 4.1 to each class logit (see Corollary A.1). From Eq. ( 8), we could see that the expected error goes down linearly when concept number k increases, and achieves 0 when k ≥ d, where d is the dimension of backbone representation z. This suggests that, even with a random CBL (i.e. W c is simply drawn from a standard Gaussian distribution without any training), the classifier could still approximate the original classifier well and achieve good accuracy, when concept number k is large enough. We defer the formal proof of Theorem 4.1 to Appendix A.Theorem 4.1 provides a formal theoretical explanation on [25] 's observation. Moreover, it raises a concern on the evaluation of CBMs: model classification accuracy may not be a good metric for evaluating the semantical information learned in CBL, because a random CBL could also achieve high accuracy. To address this concern, we need to control the concept number k so that the semantically meaningful CBLs can be distinguished with random CBLs w.r.t. the final classification performance.We notice that previous works mainly use two approaches to control k:1. Control the total number of concepts: [25] used a more concise concept layer, i.e. reduce the total number of concepts. However, this approach may miss some important concepts due to limitations in total concept numbers. Additionally, they used a dense final layer which is less interpretable for humans, as each decision is related to the whole concept set. 2. [14, 28] suggested using a sparse linear layer for final prediction to enhance interpretability.Though sparsity is initially introduced to enhance interpretability, we note that this also reduces the number of concepts used in the decision, thus controlling the information leakage. However, the problem is, these works lack the quantification for sparsity, which is necessary for fair comparison between methods.To provide a unified metric for both approaches, we propose to measure the Number of Effective Concepts (NEC) for final prediction as a sparsity metric. It is defined asEQUATIONIntuitively, NEC measures the average number of concepts the model uses to predict a class. Using NEC to evaluate CBM provides the following benefits:1. A smaller NEC reduces the information leakage. As shown in Fig. 3 , with large NEC, even random CBL could achieve near-optimal accuracy, suggesting potential leakage in information. However, by reducing NEC, the accuracy of random concepts drops quickly. This implies enforcing a small NEC could help to control information leakage. 2. A model with a smaller NEC provides more interpretable decision explanations. Humans can recognize an object with several important visual features. However, models can utilize tens or hundreds of concepts for the final prediction. By using a smaller NEC, the model's decision could be attributed mainly to several concepts, making it more interpretable to human users. 
experiments: 
 In this section, we conduct a series of experiments to evaluate our method, including illustrating the faithfulness of concept prediction, interpretability of model decisions, and performance with controlled NEC.Setup. Following prior work [14] , we conduct experiments on five image recognition datasets: CIFAR10, CIFAR100 [6] , CUB [23] , Places365 [30] and ImageNet [18] . We follow the choice of [14] to use CLIP(RN50) [16] for CIFAR10 and CIFAR100, ResNet-18 [2] for CUB and ResNet-50 (trained on ImageNet) for Places365 and ImageNet as the backbone.Baselines. We compare our method with three major baselines when applicable: LF-CBM [14] , LaBo [27] , and LM4CV [25] . These are SOTA methods for constructing scalable CBMs, with [14] most flexible and [27, 25] limited by specific architecture and not available for certain dataset. Additionally, we present the results from a randomly initialized CBL for comparison.Metrics. As discussed in Section 4, in order to evaluate the final classification power of CBM, the NEC needs to be controlled. Therefore, we measure the following two metrics:1. Accuracy at NEC=5: (Acc@5) This metric is designed to show the performance of CBM which could provide an interpretable prediction. We choose the number 5 so that human users could easily inspect all concepts related to the decision without much effort.In order to examine whether our CBL learns concepts aligned with human perception, we list the top-5 activated images for example concept neurons on the model trained on the CUB dataset in Fig. 4 . As shown in the figure, our CBL faithfully captures the corresponding concept. We provide more visualization results in Appendix K. In this section, we conduct a case study to compare the concept prediction between our VLG-CBM , LF-CBM [14] and LM4CV [25] as shown in Fig 1 . We provide extended results and comparison with LaBo [27] in Appendix G.2. For our method, we use the final layer with NEC = 5. We show that our method provides more accurate concept prediction and more interpretable decisions for users.Decision interpretability. We examine the explanation of each CBM model on example images by showing the top-5 concept contributions. The contribution of each concept is calculated as the product of the concept prediction value and corresponding weight. Formally, the contribution of i-th concept to j-th class is defined as: Contribution(i, j) = g i (z) • (W F ) ji . We pick the top-5 contributing concepts for the final predicted class and visualize it in Fig. 1 . We could see that:1. For other CBMs, a large portion of the final decision is attributed to the "Sum of other features". This part hurts the interpretability of CBM, as it's difficult for users to manually inspect all these concepts in practice. We conduct further study on this in Section 5.4. Our model, however, provides a concise explanation from a few concepts because we apply the constraint NEC=5. This ensures users can understand model decisions without difficulties.2. Our VLG-CBM provides explanation more aligned with human perception. From the example, we can also see that our model explains the decision with clear visual concepts.Other CBMs attribute the decision to non-visual concepts (e.g. LaBo), concepts that do not match the image (e.g. LM4CV), or negative concepts (LF-CBM).Besides training a final layer with a small NEC, another common approach to provide a concise explanation is showing only the top contribution concepts. However, we argue that this approach may not faithfully explain the model's behavior, as the non-top concepts also make a significant contribution to the decision. To verify this, we conduct the following experiment: On the CUB dataset, we prune the final weight matrix W F to leave only the top-5 concepts for each class, whose weight has the largest magnitude. Then, we use the pruned model to make predictions and compare them with the prediction results from the original model. Table 4 shows results for our VLG-CBM which uses NEC= 5 to control sparsity, and other three baselines, LaBo [27] , LF-CBM [14] and LM4CV [25] , without any constraint on NEC. As shown in the  
conclusion, potential limitations and future work: 
 In this work, we study how to improve the interpretability and performance of concept bottleneck models. We introduce a novel approach VLG-CBM based on both vision and language guidance, which successfully improves both the interpretability and utility of existing CBMs in prior work. Additionally, our theoretical analysis show that information leakage may exist on even in the untrained CBLs, serving the foundations for our proposed new metric (NEC). We show that NEC not only allow fair evaluation of CBMs but also can be used to effectively control information leakage of CBM and ensure interpretability. Extensive experiments on image classification benchmarks demonstrated our VLG-CBM largely outperform previous baselines especially for small NEC, providing more interpretable decisions for users.Despite the superior performance of VLG-CBM over prior work as demonstrated in extensive experiments, one potential limitation is the dependence on large pretrained models (e.g. the success of open-domain grounded object detection model that we use to enforce vision guidance). However, prior work (e.g. LaBo, LM4CV, LF-CBM) also shared similar limitation on the reliance of large pre-trained models (e.g. CLIP). Nevertheless, it also means that our techniques have the potential to be further improved with the advancement of large pre-trained models. In the future, we plan to explore training CBL with even more vision guidance, such as using segmentation maps of concepts.A Proof of Theorem 4.1In this section, we present a formal definition of the expected square error in Theorem 4.1 and show the proof. First, we define the square approximation error asE z |f (z) -f (z)| 2 , (A.1)which is the average square distance between f (z) and f (z). Given a specific CBL W c , we seek a final layer w to minimize the square error:min ( w, b) E z |f (z) -f (z)| 2 . (A.2)For randomly Gaussian initialized W c , we care about the minimal error we could achieve on average. Thus, for each W c , we choose w and b to achieve minimum approximation error, then take the expectation over W c to define the expected square error asE(k) = E Wc min ( w, b) E z |f (z) -f (z)| 2 . (A.3)Setting Suppose the representation z has variance Σ ∈ R d×d which is positive definite. The weight matrix W c ∈ R k×d is sampled i.i.d from a standard Gaussian distribution. Here, we show that any linear classifier which is built directly on representation z, i.e. f (z) = w ⊤ z + b, could be approximated by a linear classifier on concept logits g(z) = W c z, i.e. f (z) ≈ f (z) = w⊤ g(z) + b.Theorem 4.1. Suppose Σ ∈ R d×d is the variance matrix of the representation z which is positive definite, λ max is the largest eigenvalue of Σ, and the weight matrix W c ∈ R k×d is sampled i.i.d from a standard Gaussian distribution. For any linear classifier f which is built directly on the representation z, i.e. f : R d → R, f (z) = w ⊤ z + b, it could be approximated by another linear classifier f on concept logits g(z) = W c z, i.e. f (z) ≈ f (z) = w⊤ g(z) + b, with the expected square error E(k) upper-bounded byEQUATIONHereE(k) = E Wc min ( w, b) E z |f (z) -f (z)| 2 denotes the average square error, w ∈ R d , w ∈ R k , k is the number of concepts in CBL.Proof. Based on the value of k, we can consider two cases: (I) k < d, and (II) k ≥ d, and derive the E(k) respectively.Case (I): k < d. First, we consider under a fixed W c , what is the minimum error we could achieve. The expected approximation error is:E z |f (z) -f (z)| 2 = E z |w ⊤ z + b -( w⊤ W c z + b)| 2 = E z |(w ⊤ -w⊤ W c )z + b -b| 2 = V z [(w -W ⊤ c w) ⊤ z] ( * ) + E z [(w ⊤ -w⊤ W c )z] + b -b 2 ( * * ) . (A.4)The last equality is from E(X 2 ) = VX + (EX) 2 . The second term ( * * ) takes minimum 0 whenb = E z [(w ⊤ -w⊤ W c )z] + b.The remaining question is to choose a proper w to minimize ( * ).Notice thatV z (w -W ⊤ c w) ⊤ z = (w -W ⊤ c w) ⊤ Σ(w -W ⊤ c w) = ∥Σ 1 2 (w -W ⊤ c w)∥ 2 2 = ∥Σ 1 2 W ⊤ c w -Σ 1 2 w∥ 2 2 , (A.5)where Σ is the covariance matrix of z, Σ 1 2 is the principal square root of Σ, Σ ∈ R d×d , Σ 1 2 ∈ R d×d . Now the problem in Eq. (A.2) can be reduced to a linear least square problem:min ( w, b) E z |f (z) -f (z)| 2 = min w ∥Σ 1 2 W ⊤ c w -Σ 1 2 w∥ 2 2 (A.6)Since Σ is positive definite, so is Σ 1 2 . Thus, the eigen decomposition of Σ 1 2 satisfies the following: Σ 1 2 = Q⊤ Λ Q, where Q ∈ R d×d is an orthogonal matrix and Λ ∈ R d×d is a diagonal matrix with positive entries. With Gram-Schmidt process, we could derive QR factorization of W ⊤ c : W ⊤ c = QR, where Q ∈ R d×d is orthogonal and R ∈ R d×k is upper triangular. Plugging above decomposition of Σ 1 2 and W ⊤ c , now we havemin w ∥Σ 1 2 W ⊤ c w -Σ 1 2 w∥ 2 2 = min w ∥ Q⊤ Λ QQR w -Q⊤ Λ Qw∥ 2 2 = min w ∥Λ QQR w -Λ Qw∥ 2 2 ( Q is orthogonal, thus preserves 2-norm) = min w ∥Λ( QQR w -Qw)∥ 2 2 ≤ min w λ 2 max ∥ QQR w -Qw∥ 2 2(Since all entries of Λ are positive.)= λ 2 max min w ∥R w -Q ⊤ w∥ 2 2(Multiply by Q ⊤ Q⊤ preserves the norm) (A.7) where λ max is the largest eigenvalue of Σ E z |f (z) -f (z)| 2 ≤ λ 2 max min w ∥R w -Q ⊤ w∥ 2 2 (A.8)Secondly, we consider when W c is sampled i.i.d. from standard normal distribution, and calculate the expected error. From above derivation,E Wc min ( w, b) E z |f (z) -f (z)| 2 ≤ λ 2 max E (R,Q) min w ∥R w -Q ⊤ w∥ 2 2 (A.9)Note that since W ⊤ c = QR, the randomness in W c is reflected in Q and R. The matrices Q and R satisfies the following properties:1. Q is a random rotation following uniform distribution. This is intuitive because standard Gaussian distribution is rotation-invarant. For a formal statement and proof, we refer to Proposition 7.2 of Eaton [1] . In the following derivation, since we only cares about the expectation, we omit "with probability 1" for brevity. 
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 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [NA] Justification:This work focus on technical development of making neural network models more interpretable.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.• Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
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 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: The paper poses no such risks. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: We properly cite the dataset and models we used. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. 
 
 Appendix: Computational resources and codes. Our experiments run on a server with 10 CPU cores, 64 GB RAM, and 1 Nvidia 2080Ti GPU. Our implementation builds on the open-source implementation of the LF-CBM [14] available: https://github.com/Trustworthy-ML-Lab/Label-free-CBM.For training the final predictive layer, we use publicly available code for GLM-SAGA [24] .Hyperparameter tuning. We tune the hyperparameters for our method using 10% of the training data as validation for the CIFAR10, CIFAR100, CUB and ImageNet datasets. For Places365, we use 5% of the training data as validation. We use CLIP(RN50) image encoder as the backbone for CIFAR10 and CIFAR100, Resnet-18 [2] trained on CUB for CUB dataset, and Resnet-50 pretrained for Places365 following setup similar to LF-CBM. We tune the CBL with Adam [4] optimizer with learning rate 1×10 -4 and weight decay 1×10 -5 . The concept dataset obtained from GroundingDINO is inherently unbalanced since there is a much lower proportion of positive datapoints for a concept. Consequently, we scale the CBL loss by multiplying it with a positive value to balance the tradeoff between precision and recall and improve the imbalance of positive data points. We set T = 0.15 in Eq. ( 2) in all our experiments. We seed the random number generator with a fixed seed to ensure the results can be reproduced.Confidence threshold CUB200 Places365Metrics Acc@5 Avg. Acc. Acc@5 Avg. Acc. Confidence threshold T in Eq 2 filters concepts with bounding boxes' confidence less than T . In this experiment, we study the affect of T on the VLG-CBM's accuracy. The results are shown in Table C .1. We observe that the accuracy at NEM@5 and average accuracy first increases (or stays constant) and then decreases. We attribute this effect to to the fact that as T increases, the number of false-positive decreases leading to better learning of concepts, however, as the number of annotations available for learning a concept decreases.This section quantitatively evaluates concept annotations obtained from Grounding DINO. We use CUB dataset for comparison which contains ground-truth for fine-grained concepts present in each image. We use the label set from Koh et al. [5] which has 1:1 mapping with the ground-truth concepts in the CUB dataset. We use precision and recall metric to measure the quality of annotations from Grouding DINO for each concepts. Table D .1 present mean precision and mean recall value at different confidence threshold. We observe that the obtained annotations have a very high recall i.e if the concept is present in the image, grounding DINO is able to retrieve the object. The precision is also sufficiently high though it suffers from a relatively higher false-positive detection rate compared to false-negative detection rate. However, as demonstrated in our qualitative and quantitative studies (Table 2 In this section, we discuss how to construct models with specified NEC. When using methods with dense final layers (e.g. [25] ), controlling NEC is simply controlling total number of concepts in the concept set. Hence, below we mainly focus on models with sparse final layers.When training the final linear layer, larger lambda(regularization strength) pushes the model to be sparser. Hence, we utilize GLM-SAGA [24] , which allows us to obtain a regularization path consists of different lambdas. To be more specific, we choose a λ max and train models with λ in [λ min = λ max /500λ max ], and take 50 λ evenly from the interval in log space. Then, we choose the weight matrix with the closest NEC and pruning the weights from smallest magnitude to largest to enforce strict NEC. Hence, the actual NEC is enforced to be exactly as prespecified ones.In LF-CBM [14] and our VLG-CBM, normalization is applied on concept logits before the final decision layer. Hence, a negative value of concept logits indicates corresponding concept does not appear in the image. Following LF-CBM, we mark these concepts as "NOT {concept}" in explaining the decision. To study the frequency of this negative reasoning, we count the times these negative concepts appear in top-5 contributing concepts on CUB dataset. The results show that, for VLG-CBM, 162 out of 28950(0.56%) reasonings are through negative concepts. For comparison, LF-CBM utilizes 6687 out of 28950(23.10%) negative reasoning.The study in Section 5.3 shows that our VLG-CBM provides more interpretable decisions than baseline methods. To better understanding where these advantages comes from, we conduct a further study to set the baselines with NEC=5 and compare the decision interpretation, see Figs. G.2 to G.4. The results suggest setting NEC=5 alleviate the problem from non-top-5 concepts. However, wrong/inaccurate/less useful explanations still exist. In this section, we further discuss some interesting phenomena observed in the decision explanations generated by different models.In Fig. 1 , we could see that LM4CV [25] generates negative contribution values, while LF-CBM [14] and our VLG-CBM do not. We hypothesize the reason is different training methods: LM4CV trains a dense final layer, hence the concepts irrelevant to the class may provide a negative contribution. LF-CBM and VLG-CBM, however, train a sparse final layer. To enforce sparsity, the model only captures relevant concepts for decision. Hence, it's natural to expect most contributions should be positive.In this section, we study a question: will our VLG-CBM hurts the generalization ability of original model to Out-Of-Distribution(OOD) dataset? To study this problem, we conduct experiment on Waterbirds dataset [19] . Waterbirds is an OOD dataset adapted from the CUB dataset, which combines bird photos from CUB with image backgrounds from Places365. We use the same ResNet model as we used in Table 2 that our VLG-CBM generalizes well as the standard model does, which shows that our VLG-CBM is competitive and has very small accuracy trade-off with the interpretability compared with the standard black-box model.In this section, we conduct an ablation study on the probability of applying our crop-to-concept data augmentation introduced in Section 3.1. The dataset we used is the CUB dataset and the backbone is ResNet as we used in the main experiment. The results are listed below. From the In this section, we present a human study following the practice of Oikarinen et al. [14] on Amazon MTurk platform. To briefly summarize, we show the annotator top-5 contributing concepts of our method (VLG-CBM) and baseline (LF-CBM or LM4CV) and asking them which one is better. The scores for each method are assigned as 1-5 according to the response of annotators: 5 for the explanations from VLG-CBM is strongly more reasonable, 4 for VLG-CBM is slightly more reasonable, 3 for both models are equally reasonable, 2 for the baseline is slightly more reasonable, and 1 for the baseline is strongly more reasonable. Thus, if our model provides better explanations than the baselines, then we should see a score higher than 3. We show an example screenshot of our study in Fig. J .1.We report the average score in Table J .1 for two baselines: LF-CBM and LM4CV. For each baseline, we randomly sample 200 images and collect 3 results from 3 different annotators. It can be seen that VLG-CBM has scores higher than 3 for both baselines, indicating our VLG-CBM provides better explanations than both baselines. LaBo is excluded in our experiment due to its dense layer and large number of concepts: the top-5 concepts usually account for less than 0.01% of final prediction. This section presents an extended version of Table 4 visualizing NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.Justification: Our research is conducted following NeurIPS Code of Ethics.Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).Figure K.5: Randomly selected explanations for Places365 (Part 3)Figure K.7: Randomly selected explanations for CUB (Part 2) 


Decision: After the rebuttal, the submission received relatively positive reviews (1 Accept, 3 Borderline Accept, and 1 Borderline Reject). The main concerns about the papers are: 1) The novelty of the proposed idea is limited. 2) More comprehensive studies are expected. During the rebuttal, the authors provided extensive new results to show the generalization ability of the proposed method. Overall, I think the submission is worth to be published and recommend **Accept** for this submission. As suggested by reviewers, in the camera-ready version, a more detailed comparison with previous works should be added.

Reviews: review: paper_summary: paper_summary: This paper proposed a framework that leverages existing grounding models to refine the accurate concept annotations, and therefore enables better CBM training. Also, a new metric is proposed to avoid leakage and improve interoperability. Experiments and visualizations are conducted to demonstrate the superior performance of the proposed method.
strength_weakness: strength_weakness: 1. Missing comparison with recent works in CMB, for example,  [1] and [2].

2. How good is LLM at generating candidate concepts, and how the different LLMs' quality would impact the generated candidate quality as well as final performance? An ablation/analysis is needed.

3. How good is the grounding model in terms of object class matching and box location prediction? The ablation of different grounding models and even a random baseline should be added. I am also curious about the training augmentation based on bounding box, how much gain it can bring?

4. It's natural that different classes have different numbers of concepts to distinguish the class because some are complex and some are simple. If forcing the NEC to certain number, would the model automatically learn to assign different numbers of concepts to different classes? An analysis is needed.

Refs:\
[1]. Pham, Thang M., et al. "PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck." arXiv preprint arXiv:2403.05297 (2024). \
[2]. Sun, Ao, et al. "Eliminating information leakage in hard concept bottleneck models with supervised, hierarchical concept learning." arXiv preprint arXiv:2402.05945 (2024).
questions: questions: In summary, my concerns mainly focus on the lack of comprehensive comparison, ablation, and analysis.
limitations: limitations: Yes, it is discussed in Section 6.
paper_summary: paper_summary: The paper introduces Vision-Language-Guided Concept Bottleneck Model (VLG-CBM), an innovative approach to training Concept Bottleneck Models (CBMs) using vision-language models (VLMs). This method aims to improve the faithfulness and performance of CBMs by addressing the limitations of existing models, specifically inaccurate concept predictions and information leakage. VLG-CBM leverages grounded object detectors for visually recognizable concept annotations and introduces a new metric, the Number of Effective Concepts (NEC), to control information leakage and enhance interpretability. The method demonstrates significant performance improvements across multiple benchmarks.
strength_weakness: strength_weakness: 1）The proposed pipeline integrates several independent models, ie, LLM for concept candidate generation, and GroundingDINO for bounding box detection. I'm wondering how well the grounding detector can do in discovering the concept candidate.  And if the detector fails to detect a key concept, will the final recognition result be affected? 

2) Dependence on Pre-trained Models: The reliance on pre-trained VLMs and object detectors may limit the approach's applicability to domains where such models are not readily available or effective. 

3) My major concern is in generalization. While the method shows impressive results on standard image recognition benchmarks, its effectiveness in more diverse or less-structured real-world scenarios needs further validation. For example, how would the VLG-CBM perform for OOD image recognition like NICO or Water-Bird, or how could it handle more complex tasks like VQA?
questions: questions: see weaknesses
limitations: limitations: see weaknesses
paper_summary: paper_summary: This work proposed a new way to implement concept bottleneck models (CBMs), which use pretrained object detectors (Grounding-DINO) to filter and annotate the concepts. This step can make the concepts more visual and groundable, which improves the reliability of the concept predictor. This paper proposed a new metric (NEC) to control the number of concepts that the model uses when making the predictions. In the experiments of 5 datasets, the proposed model shows better performance using the NEC metric.
strength_weakness: strength_weakness: 1. **Limited contribution.** This paper is an incremental work to previous LLM-guided CBMs [1, 2] by adding a step to filter concepts with an object detection model. The findings that "CBM with random concepts can achieve good performance" have also been shown in previous works [3, 4]. From the technical part, the only difference between the proposed model and prior CBMs is the object detection model for concept filtering. However, the detected bounding boxes are not used in the model training and inference. Therefore, the technical contribution is also limited. Overall, this paper makes limited contributions to the community and does not provide enough insights for future work.

2. **Generalizability of the method.** The proposed method relies on an open-vocab detection model. However, open-vocab detection is still a very challenging task, and from my experience, the off-the-shelf models do not work well in many cases. For example, part-based detection is very difficult. In some datasets like DTD or action detection, the concepts are hard to capture in bounding boxes. Even in the evaluated datasets in this paper, like CIFAR, which is in low resolution, the performance of object detection is also quite skeptical. Although the authors have mentioned this limitation in the last section, I don't agree with their arguments that "prior work (e.g., LaBo, LM4CV, LF-CBM) also shared similar limitations on the reliance of CLIP". CLIP definitely has better generalization than the open-vocab detection model because the latter is a more challenging task. This concern is also reflected by the small set of datasets in their experiments, only 5 datasets, compared to what LaBo evaluated (11 datasets).

3. **Baseline Comparison.** The linear probing baseline is missing, which is necessary to justify the effectiveness of CBMs. For CUB, Place365, and ImageNet, the numbers of LM4CV and LaBo are missing. The reason shown in the caption that "they could not be applied on non-CLIP backbones" cannot convince me. I didn't see any difficulties in letting your model support CLIP. You can simply fine-tune CLIP using BCE loss in Eq (6) or just use CLIP as a feature extractor and learn a linear layer over it. In general, the numbers shown in Table 2 are quite low, far behind linear probing based on my experience. It seems the proposed method sacrifices a lot of performance for interpretability, which is not validated properly (see weakness 4). I feel that the performance gain of the proposed method comes from the priors of the object detection model.

4. **Human evaluation to justify faithfulness.** This paper claims that the proposed VLG-CBM is more interpretable and faithful. However, without concrete evidence like large-scale human evaluation, it is hard to justify VLG-CBM as more interpretable by just showing a few qualitative examples.

[1] Yang et al. Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification. CVPR 2023.
[2] Oikarinen et al. Label-Free Concept Bottleneck Models. ICLR 2023.
[3] Roth et al. Waffling around for Performance: Visual Classification with Random Words and Broad Concepts. ICCV 2023.
[4] Yan et al. Learning Concise and Descriptive Attributes for Visual Recognition. ICCV 2023.
questions: questions: How is the threshold of detection confidence (T=0.15) chosen? Can this generalize to different datasets?
limitations: limitations: The authors discussed the limitations in Section 6.
paper_summary: paper_summary: The paper uses foundational models to automate the generation of concept annotations which are then used to train a concept bottleneck model (CBM). To generate the concept set, the paper extends prior scalable CBM approaches[1] by using open-domain object detectors to weed out concepts that are not visually grounded. Concepts that are not present in any bounding-box for any training image are removed. Following existing work,  a CBM is trained sequentially – first, the concept layer is trained and frozen, then a sparse linear layer is trained. The paper introduces a theoretically justified metric, Number of effective concepts (NEC), which is the average number of concepts the sparse final layer uses to make the class prediction. NEC can be used to compare between various CBMs. Results are shown on 5 standard datasets, and they compare against 3 scalable CBM baselines.

[1] Oikarinen, Tuomas P. et al. “Label-Free Concept Bottleneck Models.” ArXiv abs/2304.06129 (2023): n. Pag.
strength_weakness: strength_weakness: \- It is not clear if NEC controls information leakage. As defined in [2], information leakage happens because of a `soft’ concept layer (that predicts probability of a concept instead of presence and absence). Though a small NEC reduces the possibility of information leakage and is a useful metric to check the goodness of the concept set.  
\- A study of time and compute complexity is needed, as every image has to be passed through a large object-detector. The effort to generate concepts is much higher (compared to [1], who generate concepts class wise).  

[1] Oikarinen, Tuomas P. et al. “Label-Free Concept Bottleneck Models.” ArXiv abs/2304.06129 (2023): n. Pag.
[2] Mahinpei, Anita et al. “Promises and Pitfalls of Black-Box Concept Learning Models.” ArXiv abs/2106.13314 (2021): n. pag.
questions: questions: 1. A study of the impact of proposed visually-grounded concept filtering would be interesting. For ex. What fraction of quantitative gains are because of the removed concept set? How much does augmentation contribute to overall performance gains?  
2. As mentioned in the weaknesses section, a section on time complexity of concept set generation would be useful.
3. The theoretical analysis, though appreciated, seems out of place. The result presented in Thm 4.1 is not specific to concept models, but can be seen as a general statement on fidelity of performing dimensionality reduction with a linear random projection. It is not clear to me how NEC follows from here, as NEC is a measure of the sparsity of the final layer, which can be low even for large concept sets.
limitations: limitations: Yes, limitations have been addressed.
paper_summary: paper_summary: In this paper, the authors propose a learning pipeline named VLG-CBM, which enhances CBM with the aid of open-domain object detectors to create more accurate concept labels, while also excluding image-irrelevant concepts (e.g,. "loud music").
Also, in order to prevent the 'information leak' in CBMs, which stands for a phenomenon where the exploitation of irrelevant concepts may help downstream accuracy, authors propose a metric named Number of Effective Concepts (NEC) which enables the fair comparison of different CBMs within a similar amount of information being provided.
Experimental results show that with the proposed VLM-CBM, high-quality concept labels are obtained therefore achieving better performances within similar NEC.
strength_weakness: strength_weakness: 1. Only experimental results under controlled NEC are reported, making it difficult to compare the performance of VLG-CBMs with previous works in conventional settings. For instance, the average accuracy of LaBo in the CIFAR-100 dataset under an NEC-controlled setting is reported as 55.18%, while the full performance in the original paper is around 86% (86.82 in the dev set, 86.04 in the test set). Therefore, also comparing the performance of VLG-CBM under conventional settings in previous work would make the comparison easier.

2. The idea of applying a detection or a grounding module to generate an image annotation is already widely explored (e.g., KOSMOS-2 [1]). The proposed data generation process does not significantly differ from previous approaches, limiting the originality of the proposed method.


[1] Peng et al., KOSMOS-2: Grounding Multimodal Large Language Models to the World.
questions: questions: 1. Questions regarding Table 3. 1) How is the performance of each method? Only the percentage of changed decisions are reported, therefore providing accuracies may help a better understanding of the effectiveness of each method. 2) Isn't it obvious that the percentage of changed decisions is lower in VLG-CBM since the sparsity constraint is not applied to other methods? Can you also provide the same analysis with sparsity constraint applied to other methods?

2. Providing qualitative examples of labeling results using Grounding-DINO would help in understanding the quality of the auto-labeled dataset, which seems to be absent in the current version of the paper.

3. Also, please address the concerns elaborated in the weakness section.
limitations: limitations: Authors have described limitations and possible societal impacts in the main paper and the supplementary material.

Comments: title: Thank you for raising the score!
Follow-up and Additional results to show the generalizability of our methods
Additional results for the quality of concept sets in Q2
Clarification on our novelty and contributions - Part 3
Clarification on our novelty and contributions - Part 2
Clarification on our novelty and contributions - Part 1
Clarification on misunderstanding of our contributions - Part 2
Clarification on misunderstanding of our contributions - Part 1
comment: Dear Reviewer i5YZ,

We are happy to see our clarification has addressed your concern on the contribution of our paper, and we appreciate your decision to raise the score to 5! 

We will follow your suggestion to update the manuscript with a detailed comparison and discussion with previous works and make it more clear about the novelty of our methods in the revised version. 

Thank you for the discussion and we appreciate your time and feedback!
Thanks for your response.
I appreciate the authors for providing a detailed comparison with previous works, which makes the position of the paper more clear.

Since now I better understand the novelty of the paper, I am raising the score by one.

Still, I highly recommend the authors include a detailed comparison with previous works in the manuscript.
Since the contributions of the paper are built upon previous observations, clearly stating the novel points and importance of the Grounding-DINO approach and NEC metric may help readers better grasp the value of the paper.
Dear Reviewer QueL,

As the rebuttal period is close to an end, please let us know if you still have any remaining concerns. We have tried to address all your concerns in our rebuttal response and we also give additional clarification on our contributions to address your new comments yesterday. 
 
While we are waiting for your follow-up response, we would like to share additional results on four new datasets to further address your concerns in Weakness #2 and #3. In the rebuttal, we have provided some of the additional experiments results in **A5** as requested and now we would like to provide more results to further support the generalizability of our methods. 

**1. Additional results on 4 more datasets**

Specifically, we provide additional results on four new datasets that Labo [1] used: Aircraft, Flower102, Food101, and DTD. For a fair comparison of our method with  LaBo[1], we have followed the same dataset splits as used by LaBo and reported accuracy for both LaBo and VLG-CBM on the Resnet50-CLIP backbone. The results of our experiments are reported in the table below. We observe that VLG-CBM consistently outperforms LaBo on Aircraft, Flower102, Food101, and DTD dataset by as much as 8.16% points on average accuracy, and 12.72% on Acc@5. 

This demonstrates that our method can successfully generalize to fine-grained and challenging classification datasets (e.g. DTD as mentioned by the reviewer) and outperform existing state-of-the-art CBMs like Labo. Additionally, our original result on Cifar 10 and Cifar 100 also outperforms the baselines by a large margin (e.g. on Acc accuracy, our method is 3-19% better on Cifar 10, and 4-29% better on Cifar 100) as shown in the Table 2 in our manuscript. Furthermore, on larger dataset like Imagenet, it’s 0.5-36% better on the Avg accuracy, as shown in the Table R1 in the global response of the rebuttal.  

Table R4: Comparison on additional 4 datasets

| Dataset   |     Acc@5    |        |   Avg. Acc   |        |
|-----------|:------------:|:------:|:------------:|:------:|
|           | Ours(VLGCBM) | LaBo   | Ours(VLGCBM) | LaBo   |
| Aircraft  | 36.54%       | 23.82% | 39.26%       | 31.10% |
| Flower102 | 94.19%       | 81.89% | 95.36%       | 93.06% |
| Food101   | 78.02%       | 66.46% | 79.58%       | 75.05% |
| DTD       | 67.67%       | 60.40% | 69.82%       | 66.37% |

Note: The results in **A5** were generated for Food and Flower dataset following splits from the pytorch library. To have a fair comparison with LaBo, we follow LaBo’s split as described below, which is different from the standard split in the pytorch library used in **A5**, resulting in a slight difference in the result number. 

| Dataset | Pytorch-test-size | Labo-test-size |
|---------|-------------------|----------------|
| Food    | 25,250| 30,300 |
| Flower  | 1,020 | 2,463|

The above results will be included in the revised manuscript. 

---

**2. To sum up, we believe that we have addressed all your concerns in the 4 Weaknesses points in the review comments with the extensive evaluations, additional requested experiments, and clarifications.**

Specifically,  

* The **weakness point #1** on **Limited contribution** is addressed by our clarification response yesterday (Aug 12) with title “Clarification on misunderstanding of our contributions - Part 1” and “Clarification on misunderstanding of our contributions - Part 2”. We clarified that there are 3 main contributions (the first end-to-end pipeline, the first theoretical analysis on CBM leakage with random concepts, and a new and useful metric for fair comparison between CBM) and explain why each of them are non-trivial and novel to the field. 
* The **weakness point #2** on **Generalizability of the method** is addressed by our rebuttal and additional result, which shows that our methods generalize to the DTD and CIFAR dataset that the reviewer mentioned to be challenging and outperform existing methods. 
* The **weakness point #3** on **Baseline Comparisons** is addressed by our rebuttal and additional results, because we have shown that our methods outperform existing methods on numerous datasets, including Cifar 10, Cifar 100, ImageNet, CUB, Place365, Flower 102, Food 101, DTD, Aircraft, as demonstrated in Table 2 in the manuscript, Table R2 in the global response, and Table R4 in this response. 
* The **weakness point #4** on **Human evaluation to justify faithfulness** is addressed by our rebuttal with human study in the global response of rebuttal (Table R3) where we show that our methods outperforms the baselines in the human study. 

We value the reviewer’s input and welcome any additional comments. If our clarifications and additional results address your major concerns, we would appreciate your consideration in raising the score. Thanks for your time and feedback.  

---

## Reference

[1] Yang etal. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. CVPR 23.
Dear Reviewer hsJW,

We would like to provide additional results regarding your concerns in **Q2** on the quality of concept sets obtained from different LLMs. We have conducted an ablation study to obtain concept sets from two additional LLMs (LLama3-8B and GPT4o-Mini) following methodology in [1]. 

The results of our experiments are reported in the table below. We observe that the results are similar across three different LLMs, with a slight deviation within 0.5%. Specifically, we find that GPT4oMini performs the best, achieving a maximum average accuracy of 76.11% on the CUB dataset. We will include this ablation study to the revised draft.  

Please let us know if you have any additional questions or concerns, and we would be happy to resolve them! 

|               | Acc@NEC=5 | Avg. Acc |
|---------------|-----------|----------|
| CUB_GPT3      | 75.79     | 75.82    |
| CUB_LLama3    | 75.57     | 75.73    |
| CUB_GPT4oMini | 76.07     | 76.11    |

---

## Reference 
[1] Oikarinen et al. Label-free concept bottleneck models. ICLR 23
## Reference
[1] Kim et al., Concept bottleneck with visual concept filtering for explainable medical image classification, MICCAI Workshop 2023

[2] Roth et al., Waffling around for Performance: Visual Classification with Random Words and Broad Concepts, ICCV 2023

[3] An et al. "Learning concise and descriptive attributes for visual recognition." ICCV 23

[4] Mahinpei et al. “Promises and pitfalls of black-box concept learning models.” ICML 21
Additionally, we would like to clarify that Grounding-DINO is not applied to just removing the concept set. In fact, removing noisy LLM-based concepts is just a natural outcome of our method, and this idea is actually more powerful than that: Based on Grounding-DINO we design a novel training pipeline: unlike CLIP, the Grounding-DINO model could generate bounding boxes for each concept. Our VLG-CBM designed a data augmentation mechanism (line 134-136 of the draft) to utilize this spatial and localized information, which is a key novelty compared to previous CLIP-based methods like [1]. 

---

#3 Details of our **Contribution (2)**

This is a contribution that has been missed by the reviewer and we would like to give more details on why this contribution is novel and important. As described in #1, our **Contribution (2)** is the **first** rigorous theoretical analysis to prove that CBMs have serious issues on information leakage in Sec 4.1. 


As we pointed out in the original manuscript (Line 40-42, Line 152-162), information leakage is an important issue that was observed by prior work [3, 4]. However, there is no rigorous theoretical understanding of this phenomenon in [3, 4], i.e. why CBM can achieve good accuracy even on random concepts? In fact, we are the first work to provide the first theoretical analysis and proof to explain this phenomenon: we prove in Thm 4.1 that a CBM can achieve optimal accuracy even on random conconcepts when the number of concepts is large enough. 

Our Thm 4.1 suggests that the number of concepts is an important factor of information leakage, and it also show that how the approximation error is controlled by number of concepts, which inspired us to control information leakage via the novel NEC metric that we propose in Sec 4.2 (which is our **Contribution (3)**). 

---

#4 Details of our **Contribution (3)** and how is our work different from [2, 3]? 

As we clarified in the above point **#1**, our **Contribution (3)** is a **new and useful** metric to control information leakage problems that can facilitate fair comparison between different CBMs in Sec 4.2 and Sec 5. 

As we described in the introduction of the draft (see lines 34-42), existing CBMs have serious problems of faithfulness due to information leakage problems. Without this metric, the performance of CBMs do not correctly reflect their “true” performance, which causes a false sense of confidence. Indeed, as we show in Table 2 in the draft and Table R1 in the Global response in the rebuttal, under the fair comparison setting, many existing CBMs' true performance are much worse than what they were reported. For example, using Acc@NEC=5, LF-CBM has around 2%-21% drop, Labo has around 20%-58% accuracy drop, LM4CV has around 34%-63% accuracy drop than reported, providing a false sense of performance. 

Additionally, we would like to highlight that our proposed new metric NEC is a theoretically grounded solution to control information leakage problems and has much more flexibility in concept choices and better interpretability compared with the previous approach [3]. In [3], the authors proposed to control the total number of concepts, which however limits the choice of concepts and can harm the interpretability due to dense connection, as supported by the human study results in **General Response #3** and the qualitative example in **Fig. 1** and **Fig. H.2 - H.4** in the appendix of our draft.

For [2], we would like to clarify that [2] is mainly focused on using random words to improve the CLIP zero-shot classification, which is a different problem from what we are solving for random concepts in the CBMs in Sec 4. Although the results in [2] are interesting and show that using random words and characters could achieve comparable zero-shot classification performances, it is technically different from the information leakage problem that we study in CBMs in Sec 4.  

We will add the discussion of paper [2] in the revised draft and we have already cited [3] in our draft (please see line 155-158).

---

## Summary 
To sum up, we clarify that there are 3 main contributions in our work, clarify the novelty of our methods, and compare them to [1-3]. Specifically, we address the reviewer’s additional comments by 
* Clarifying our 3 contributions in **#1**
* Clarifying the novelty of our **Contribution (1)** and compared it with **[1]** in **#2**
* Clarifying the importance and novelty of our **Contribution (2)** in **#3**
* Clarifying  the novelty of our **Contribution (3)** and compared it with **[2, 3]**  in **#4**


Please let us know if you still have any concerns, we would be happy to discuss further.
Dear Reviewer i5YZ,

Thanks for your response! We are happy to learn that some of your concerns have been addressed in our rebuttal response, and we appreciate the opportunity to provide you with additional clarifications regarding our contributions and the novelty of our methods as requested in the new comments.  

**#1 Our main contributions and novelty**

We would like to clarify that our contribution is not limited to the two as the reviewer has mentioned, and the Reviewer QueL actually has some misunderstandings of our contributions which we clarified in the new response (please see post title: **Clarification on misunderstanding of our contributions - Part 1 & Clarification on misunderstanding of our contributions - Part 2**) and our original rebuttal response to Reviewer QueL (please see our response **A1 and A2**). 

Below we provide the details of our contributions, explain why our work is novel, and compare with the reference [1-3] as provided by the reviewer. 

First of all, there are **three** main contributions of our work: 
* **Contribution (1):** the **first** end-to-end pipeline to build CBM with vision guidance from open-vocabulary object detectors in Sec 3; 
* **Contribution (2):** the **first** rigorous theoretical analysis to prove that CBMs have serious issues on information leakage in Sec 4.1, whereas existing work [3, 4] only have empirical observations; 
* **Contribution (3):** a **new and useful** metric to control information leakage problems that can facilitate fair comparison between different CBMs in Sec 4.2 and Sec 5. 


Our **Contribution (1) and (3)** has been somehow (but not fully) recognized by the reviewer and our **Contribution (2)** was missed by the reviewer. The reviewer is correct that our **Contribution (1)** is to address non-visual concepts generated by LLMs and our **Contribution (3)** is to address the information leaks in CBM. Although there is some previous work [1-3] mentioned by the reviewer, they are limited by the following points:
* These works do not solve the problem fully
  * e.g. [1] is more related to Labo and still uses a CLIP-backbone, whereas our pipeline leverages open-vocabulary object detectors and is more comprehensive and can provide localized concepts information. Please see our below response **#2** for more details. 
  * e.g. [3] only has empirical observation and did not provide theoretical analysis, whereas we provide the first theoretical analysis and proof to analyze the random concepts in CBMs. Please see our below response **#3 and #4** for more details. 

* Some of the work is not solving the same problem as we did
  * e.g. [2] focus on CLIP zero-shot classification, which is a different problem from what we are solving for random concepts in the CBMs in Sec 4. Please see our  below response **#4** for more details.  

Together with our 3 contributions, the extensive evaluations in sec 5 (see Table 2, 3, Figure 1,3, 4), and global response in the rebuttal (Table R1, R2, additional experiments, ablation study and human study) supports the superiority of our method and shows that our VLG-CBM framework is a promising approach towards building a faithful, reliable, and high performance CBM. More specifically, our method provides more favorable explanations than the baselines by around 18% in human study, our method also achieves up to 29.78% improvement on average accuracy and gets up to 51.09% improvement on accuracy at NEC=5. 

---

#2 Details of our **Contribution (1)** and how is our work different from [1]?

To our best knowledge, there is no other work in the CBM literature that has the idea of leveraging object detection to address the concept faithfulness problem in CBMs. We are the first to provide this elegant and effective approach to address the inaccurate concept prediction problems. In [1], the key idea is to filter out the non-visual concepts by adding a vision activation term to the concept selection step. However, the main training pipeline is still CLIP-based, similar to LaBo. Additionally, our method in Sec 3 is more comprehensive and utilizes open-vocabulary object detectors in multiple stages of CBM pipeline: Obtaining annotations for multi-class training, filtering non-visual concepts, and utilizing predicted bounding boxes for improving classification performance. We will cite the paper [1] and discuss the above differences in the revised manuscript.
To give more details, our work encompasses the following additional crucial steps in the pipeline and have not been studied previously in the context of CBMs:
1. **Handling False-positives from foundational models**: False-positive outputs from foundational vision models currently limit their use in training CBMs. Our work provides a simple, yet clever, way to solve the issues with false positive detections: i) We use concepts corresponding to a class for obtaining annotation for an image. ii) We use confidence threshold to remove bounding boxes with low confidence. As demonstrated in the appendix D along with Table 2, Fig 4, Fig G.1 and G.2, and the ablation study on threshold in Table C.1 in appendix C, this design significantly reduces the effect of false-positives, making foundational vision models viable for training CBMs
2. **Using obtained bounding boxes for training**: We use the generated bounding box for training the CBL layer. As mentioned in Ln 134-136 in our draft, we augment the training dataset by cropping images to a randomly selected bounding box and modifying the target one-hot vector to predict the concept corresponding to the bounding box. We have provided an ablation study highlighting the effectiveness of this approach in global response under #2 ablation studies.

---

**#3**. Why is our **Contribution (2)** novel and important to the field?

As we clarified in the above point **#1**, our **Contribution (2)** is the **first** rigorous theoretical analysis to prove that CBMs have serious issues on information leakage in Sec 4.1, whereas existing work [1] only has empirical observations.  

As we pointed out in the original manuscript (Line 40-42, Line 152-162), information leakage is an important issue that was observed by prior work [1, 2]. However, there is no rigorous theoretical understanding of this phenomenon in [1, 2]. As we responded in our **A2** in the rebuttal response, we are the first work to provide the first theoretical analysis to explain this phenomenon and therefore identify the number of concepts as an important factor of information leakage in Thm. 4.1, which inspired us to control information leakage via the novel NEC metric that we propose in Sec 4.2 (which is our **Contribution (3)**). 

---

**#4.** Why is our **Contribution (3)** novel and important to the field?

As we clarified in the above point **#1**, our **Contribution (3)** is a **new** and **useful** metric to control information leakage problems that can facilitate fair comparison between different CBMs in Sec 4.2 and Sec 5. 

We respectfully disagree with the reviewer's comments that *"I don't think a new metric is a big contribution"*. As we described in the introduction of the draft (see lines 34-42), existing CBMs have serious problems of faithfulness due to information leakage problems. Without this metric, the performance of CBMs do not correctly reflect their "true" performance, which causes a false sense of confidence. Indeed, as we show in Table 2 in the draft and Table R1 in the Global response in the rebuttal, under the fair comparison setting, many existing CBMs' true performance are much worse than what they were reported. For example, using Acc@NEC=5, LF-CBM has around 2%-21% drop, Labo has around 20%-58% accuracy drop, LM4CV has around 34%-63% accuracy drop than reported, providing a false sense of performance. 

Additionally, we would like to highlight that our proposed new metric NEC is a theoretically grounded solution to control information leakage problems and has much more flexibility in concept choices and better interpretability compared with the previous approach [1]. In [1], the authors proposed to control the total number of concepts, which however limits the choice of concepts and can harm the interpretability due to dense connection, as supported by the human study results in **General Response #3** and the qualitative example in **Fig. 1** and **Fig. H.2 - H.4** in the appendix of our draft.

---

## Summary
To sum up, we believe that there are misunderstandings from the reviewer on our contributions and novelty due to inaccurate description. To clarify the misunderstanding, in this response, we address the reviewer's additional comments by 
* Clarifying our 3 contributions in **#1**
* Clarifying the importance and novelty of our **Contribution (1)** in **#2**
* Clarifying the importance and novelty of our **Contribution (2)** in **#3**
* Clarifying the importance and novelty of our **Contribution (3)** in **#4**

Please let us know if you still have any concerns, we would be happy to discuss further. 


## Reference
[1] An et al. "Learning concise and descriptive attributes for visual recognition." ICCV 23

[2] Mahinpei et al. "Promises and pitfalls of black-box concept learning models." ICML 21
Dear Reviewer QueL,

Thanks for the response! We appreciate the opportunity to further discuss our work and clarify a few more points that might have been misinterpreted or misunderstood. Based on the inaccurate descriptions of our contributions in the new comments, we are concerned that our work is undermined by the reviewer’s comments. 

Below we provide detailed explanations (#1-#4) of our main contributions to the field and discuss why they are both important and non-trivial. We believe these clarifications will effectively demonstrate the novelty and impact of our work. 

**#1.** First of all, there are **three** main contributions of our work instead of **two** that the reviewer mentioned, which we have already clarified in our rebuttal response **A1** that there are **three contributions** of our method: 
- **Contribution (1)**: The **first** end-to-end pipeline to build CBM with vision guidance from open-vocabulary object detectors in Sec 3; 
- **Contribution (2)**: The **first** rigorous theoretical analysis to prove that CBMs have serious issues on information leakage in Sec 4.1, whereas existing work [1, 2] only have empirical observations; 
- **Contribution (3)**: A **new** and **useful** metric to control information leakage problems that can facilitate fair comparison between different CBMs in Sec 4.2 and Sec 5. 

Notably, as we described in the introduction of the draft (see lines 34-42), existing CBMs still face two critical challenges that urgently needs to be fixed: 
- (a) inaccurate concept prediction problem, where the predicted concepts do not match the image and hurt the faithfulness of CBM; 
- (b) information leakage problem, where task-irrelevant concepts (or even random concepts) can produce a high accuracy CBMs, which also raise concerns on the faithfulness of the CBMs.   

Specifically, our **contribution (1)** is to address the problem of inaccurate concept prediction in (a) and our **contributions (2)&(3)** are to address the problem of information leakage problem in (b). 

Together with our 3 contributions, we show in extensive evaluations in sec 5 (see Table 2, 3, Figure 4), and global response in the rebuttal (additional experiments, ablation study and human study) that our VLG-CBM framework is a promising first step towards building a faithful, reliable, and high performance CBM, which is currently lacking in the field and literature. More specifically, our method provides more favorable explanations than the baselines by around 18% in human study, our method also achieves up to 29.78% improvement on average accuracy and gets up to 51.09% improvement on accuracy at NEC=5. 

---

**#2**. Why is our **Contribution (1)** non-trivial and important to the field?

As we clarified in the above point **#1**, our **Contribution (1)** is the **first** end-to-end pipeline to build CBM with vision guidance from open-vocabulary object detectors described in Sec 3. 

We respectfully disagree with the reviewer’s comment that “*adding object detection in CBM feels very trivial*”. To our best knowledge, there is no other work in the CBM literature that has the idea of leveraging object detection to address the concept faithfulness problem in CBMs. We are the first to provide this elegant and effective approach to address the inaccurate concept prediction problems. 

Besides, the reviewer mentioned that using the open vocabulary detection model is very challenging in **Q4**, which exactly indicates that it is non-trivial to apply open-vocabulary object detectors directly to the pipeline. As we responded in **A4** of our rebuttal response, we pointed out that open-vocabulary object detectors could indeed suffer from false-positive detections and we described how we have addressed this challenge in our pipeline.
Thanks for answering my questions. After reading your response and other reviewers' comments, I still believe this paper's contributions are limited. The main contritions of this paper are adding Grounding DINO in CBM and introducing a new metric. Adding object detection in CBM feels very trivial, and I don't think a new metric is a big contribution.
Thank you for your response in a limited time.

Although some of my concerns have been addressed (e.g., questions regarding Table 3 and request for qualitative results), I still feel that the contribution is somewhat limited, which is also pointed out by reviewer QueL.

For me, the novel point of this paper seems to be
1. Introducing the idea of utilizing Grounding-DINO to remove noisy concepts generated by LLMs,
2. Proposing the new evaluation metric, NEC, quantifying the previously defined problem of information leaks.

Since the problem of non-visual concepts being generated by LLMs [1] and information leaks in CBM [2, 3] have been discovered before, it seems that the novelty in the problem definition or its solution is somewhat limited for me.

Therefore, could the authors provide additional points that I am missing that make the problem definition or its solution's novelty clearer?

[1] Kim et al., Concept bottleneck with visual concept filtering for explainable medical image classification, MICCAI Workshop 2023

[2] Roth et al., Waffling around for Performance: Visual Classification with Random Words and Broad Concepts, ICCV 2023

[3] Yan et al., Learning Concise and Descriptive Attributes for Visual Recognition, ICCV 2023
Dear Reviewers,

Thanks for your efforts in reviewing this paper again! The authors have provided a detailed rebuttal to answer your concerns and questions. Please take a look, and let us (PCs, SACs, ACs, peer reviewers, and authors) know whether your concerns have been addressed. If not, please highlight your remaining concerns. And the authors still have opportunities to answer your questions.

Best,

AC