Few-Shot Multimodal Explanation for Visual Question Answering

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-08 17:31:56

Abstract: A key object in eXplainable Artificial Intelligence (XAI) is to create intelligent systems capable of reasoning and explaining real-world data to facilitate reliable decision-making. Recent studies have acknowledged the importance of providing user-friendly and verifiable explanations to facilitate trustworthy Visual Question Answering (VQA) systems. This paper aims to promote explainable VQA from both data and method perspectives. First, we propose a new Standard Multimodal Explanation (SME) dataset and a new Few-Shot Multimodal Explanation for VQA (FS-MEVQA) task, which aims to generate the multimodal explanation of the underlying reasoning process for solving visual questions with few training samples. Our SME dataset includes 1,028,230 samples composed of questions, images, answers, and multimodal explanations, which can facilitate the research in both traditional MEVQA and FS-MEVQA. To the best of our knowledge, this is the first large-scale dataset with joint language-vision explanations based on standard English and additional visual grounding tokens, which bridge MEVQA to a broad field in Natural Language Processing (NLP). Second, we propose a training-free Multimodal Explaining Agent (MEAgent) method based on an LLM agent with multimodal open-world tools to infer answers and generate multimodal explanations for visual questions. Our MEAgent can learn multimodal explaining from merely $N(=16)$ training samples and leverage open-world abilities to perform FS-MEVQA on test samples. Comprehensive experimental results evaluated by language quality metrics, visual detection metric, and visual attribution metrics on our SME dataset indicate the superiority of our method for FS-MEVQA, compared to state-of-the-art MEVQA methods and the multimodal LLM GPT-4V.

Main paper: 
 introduction: 
 Reasoning is a fundamental element of human intelligence and a crucial challenge in artificial intelligence [9, 10, 18] . Traditional reasoning models typically provide answers without offering explanations for their reasoning. This limitation greatly restricts their applicability, particularly in safety-sensitive situations, such as healthcare, transportation, and finance. To address this problem, a key object in eXplainable Artificial Intelligence (XAI) is to create intelligent systems capable of reasoning and explaining real-world data to facilitate reliable decision-making [11, 37, 42] . The recent development of Large Language Models (LLMs) has led to remarkable progress in reasoning on textual data and generating textual explanations for reasoning processes [12, 26] . However, when it comes to the field of multimodal reasoning, tasks necessitate the ability to comprehend multimodal content and generate multimodal explanations to reveal the underlying reasoning processes, which is still a challenging problem.While Visual Question Answering (VQA) [3, 4, 13, 15, 32] is a typical and important multimodal reasoning task, some pioneering work has been made in Multimodal Explanation for VQA (MEVQA) [7, 50, 51] . Zellers [51] construct the VCR dataset with four explanation choices for every question. The proposed task is to select the correct explanation, which is usually impractical. Moreover, VCR only considers people as visual objects in explanation, limiting its application range. Recently, Chen and Zhao [7] convert the reasoning step graph to multimodal explanations, proposing the REX dataset and a generative task, as shown in Figure 1 . However, their customized format reduces the readability of the explanations. Moreover, they use a Faster R-CNN [36] trained on MS-COCO [25] to extract object boxes for explanation annotation, which can only detect 81 classes and bring inaccuracy in explanations. Furthermore, existing generative methods for multimodal explaining [7, 50] rely on large-scale training data, which is costly and may be impractical in various vertical domains. As shown in Figure 2 , when trained on a small amount of data, state-of-the-art methods experience a notable decline in performance, despite being built upon pretrained Vision-Language Models (VLMs) Our constructed explanations are based on reasoning steps for solving the visual questions, with the associated key objects in Visual Genome scene graphs [17] of 1,703 classes. After converting the structured reasoning steps into natural language-like explanations with visual object annotations, we utilize GPT-3.5 [6] to revise the explanations without [𝐵𝑂𝑋 ] into standard English. Moreover, to facilitate an effective evaluation of visual objects grounded in multimodal explanations, we annotate key visual objects with their corresponding names mentioned in explanations and add special [𝐵𝑂𝑋 ] tokens to represent grounded visual boxes. We further propose a visual detection metric to evaluate the ability to simultaneously generate names and ground regions of key visual objects to explain the multimodal reasoning processes. As shown in Table 1 , compared to the existing explanations for VQA, our multimodal explanations based on standard English with additional [𝐵𝑂𝑋 ] tokens can be more user-friendly and expressive with far more visual objects involved in explanation, bridging MEVQA to a broad area in Natural Language Processing (NLP)..To overcome the dependency on large-scale training data, we propose a training-free Multimodal Explaining Agent (MEAgent)  
related work: 
 We review three highly related topics in explainable Visual Question Answering (VQA).Textual explanation for VQA. Researchers have long recognized the importance of explanations in yielding verifiable reasoning results and establishing trustworthy VQA systems. Early attempts focus on providing textual explanations [21, 22, 28, 30, 44] . Zhou et al [53] propose a multi-task model to predict the answer to a visual question and generate the image caption to explain the answer simultaneously. Since their VQA data and image captioning data are from independent sources, the caption may be irrelevant to the question. Following their work, Li et al. [22] propose the VQA-E dataset by adopting the answer-related caption as the explanation. Differently, Wen and Peng [47] propose to retrieve related text from databases to explain the reasoning process.Though textual explanations are simple and easy to obtain, researchers have also acknowledged their limitations. While VQA involves reasoning on multiple modalities, textual explanations often fail to effectively explain the visual concepts involved in the reasoning processes. Therefore, recent research has begun to explore Multimodal Explanation for VQA (MEVQA).Multimodal explanation for VQA. Early research on MEVQA attempts to provide individual visual explanations in addition to textual explanations [34, 48] . These works typically relate visual attention maps to the predicted answers or explanations. However, attention maps can be confusing. For example, textual explanations "a man riding on a horse" and "a horse riding on a man" can have similar attention maps. To provide more expressive and accurate explanations of the reasoning processes, joint language-vision explanations are proposed [7, 50, 51] . For example, Chen and Zhao [7] propose to represent visual objects in the textual explanations by their grounding number predicted by a trained Faster R-CNN [36] , belonging to only 81 classes. Moreover, their constructed explanations are in unnatural format, reducing the readability and applicability. Following their research, Xue et al. [50] recently propose a variational causal model to improve answer-explanation consistency by establishing the corresponding causal correlation.However, traditional MEVQA methods rely on large-scale training data, which are costly and may be impractical in various vertical Few-shot explanation for VQA Recent studies have found that LLMs are powerful few-shot learners [6, 23, 38] . Therefore, several LLM-based few-shot explanation methods have been developed [28, 29] , which mainly focus on textual explanation. For example, Lu et al. [28] input the image caption and the question with incontext examples into GPT-3 to generate a chain-of-thought (i.e., textual explanation) and the answer. Different from the existing methods, we propose a few-shot method to generate multimodal explanations for visual questions, thereby enhancing the understanding of multimodal reasoning processes for VQA. 
dataset: 
 Explaining reasoning processes for visual questions can aid in understanding and verifying the predicted answers, thereby enhancing the reliability and credibility of VQA systems. Our proposed dataset aims to provide an effective benchmark for MEVQA. Compared to previous multimodal explanations for VQA [7, 34, 51] , our dataset has three key advantages: (1) Instead of separate textual and visual explanations [34] , we integrate language and vision to construct brief but expressive explanations; (2) Instead of using unnatural formats [7, 51] , our explanations are in standard English with additional [𝐵𝑂𝑋 ] tokens for visual grounding, bridging to broad advancements in Natural Language Processing (NLP). We annotate both object boxes and their corresponding names mentioned in explanations, which support (3) a more effective visual metric for measuring the ability to simultaneously generate names and ground regions of key visual objects to explain the multimodal reasoning processes. Figure 3 demonstrates a brief construction procedure of our multimodal explanations. Data collection. We leverage VQA samples in the GQA dataset [15] . Given the 127 different operations in GQA, we first represent each reasoning step in the reasoning process by a <operation, relation/attribution, dependency 1, dependency 2> tuple, where "dependency 1" and "dependency 2" can be previous reasoning steps. Then, we follow [7] to categorize all operations into 12 atomic operations that cover the essential semantics, i.e., 𝑆𝑒𝑙𝑒𝑐𝑡, 𝐸𝑥𝑖𝑠𝑡, 𝐹𝑖𝑙𝑡𝑒𝑟 , 𝑄𝑢𝑒𝑟𝑦, 𝑉 𝑒𝑟𝑖 𝑓 𝑦, 𝐶𝑜𝑚𝑚𝑜𝑛, 𝑆𝑎𝑚𝑒, 𝐷𝑖 𝑓 𝑓 𝑒𝑟𝑒𝑛𝑡, 𝐶𝑜𝑚𝑝𝑎𝑟𝑒, 𝑅𝑒𝑙𝑎𝑡𝑒, 𝐴𝑛𝑑, and 𝑂𝑟 . For example, to answer "Does the cucumber that is to the left of the tomato look thin and green?", we need to find the tomato first by executing "<𝑆𝑒𝑙𝑒𝑐𝑡, 𝑡𝑜𝑚𝑎𝑡𝑜, 𝑁𝑜𝑛𝑒, 𝑁𝑜𝑛𝑒>", or "𝑆𝑒𝑙𝑒𝑐𝑡(𝑡𝑜𝑚𝑎𝑡𝑜)" in abbreviation.With reasoning step tuples, we need to convert reasoning steps into explanations. Previous work [7] uses a Faster-RCNN [36] trained on MS-COCO [25] to annotate visual objects related to reasoning steps, since their proposed explanation model also uses Faster-RCNN to extract visual objects. However, Faster-RCNN is often inaccurate for annotating objects in the GQA dataset. In contrast, we traverse the human-annotated scene graphs to obtain the results of all steps and link reasoning steps with their related visual objects. Then, we convert the reasoning process graphs into explanations with templates designed for all operations. For example, a 𝑉 𝑒𝑟𝑖 𝑓 𝑦 operation "<𝑉 𝑒𝑟𝑖 𝑓 𝑦, 𝐴𝑇𝑇 , 𝐷𝐸𝑃1, 𝑁𝑜𝑛𝑒>" is converted into "(DEP1) is [RETURN] ATT", where [RETURN] is "not" if this operation returns "no" or empty otherwise. (DEP1) is the phrase converted from the dependent operation. Moreover, we use the [𝐵𝑂𝑋 ] tokens in explanations to represent the grounding boxes of visual objects related to the reasoning steps. To facilitate the visual metric presented in Section 3.2, we annotate both the values of grounding boxes and their corresponding names mentioned in the explanations. We also design programs to correct some grammar issues in generated explanations, e.g., merging two 𝑉 𝑒𝑟𝑖 𝑓 𝑦 operations in Figure 3 . However, rigid programs cannot address all language errors in the constructed explanations. Therefore, we leverage the powerful language ability of GPT-3.5 [6] to correct our explanations. To avoid disruptions of [𝐵𝑂𝑋 ], we remove [𝐵𝑂𝑋 ] in the explanations and utilize GPT-3.5 to convert the explanations into standard English. Then, we add back the [𝐵𝑂𝑋 ] tokens according to their names. Finally, we take several rounds of manual checks to further clean the constructed explanations. After removing a small number of low-quality samples, we obtain 1,028,230 multimodal explanations in standard English with additional [𝐵𝑂𝑋 ] tokens for visual grounding. All data are divided into 901,203 training samples, 97,027 validation samples, and 30,000 test samples. In the MEVQA task, the model is required to answer the visual question, generate a textual representation of the explanation (e.g., "There is a cucumber [BOX] to the left of the tomato [BOX] that is green and not thin. "), and grounds visual boxes linked to all [𝐵𝑂𝑋 ] tokens.To evaluate the generated multimodal explanations (aka., reference explanations), we adopt textual and visual metrics. For textual metrics, since we only add [𝐵𝑂𝑋 ] into standard English, we directly adopt widely-used language metrics, i.e., BLEU-4 [33] , ME-TEOR [5] , ROUGE-L [24] , CIDEr [43] , and SPICE [2] . For visual metrics, we propose a new detection metric to comprehensively evaluate the ability to generate the names of visual objects and ground their corresponding regions in images. For every object name 𝑠 annotated in a ground truth explanation (e.g., "cucumber" and "tomato" in Figure 3 ), we match the [𝐵𝑂𝑋 ] token following 𝑠 in the reference explanation. Then, we compute the IoU (intersection over union) score between the ground truth boxes 𝐵 𝑠 𝑔𝑡 of 𝑠 and the reference boxes 𝐵 𝑠 𝑟𝑒 related to this [𝐵𝑂𝑋 ] token, evaluating the detection precision of this object. The final detection score of one explanation is averaged over all object names, as follows:EQUATIONwhere 𝑁 is the number of object names that occur in the ground truth and reference explanations. Therefore, the redundant boxes in the reference explanation and the missing boxes can punish the final detection score. Moreover, we propose visual attribution metrics to evaluate the ability of models to understand key visual attributions and generate them in explanations for MEVQA, which is introduced in Section 5.5. More discussions of our adopted metrics, especially their improvements compared to previous work, are included in Supplementary Materials.Our SME dataset is composed of 901,203 training samples, 97,027 validation samples, and 30,000 test samples.Distribution of Multimodal Explanations. The main statistics of multimodal explanations in the training, validation, and test sets are shown in Figure 4 . Three split sets have similar distributions of the explanation length and the number of [𝐵𝑂𝑋 ] in explanations. The explanation lengths range from 3 words to 32 words. The majority (97.0%) of explanations consist of between 5 to 19 words, indicating the requirement of generating brief but expressive text. The numbers of [𝐵𝑂𝑋 ] in explanations range from 0 to 6, which corresponds to the number of visual objects that should be grounded for explaining. The majority (95.8%) of explanations contain at least 1 [𝐵𝑂𝑋 ], highlighting the crucial role of vision in our multimodal explanations. When comparing three split sets, the test set shows a slightly higher proportion of explanations with 2 or 3 [𝐵𝑂𝑋 ], which improves the difficulty of the test set.More information about our dataset is provided in Supplementary Materials. (2) Translate the execution process into the explanation with a few-shot explanation prompt and link the detected visual boxes to form the multimodal explanation. 
method: 
 State-of-the-art MEVQA methods [7, 34, 50] rely on large-scale training data for learning explanation, despite being built upon pretrained Vision-Language Models (VLMs). However, annotating VQA data with explanations is costly and may be impractical in various vertical domains. Meanwhile, recent research [14, 29, 40] has Explanation Translation. Though the MulProg stage provides a program-like execution process for solving the visual question, such a process is abstract, verbose, and hard to read by general users. Therefore, this stage aims to translate the execution process into a user-friendly multimodal explanation. The textual representation 𝑃 of the execution process obtained by MulProg can be comprehended by GPT-3.5. Motivated by this, as shown in Figure 6, we construct a few-shot explanation prompt, including 𝑁 (= 16) in-context examples to demonstrate the correspondence between execution processes and explanations in standard English with additional {𝐵𝑂𝑋𝑖} tokens that represent the box variables in programs. For the input question 𝑄 and its execution process 𝑃, the prompted GPT-3.5 can generate the explanation for the reasoning process. However, since MulProg aims to solve the visual question and neglects to explain the reasoning process, some key visual objects for explanation may be ignored in the generated program. To address this problem, we propose a simple yet effective rethinking mechanism to improve the generated explanation. As shown in our prompt, MEAgent allows GPT-3.5 to directly output the needed objects that may not be in the program. Next, MEAgent checks all box variables in the generated explanation and links those existing in the program to their computed values. For visual boxes ignored in the program, MEAgent extracts their object names in the explanation and utilizes the location module 𝐿𝑂𝐶 to compute their values. Finally, MEAgent can obtain the multimodal explanation with [𝐵𝑂𝑋 ] tokens to represent boxes of key visual objects. 
experiments: 
 We conduct extensive experiments on the proposed SME dataset to investigate the effectiveness of our proposed MEAgent method for FS-MEVQA. More details are included in Supplementary Materials.• The performance of traditional MEVQA methods (i.e., REX and VCIN) significantly drops when 𝑁 is small. This shows these methods cannot effectively conduct FS-MEVQA, though being built upon pretrained VLMs. These methods rely on large-scale training data, which may not be an ideal training paradigm in practice, especially considering the recent progress in open-world learning and LLM. • The detection scores of REX and VCIN are much lower than that of our MEAgent, even trained on all 901,203 training samples. This can be attributed to their adopted Faster R-CNN for preextracting 36 objects for every image, which may not contain the needed objects and lacks open-world objects. Differently, we use an open-world detector OWL-ViT to detect objects with their names mentioned in explanations. In addition to quantitative results, we demonstrate qualitative results of the predicted answers and explanations for FS-MEVQA in (2) VCIN usually predicts inconsistent answers and explanations. This is because VCIN needs large-scale data to learn the causal correlation between the answer and explanation. Differently, we utilize LLM to translate the execution process of inferring the answer to its explanation, ensuring inherent consistency between answers and explanations. ( 3) In (b)-(d), the detection accuracy of GPT-4V is also unsatisfactory. In (d), GPT-4V grounds an unimportant object (i.e., "wall") in the explanation but ignores key objects (i.e., "soap dispensers" and "bathroom"), which shows GPT-4V fails to capture the correct reasoning process for solving this question. Differently, our MEAgent explicitly generates the multimodal program for solving the input question and generates the explanation accordingly. Therefore, MEAgent can capture rational reasoning processes more effectively.We further propose metrics to evaluate the ability of models to understand key visual attributions and generate them in explanations for FS-MEVQA. We have collected attribution, which are demonstrated in Table 4 . From the results, we have the following observations: (1) Explaining plants appears to be the most challenging one, which may be due to the limited training data on subdivided plant species in current research. Therefore, the experimented LLMs and VLMs may have less knowledge about plant species. (2) Interestingly, GPT-4V demonstrates superior performance compared to VCIN across all attributions except for 𝑃𝑒𝑟𝑠𝑜𝑛, despite having lower language scores in Table 2 . This can be attributed to the extensive open-world knowledge of GPT-4V, while VLM-based VCIN suffers from insufficient knowledge in fewshot learning. This comparison also shows the complementarity of visual attribution metrics in evaluating visual knowledge for explanation. (3) GPT-4V achieves an especially low score for 𝑃𝑒𝑟𝑠𝑜𝑛, which indicates GPT-4V may be too cautious in identifying the biological sexes, ages, and occupations of people. (4) Our MEAgent significantly outperforms both baselines, showing its superior ability to understand visual attributions and explain them. Though GPT-3.5 can only understand and generate text, the GPT-3.5-based MEAgent can leverage open-world visual tools (e.g., object detector and image croppers) to perform multimodal explanation, even outperforming the stronger multimodal LLM GPT-4V.Given the limited knowledge within training samples in FS-MEVQA, a key to performing FS-MEVQA is leveraging open-world knowledge. Therefore, we further gather Out-Of-Distribution (OOD) visual questions that involve concepts not present in our SME dataset to test our MEAgent method. In Figure 8 , we show the results for four OOD samples belonging to healthcare, industry, science, and office domains. We are surprised to find that MEAgent can predict rational answers and generate good explanations for these questions. In (a), since we implement a 𝐶𝑂𝑈 𝑁𝑇 module to count the number of detected boxes, MEAgent can even explain the number of injured fingers. In (d), while MEAgent can accurately locate the title and the chart, its answer is not very accurate ("upper left" may be a better answer). These results may suggest a boarder application range of our MEAgent in various domains. By employing an LLM agent with open-world tools for visual reasoning and multimodal explanation, MEAgent is capable of overcoming the limited knowledge in training data and generalizing to encompass open-world questions. Moreover, we argue that utilizing domain-specific programming modules and examples can further improve the capability of MEAgent in a particular domain.  
: 
 and our SME dataset. In REX, #𝑖 denotes boxes pre-extracted by Faster R-CNN, belonging to only 81 classes. We use [𝐵𝑂𝑋 ] to represent the detection boxes needed in the explanation and annotate these boxes with their corresponding names mentioned in the explanation, based on the scene graph annotated by humans. Our explanation is in standard English with additional [𝐵𝑂𝑋 ]. 
 
 Appendix:  


Decision: This paper was reviewed by six experts, with initial ratings of borderline reject from three reviewers and borderline accept from the other three. 

After the rebuttal, two reviewers increased their scores, and three maintained their original ratings. This resulted in one weak accept, two borderline accepts, and two borderline rejects (one reviewer did not finalize their rating). 

Specifically, one reviewer increased their rating from borderline accept to weak accept, and another increased their score from borderline reject to borderline accept.

Given the diverse ratings for this paper, the AC carefully reviewed the paper, the reviews, and the rebuttal.

The AC identified several strengths in this paper: 

(i) The new dataset is large-scale with a wide range of types, potentially contributing to multimodal explanations.

(ii) The proposed approach requires only a small subset of samples, and the newly introduced visual evaluation metric could help foster detection tasks.

(iii) The comprehensive analysis, covering visual detection, visual attribution, and language quality, demonstrates the method's effectiveness compared to existing works.

However, the AC also identified the following weaknesses:

(i) The ablation study could be improved.

(ii) The assessment of the generalization ability could be enhanced.

(iii) The motivation behind this work could be clearer. 

At least two reviewers (Reviewer 7nw3 and Reviewer gvXZ) pointed out "the necessity of the proposed task".

The rebuttal addressed some concerns, but the reviewers also highlighted their remaining issues in their final justifications.

Reviewers v3JF, gvXZ, and 7nw3 outlined concerns that the AC believes would strengthen the paper. Please consider their comments in a detailed revision.

After careful consideration, the AC recommends acceptance, subject to a careful handling of the reviewers' unaddressed concerns. The AC trusts that the authors can provide a satisfactory revision.

Reviews: review: paper_summary: paper_summary: This paper introduces large-scale Standard Multimodal Explanation dataset for multimodal explanation for Visual Question Answering (MEVQA). 

Compared to other datasets, the proposed dataset not only exceeds others in the number of visual objects but also stands out as the first to both standardize explanations using formal English, and enrich them with additional visual grounding tokens, for better multimodal explanations.

Besides, a training-free method called MEAgent is proposed with the use of both LLM agent and multimodal open-world tools.

Moreover, several metrics are considered to estimate the detection performance of MEAgent.

Under the use of SME dataset, the overall performance of MEAgent exceeds other methods on few-shot multimodal Explanation.
strength_weakness: strength_weakness: + The proposed dataset contains not only a large volume of data but also a wide variety of types, which better improves multimodal explanations across different scenarios.
+ Table 2 has made comparisons in a relatively comprehensive way to show MEAgent performing well on few shot Multimodal Explanation on the SME dataset.
limitations: limitations: Major:
- The number of datasets is not sufficient in Table 1 during the comparison of explanations for VQA.
- The efficiency is not taken into account in metrics for evaluating the performance of different methods.
- There lack testing results of MEAgent and other methods on other datasets apart from SME dataset to vertify the generalization ability of MEAgent.
-  The ablation study should be conducted more comprehensively, rather than just focusing on the rethinking mechanism and the effect of N.
-  The number of different methods is not sufficient in demonstrating accuracy of explaining key visual attributions.

Minor:
- explain more clearly how the few-shot program prompts interact with the backbone to enhance multimodal programming in Figure 5.
paper_summary: paper_summary: This paper proposed the Standard Multimodal Explanation (SME) dataset and the Multimodal Explaining Agent (MEAgent), a novel approach to enhance explainable visual question answering (VQA) through few-shot learning. 
The SME dataset include a wide range of human-annotated visual objects linked with structured language expressions, facilitating multimodal explanations. MEAgent leverages a pre-trained Large Language Model (LLM) to interpret and generate explanations for VQA tasks using only a small subset of examples from the SME dataset. This approach enables the MEAgent to deliver high quality multimodal explanations with minimal training, outperforming existing state-of-the-art methods in both accuracy and the robustness of generated explanations. The paper also includes a innovative visual evaluation metrics that can penalise missing and redundant elements in explanations, ensuring precise visual grounding and enhancing the model's performance in practical scenarios.
strength_weakness: strength_weakness: 1. The SME dataset uses human-annotated classes which provide a more comprehensive and varied set of classes for the model to learn from. It also features more structured language expressions which are user-friendly compared to earlier datasets.
2. The proposed method introduces a novel visual evaluation metric for measuring the detection score, allowing missing and redundant boxes to impact the detection score negatively.
3. The MEAgent requires only a small subset of samples from the SME dataset to achieve state-of-the-art performance, while other frameworks might need many more samples to do so.
limitations: limitations: 1. The explanation for the initialisation and generation of the few-shot program prompts seems insufficient. As this is a fundamental component of the proposed structure, a more detailed description would benefit the understanding of in-context learning.
2. In the comparisons, different backbones were tested using different numbers of samples per class, however, the complexity level or number of parameters of different backbones was not considered, which can be crucial when adapting to real-life scenarios.
3. Despite better performance in accuracy for MEAgent compared to baseline models, the processing speed of the model was not mentioned, which is crucial in terms of comparing model efficiency.
4. In the ablation study section lines 745-746, it claims that MEAgent with N=4 is able to outperform GPT-4V with N=16. However, the accuracy for MEAgent with N=4 is missing from the table, thus this conclusion lacks evidence support at this stage.
paper_summary: paper_summary: This paper proposes a standard multi -mode data set SME, which contains special labels to associate with visual information. Given the latest progress in open-world learning, the author proposes a stronger and more practical small sample learning architecture, which includes an LLM agent based on GPT-3.5. The agent transformed the execution process into a multi-mode explanation of the multi-mode reasoning process through an open tool reasoning. In addition, the paper also considers the effects of visual objects that are often overlooked in multi-model training.
strength_weakness: strength_weakness: 1. The author uses the latest external research progress as an agent to assist in conducting visual Q & A multi-modal interpretation research. This is an exciting and effective method. This saves research costs and achieves ideal results in a shorter time.
2. The SME dataset is extensive, with over a million samples, and is the first to provide joint language-vision explanations in standard English with visual grounding tokens. The new architecture dataset is very helpful for subsequent research. Also, the dataset bridges MEVQA to NLP, making it relevant for broader applications.
3. The MEAgent method is training-free and can generate multimodal explanations from only 16 training samples. It utilizes open-world tools and an LLM agent, showing significant performance improvements over state-of-the-art methods.
4. The experimental setup is robust, including language quality metrics, visual detection, and visual attribution metrics.
limitations: limitations: 1. Although the experimental results show significant improvement in reasoning ability, it is unclear whether this enhancement is solely due to the combination of strong models (LLMs ) or other factors.
2. The authors' concept of few-shot visual question-answer learning may not be entirely accurate. While the number of training samples is small, the foundational components, such as object detection models and LLM agents, have been pre-trained on extensive datasets.
3. In the Dataset section, the authors describe the reasoning process using tuples to represent reasoning steps. However, this method may ignore the complex dependencies between steps, not just two simple dependencies.
4. The paper lacks detailed case studies or examples of real-world applications where the proposed method has been implemented successfully.
5. The proposed method, it is innovative but may be complex to implement and require significant computational resources for execution, especially when integrating various multimodal tools.
6. The reliance on pre-trained models, such as GPT-3.5, introduces the risk of biases present in these models, which could affect the fairness or accuracy of the explanations generated by the system and may cause hallucination problem.
paper_summary: paper_summary: The paper focuses on advancing explainable artificial intelligence (XAI) in the context of Visual Question Answering (VQA) systems. It introduces two main contributions: a new dataset called Standard Multimodal Explanation (SME) dataset and a method named Multimodal Explaining Agent (MEAgent) for Few-Shot Multimodal Explanation for VQA (FS-MEVQA). The SME dataset consists of over a million samples containing questions, images, answers, and multimodal explanations, enabling research in both traditional MEVQA and FS-MEVQA. The MEAgent method employs a training-free approach based on a LLM agent to generate multimodal explanations and infer answers for visual questions, particularly with few training samples.
strength_weakness: strength_weakness: 1.New dataset: The paper introduces a new dataset, SME, which is substantial in scale and includes joint language-vision explanations with visual grounding tokens. The proposed dataset could be utilized in the field of interpretable VQA tasks, both for training and evaluating.

2.Comprehensive Evaluation: The paper presents comprehensive experimental results, evaluating the proposed method using various metrics including language quality, visual detection, and visual attribution. This thorough evaluation demonstrates the effectiveness and superiority of the proposed approach over existing methods.
limitations: limitations: I believe the main contribution of the paper lies in the proposed SME dataset. As for the proposed MEAgent framework, I'm concerned about its innovativeness, considering that using LLM as an agent to access open-world tools has been widely employed in previous works(for example, GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction). The implementation of few-shot training using the In-context learning method is also not novel in this work. However, considering the novelty of the task in providing explanations with bounding box annotations when answering questions, I would still give a borderline accept result. Nevertheless, I think the experimental section of this work could be supplemented. I believe comparisons with methods trained on large amounts of data are unnecessary. Instead, experiments should be expanded to include: 1) experiments on more multimodal large language models; 2) experiments on LLM-based agents and other related models to validate the effectiveness of this work.
paper_summary: paper_summary: The paper introduces a new dataset Standard Multimodal Explanation and the task of Few-Shot Multimodal Explanation for VQA. The SME dataset consists of over a million samples that include multimodal explanations. The authors propose a novel method, the Multimodal Explaining Agent , using a large language model (LLM) combined with multimodal open-world tools to generate explanations from a minimal number of training samples. The experimental results demonstrate that MEAgent significantly outperforms existing MEVQA methods and the multimodal LLM GPT-4V, particularly in few-shot learning scenarios. This work contributes to advancing explainable AI by improving the readability and usability of explanations in VQA systems.
strength_weakness: strength_weakness: 1. This paper is integral in general and able to justify itself, fulfilling what it takes to propose a new dataset and task.

2. The proposed method is effective on the proposed dataset, making the combination GPT3.5 and off-the-shelf tools more effective than GPT4V.

3. I believe the generated multimodal explanation is indeed more interpretable and fluent than previous works.
limitations: limitations: 1. I doubt the necessity in the proposal of the task. Is it really necessary to combine multimodal explanation with few-shot scenarios? It is relatively easy to find multimodal rationales even for low-resourced languages as long as there are image-text pairs, especially in the current era when MLLMs are good at generating such information.

2. In results comparison, only gpts and ancient methods like LXMERT and VisualBERT are present, which were proposed five years ago. I expect more results from recent MLLMs-based methods.

3. Using formulaic metrics like BLEU and CIDER might not bring fair evaluation. The generation styles of models vary, and results from GPT4V might be hard to hit the expected text.

(Minor) Clerical error: In line 922, bridge->brigdes
paper_summary: paper_summary: This paper proposes a large-scale VQA dataset for MEVQA tasks and proposes a training-free MEAgent framework to solve the MEVQA tasks. This method has better results on the MEVQA data set than comparison methods.
strength_weakness: strength_weakness: 1. The author proposes a novel MEVQA dataset(SME) based on GQA, whose quantity and annotation are improved compared with existing datasets.
2. Compared with other MEVQA methods, the method proposed by the author has better results in terms of accuracy and interpretability indicators.
limitations: limitations: 1. The description of GPT-4V and MEAgent in Figure 2 is inaccurate. For GPT-4V and MEAgent, should it be few-shot samples instead of training samples? This problem appears many times elsewhere in the paper (eg, line 223, table 3). However, based on the descriptions of line 186 and line 202, it can be understood that the authors meant the number of in-context examples. I suggest the author modify the description for GPT-4V and MEAgent.
2. The motivation behind this work is somewhat confusing. In my view, this work is based on visual programming. That is, based on visual programming, LLM is used to combine the detected boxes to give interpretability in standard English. But visual programming itself already has strong interpretability and clear intermediate processes. I noticed the author's explanation in line 517, that is, the code is abstract and difficult to understand for users, but the execution process of the code does not only include the code, such as the example on the VperGPT homepage (https://viper.cs.columbia.edu/), is visual programming with intermediate execution processes and results inherently more interpretable than textual language? Therefore, from this perspective, such a conversion from code to standard English is somewhat weak.
3. Will the SME data set based on the [BOX] tag limit its diversity of explanations about the question? For example, in Figure 8(c), it seems that [BOX] does not play a role in explaining the answer.