Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation

## Abstract

Abstract With the recent emergence of foundation models trained on internet-scale data and demonstrating remarkable generalization capabilities, such foundation models have become more widely adopted, leading to an expanding range of application domains. Despite this rapid proliferation, the trustworthiness of foundation models remains underexplored. Specifically, the out-of-distribution detection (OoDD) capabilities of large vision-language models (LVLMs), such as GPT-4o, which are trained on massive multi-modal data, have not been sufficiently addressed. The disparity between their demonstrated potential and practical reliability raises concerns regarding the safe and trustworthy deployment of foundation models. To address this gap, we evaluate and analyze the OoDD capabilities of various proprietary and open-source LVLMs. Our investigation contributes to a better understanding of how these foundation models represent confidence scores through their generated natural language responses. Based on our observations, we propose a self-guided prompting approach, termed Reflexive Guidance (ReGuide) , aimed at enhancing the OoDD capability of LVLMs by leveraging self-generated image-adaptive concept suggestions. Experimental results demonstrate that our ReGuide enhances the performance of current LVLMs in both image classification and OoDD tasks.

## 1 Introduction

Thanks to substantial advancements in hardware and the availability of large-scale datasets far exceeding conventional ones, foundation models have achieved remarkable performance across a wide range of tasks, demonstrating exceptional generalization. This evolution has shifted toward leveraging multiple data modalities, particularly vision and natural language. As a result, vision-language foundation models have demonstrated their capabilities across diverse domains, from general tasks like text-to-image generation and vision-question answering, to specialized fields such as medical diagnosis and robotics (Esser et al., ; Li et al., ; Wake et al., ; Majumdar et al., ).

Despite the widespread adoption of these large vision-language models (LVLMs) including GPT, Claude, and Gemini (OpenAI, ; Anthropic, ; Reid et al., ), their trustworthiness and reliability have not been adequately investigated. Ensuring the robustness of deep neural networks has been a key research area to guarantee their safe application in practice. With the rapid popularization of LVLMs, practical concerns such as harmful content filtering and domain generalization have been actively studied (Zhang et al., ; Han et al., ). However, fundamental aspects, such as the quality of confidence estimates for model predictions—extensively studied in single-modal models—remain underexplored. Some works have studied OoDD in CLIP (Ming et al., ; Jiang et al., ; Cao et al., ) and Stable Diffusion (Zhu et al., ), but few have examined OoDD in LVLMs, particularly with respect to confidence scores expressed through their natural language responses. This gap between their demonstrated capabilities and real-world reliability raises concerns about ensuring the safe and dependable deployment of LVLMs.

To bridge the gap, we first evaluate and compare the OoDD capabilities of LVLMs. Due to the lack of prior experimental configurations, we develop a framework for evaluating the OoDD capabilities of LVLMs. Our focus is on detecting OoD image inputs based on the in-distribution (ID) space specified by text inputs. To this end, we design a prompt that defines the ID space and guides the LVLM to provide confidence estimates through its generated responses. Using this prompt, we evaluate the OoDD capabilities of both proprietary and open-source LVLMs and analyze their behavior in expressing confidence from various perspectives. Fig. 1(a) summarizes the comparison of the evaluated LVLMs. In general, proprietary models outperform open-source models, and open-source models show performance improvements as model size increases. However, some open-source models including LLaVa-v1.6 (Mistral-7B) (Li et al., ) and GLM-4v-9B (GLM et al., ) exhibit low OoDD performance despite achieving decent results on popular VLM benchmarks.(^1^11[https://huggingface.co/spaces/opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard))
We found that one of the reasons for this discrepancy is the insufficient image interpretation capabilities of these models, which limits their ability to accurately distinguish between ID and OoD inputs.

To enhance the OoD detectability of LVLMs, we propose a two-stage self-guided prompting approach called Reflexive Guidance (ReGuide). In the first stage, ReGuide prompts an LVLM to suggest two groups of concepts based on the given image: semantically similar concepts (i.e., near-OoD) and semantically dissimilar concepts (i.e., far-OoD). In the second stage, these suggested concepts are employed as auxiliary OoD classes. To the best of our knowledge, this is the first study to leverage image inputs to generate informative texts for OoDD. By utilizing the visual interpretation capabilities of LVLMs, ReGuide remains simple and model-agnostic, allowing the same prompt to be applied to different LVLMs.
Fig. 1(b) shows the effectiveness of ReGuide.
Notably, ReGuide significantly boosts the overall performance of open-source models, making them comparable to proprietary models. GPT-4o, which is the top performer prior to applying ReGuide, also benefits from ReGuide, especially on near-OoD datasets (e.g., NINCO, SSB Hard). Our results highlight the effectiveness of guiding LVLMs through self-generated, image-adaptive concept suggestions for OoDD.

From the results of our study, we can draw the following insights regarding the effectiveness of ReGuide: Despite the strong visual interpretation capabilities of LVLMs, which enable them to predict fine-grained classes of objects effectively, these models tend to avoid generating responses that fall outside the given prompt categories. Our findings suggest that the models may have developed a form of positive bias due to their training on positive image-text pairs, or that their ability to explore areas beyond the information embedded in the prompt has been diminished, possibly due to human alignment processes. Addressing these limitations can lead to more reliable and versatile applications of LVLMs in various domains.

Figure: (a) Comparison of LVLMs
Refer to caption: x1.png

## 2 Related work

In single-modal vision models, OoDD has been actively studied. Starting with the baseline method of using the maximum softmax value as an OoD score (Hendrycks & Gimpel, ), methods have evolved to improve confidence modeling (Moon et al., ; Liu et al., ; Bibas et al., ), post-hoc techniques (Liang et al., ; Lee et al., ; Sun et al., ; Djurisic et al., ; Sun et al., ; Zhang et al., ; Liu & Qin, ), or a combination of both (Xu et al., ). Another approach involves leveraging auxiliary OoD samples to better distinguish between ID and OoD inputs. While real OoD samples show strong detection performance (Hendrycks et al., ; Chen et al., ; Lu et al., ; Katz-Samuels et al., ; Koo et al., ), they require access to real OoD data. Synthetic samples offer an alternative, providing the benefits of auxiliary OoD data without the need for collecting real samples (Lee et al., ; Du et al., ; Tao et al., ; Zheng et al., ).

With the growing interest in multi-modal models, particularly VLMs, OoDD methods have advanced toward leveraging both vision and language modalities. A pioneering LVLM (non-generative) is CLIP (Radford et al., ). Ming et al. () established a baseline for OoDD on CLIP by using the maximum cosine similarity between image and text features as an OoD score. One main approach in CLIP-based OoDD focuses on using auxiliary text for OoD concepts, as text data is relatively easy to collect. Wang et al. () simply added ‘no’ as a prefix to ID classes, while Fort et al. () used labels from OoD datasets, which requires prior knowledge of OoD inputs. Jiang et al. (); Esmaeilpour et al. (); Cao et al. () derived auxiliary texts using only ID data, with Cao et al. () obtaining OoD texts from large language models (LLMs) like GPT. Previous work such as NegLabel (Jiang et al., ) and EOE (Cao et al., ), as well as our proposed method, share the idea of using negative concepts for OoDD. However, we focus on generative LVLMs and our approach is the first to utilize image-adaptive negative concepts generated by LVLMs.

Diffusion models, a type of generative model, have also been employed for OoDD. One approach is to compare the reconstruction quality between ID and OoD images (Gao et al., ; Tong & Dai, ), or to generate synthetic images for OoDD in single-modal vision models (Du et al., ; Girella et al., ). However, generative VLMs, particularly foundation models (i.e., LVLMs), remain understudied in the context of OoDD. While several works have explored confidence estimates in model predictions (Han et al., ; Groot & Valdenegro-Toro, ), they do not focus on detecting OoD inputs. Miyai et al. () introduced Unsolvable Problem Detection to evaluate VLMs’ ability to reject unanswerable inputs. However, their focus is on rejection ability, while our study focuses on distinguishing between answerable and unanswerable inputs and proposes a method to enhance this ability. In LLMs, several works use model-generated answers to guide final responses more accurately (Yao et al., ; Wei et al., ; Shanahan et al., ). Our work is the first to propose a similar approach in LVLMs, leveraging the model’s own generated answers.

## 3 OoD Detection on Vision-Language Foundation Models

### 3.1 Problem Definition

OoD is conventionally defined as distributions outside the training distribution. However, given the vast amount and broad domain coverage of data used to train LVLMs, this conventional definition faces challenges in its direct application to LVLMs. To address this, we extend the zero-shot OoDD framework of CLIP (Radford et al., ) to generative LVLMs.

Let $\mathcal{X}$ be the image space and $\mathcal{Y}=\{y_{i}\}_{i=1}^{C}$ the set of class-representing words, where $C$ is the number of classes. The OoDD problem for CLIP in zero-shot image recognition is defined as the scenario where $\mathcal{Y}$ does not contain the ground-truth label of an input image $\mathbf{x}\in\mathcal{X}$. Given $\mathbf{x}$, CLIP yields a prediction for $\mathbf{x}$ based on $sim(f_{I}(\mathbf{x}),f_{T}(\texttt{prompt}({y_{i}})))$ where $sim(u,v)$ is the cosine similarity, prompt is a text template designed to reflect $y_{i}$ (e.g., a photo of $y_{i}$), and $f_{I}$, $f_{T}$ are the image and text encoders, respectively. The cosine similarities between $\mathbf{x}$ and each $y_{i}\in\mathcal{Y}$ are used to determine whether $\mathbf{x}$ belongs to ID or OoD. If the ground-truth label of $\mathbf{x}$ is not in $\mathcal{Y}$, those similarities for all $y_{i}$ in $\mathcal{Y}$ will be relatively low, leading to the classification of $\mathbf{x}$ as OoD.

We frame the OoDD problem for LVLMs based on the zero-shot OoDD scenario defined for CLIP. Similarly, $\mathcal{Y}$ is set to a fixed word set containing only ID class words. Given both $\mathbf{x}$ and $\mathcal{Y}$ as inputs, an LVLM is instructed to produce prediction results in a structured format. Then, class predictions and confidence estimates for $\mathbf{x}$ are extracted from the natural language responses by the LVLM.
If the ground-truth label of $\mathbf{x}$ is not in $\mathcal{Y}$, the LVLM should provide low confidence scores for all $y_{i}$ in $\mathcal{Y}$ in its answers.

Figure: Figure 2: Framework for OoDD evaluation on LVLMs
Refer to caption: x3.png

### 3.2 Prompt Design

Fig. 2 illustrates the framework for OoDD evaluation on LVLMs with a simplified prompt. Our prompt consists of four components: a task description, an explanation of the rejection class, guidelines, and examples for the response format.
Unlike previous work on LVLMs (Han et al., ; Hwang et al., ; Groot & Valdenegro-Toro, ), we encountered a significant number of failure cases when using a simple prompt consisting of the task statement including $\mathcal{Y}$ and a formatted output structure. We attribute this inconsistency to differences in how confidence scores are assigned in our framework. In our prompt design, the LVLM is expected to provide confidence scores for each class in $\mathcal{Y}$, whereas prior works require only a single confidence score for the predicted class. To mitigate these failures, we enhance the prompt by adding the following components, resulting in the final prompt used in our experiments. The complete prompt can be found in Appendix B.6.

Rejection class.
When we provide only $\mathcal{Y}$, predictions for OoD samples often do not correspond to any class in $\mathcal{Y}$. Due to its strong zero-shot visual recognition capabilities, the LVLM can either provide the ground-truth label for an OoD input image or reject classification into any of the classes in $\mathcal{Y}$. To address this unintended behavior, we introduce a rejection class named none of these classes and provide a detailed explanation of its role to the LVLM.

Guidelines addressing failure cases.
In addition to the case where the prediction does not fall within $\mathcal{Y}$, we identified several other issues, such as mismatches between the class assigned the highest confidence score and the predicted class, assigning a confidence score of 0.0 to all classes including the rejection class, or failing to provide confidence scores for all classes in $\mathcal{Y}$. To reduce the frequency of these failures, we include guidelines that address the most common cases.

Response examples.
In-context learning (ICL) is commonly used to improve response quality by providing models with examples that illustrate the desired format during inference, allowing them to generate outputs that better align with these examples (Brown et al., ). We leverage ICL to reduce failure caused by formatting issues. However, we observed that when only a single example is provided, the LVLM tends to mimic the example, regardless of whether the input image is ID or OoD. To mitigate this tendency, we provide examples for both ID and OoD input images.

### 3.3 OoD Score Design

Since we provide the rejection class for OoD inputs, the ideal behavior of LVLMs for confidence estimates is to assign high confidence scores to one of the classes in $\mathcal{Y}$ for ID inputs, and to the rejection class (i.e., none of these classes) for OoD inputs. Therefore, we use the maximum confidence score among the classes in $\mathcal{Y}$ (i.e., ID classes) as the OoD score. Note that we do not constrain the sum of confidence scores. We apply the softmax function to all confidence values to normalize them, including that of the rejection class. Based on this OoD score design, an input image is likely to be ID if the score is high, and likely to be OoD if the score is low.

### 3.4 Experimental Settings

Comparison models.
To compare LVLMs from diverse perspectives, we consider both proprietary and open-source models. For proprietary models, we employ three state-of-the-art (SOTA) models: GPT-4o (2024-08-06) (OpenAI, ), Gemini Pro 1.5 (Reid et al., ), and Claude 3.5 Sonnet (Anthropic, ). For open-source models, we use five models: LLaVA-v1.6-Mistral-7B (LLaVA-v1.6) (Li et al., ), GLM-4v-9B (GLM-4v) (GLM et al., ), QWEN-VL-Chat (QWEN) (Bai et al., ), InternVL2-InternLM2-Chat-26B (InternVL2-26B), and InternVL2-LLaMA3-76B (InternVL2-76B) (Chen et al., ). Additionally, we include OpenCLIP (ViT-B-32 pretrained on LAION 2B-s34b-b79k) (Cherti et al., ) as a non-generative LVLM. To further facilitate comparison between single- and multi-modal models, we also include three single-modal state-of-the-art vision OoDD models, SCALE (Xu et al., ), fDBD (Liu & Qin, ), AugMix+ASH (Djurisic et al., ; Hendrycks et al., ).

Benchmark datasets.
We evaluate the comparison models on the CIFAR10 and ImageNet200 benchmarks proposed in OpenOOD v1.5 (Zhang et al., ).
We focus on the standard OoD setting in OpenOOD v1.5, which includes two types of datasets, near- and far-OoD, categorized based on the semantic distance between ID and OoD datasets. Since LVLMs are trained on high-resolution images, our main experiments are conducted on the ImageNet200 benchmark. We also evaluate the models on the CIFAR10 benchmark to assess their scalability with respect to input image resolution. For each benchmark, we consider the set of class names from the ID dataset as $\mathcal{Y}$. Due to cost, time, and API rate limits, we use $25\%$ subsets of the benchmarks. A detailed explanation of the benchmark datasets can be found in Appendix B.1.

Evaluation metrics.
We measure the OoDD performance using two commonly used metrics in OoDD: the area under the receiver operating characteristic curve (AUROC) and the false positive rate at the 95% true positive rate (FPR@95%TPR; FPR). We also include the ratio and number of valid responses—those that adhere to the provided guidelines and example format—as part of the evaluation metrics. We consider the ability to understand given instructions to be one of the model’s key capabilities. Only valid responses are included when measuring performance.

**Table 1: Comparison results on the ImageNet200 benchmark. Full model names are provided in the footnotes. ‘Valid’ indicates the ratio of valid responses out of a total of $23,031$ image-prompt queries, with the number in brackets showing the valid response count. Bold highlights the best performance among generative LVLMs.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | IN200 | NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |
| Valid | ACC ($\uparrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| SCALE^∗∗ | - | 86.37 | 84.84 | 93.98 | - |  |  |  |
| fDBD^∗∗ | - | 86.37 | 84.27 | 93.45 | - |  |  |  |
| AugMix+ASH^∗∗ | - | 87.01 | 55.83 / 85.74 | 71.22 / 80.00 | 19.14 / 95.81 | 21.00 / 95.67 | 31.06 / 92.51 | - |
| OpenCLIP | 100.00 (23,031) | 87.41 | 62.42 / 85.31 | 71.55 / 78.36 | 42.92 / 92.49 | 47.98 / 89.62 | 47.49 / 90.68 | 61.50 / 83.54 |
| GPT-4o | 85.49 (19,690) | 89.78 | 22.30 / 92.08 | 38.94 / 81.41 | 2.06 / 97.58 | 7.45 / 95.85 | 3.78 / 97.17 | 23.50 / 88.50 |
| Claude 3.5 Sonnet | 80.39 (18,515) | 86.06 | 52.92 / 72.18 | 78.41 / 58.09 | 9.23 / 94.93 | 10.17 / 94.28 | 18.31 / 89.71 | 49.01 / 73.69 |
| Gemini Pro 1.5 | 91.92 (21,170) | 88.84 | 21.55 / 91.97 | 55.88 / 81.39 | 1.33 / 97.71 | 6.00 / 95.30 | 5.01 / 96.55 | 33.31 / 88.09 |
| LLaVA-v1.6 | 71.63 (16,496) | 2.45 | 100.00 / 50.85 | 100.00 / 48.95 | 100.00 / 50.05 | 100.00 / 59.26 | 100.00 / 49.23 | 100.00 / 50.11 |
| GLM-4v | 89.00 (20,498) | 69.48 | 100.00 / 79.23 | 100.00 / 74.35 | 100.00 / 83.01 | 100.00 / 83.45 | 100.00 / 83.11 | 100.00 / 77.86 |
| InternVL2-26B | 61.64 (14,197) | 90.39 | 82.07 / 58.55 | 94.13 / 52.52 | 36.69 / 81.24 | 28.08 / 85.54 | 50.70 / 74.23 | 74.69 / 61.74 |
| InternVL2-76B | 97.36 (22,424) | 88.30 | 100.00 / 72.27 | 100.00 / 62.39 | 100.00 / 95.57 | 100.00 / 91.62 | 100.00 / 90.12 | 100.00 / 74.14 |

### 3.5 Results

Tab. 1 presents the OoDD capabilities of the compared models on the ImageNet200 benchmark. The near- and far-OoD results for SCALE and fDBD represent the average performance across their respective categories. ‘All OoD’ refers to the performance in distinguishing all OoD inputs, including near- and far-OoD inputs, from ID inputs (i.e., ID vs. all OoD). The results of QWEN-VL-Chat are omitted due to its exceptionally low ability to follow instructions, with a valid response rate of less than $1\%$.

On both image recognition and OoDD tasks, proprietary models outperform open-source models in most cases, with reasonable valid response rates. All compared models have more difficulty in detecting near-OoD than far-OoD. The overall performance ranking of the evaluated LVLMs generally aligns with the OpenVLM leaderboard(^3^33[https://huggingface.co/spaces/opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard)), except for Gemini Pro 1.5.
In our results, Gemini Pro 1.5 shows better ID accuracy and OoDD performance than Claude 3.5 Sonnet and InternVL-76B.
Claude 3.5 Sonnet frequently generates invalid responses compared to other proprietary models and struggles to detect near-OoD, resulting in worse performance on NINCO and SSB-Hard compared to the open-source model GLM-4v in terms of AUROC, despite achieving $16.58\%$ higher ID accuracy. InternVL2-26B achieves the best ID accuracy, but has difficulty with OoDD and shows the lowest valid response rate.
While there is no clear-cut relationship between image recognition and OoDD capabilities, models with better image recognition performance generally exhibit stronger OoDD performance, consistent with the findings of Vaze et al. ().

It is worth noting that the proprietary models generally perform on par with or better than the single-modal SOTA OoDD models. In addition, GPT-4o and Gemini Pro 1.5 outperform OpenCLIP in both ID classification and OoDD, showing significantly better performance in OoDD. For a more rigorous comparison, we evaluate OpenCLIP on the images where GPT-4o generates valid responses, as GPT-4o shows a relatively lower valid response rate. On the GPT-4o valid query set, GPT-4o still yields better results on both tasks. This demonstrates its superior visual recognition capability and its ability to express confidence scores through its generated responses. Detailed results can be found in Appendix B.2.

Among the open-source models, one notable observation is their FPR of $100\%$. Our analysis indicates that this is caused by extremely biased confidence values, which will be further discussed in Sec. 3.6. GLM-4v exhibits the best OoDD performance, while InternVL2-76B shows the second-best zero-shot image recognition performance among the open-source models. Both models also demonstrate a better understanding of instructions than GPT-4o and Claude 3.5 Sonnet. In overall, InternVL2-76B shows superior performance in all aspects, including valid response rate, ID accuracy, and OoDD performance. It achieves higher ID accuracy and comparable OoDD performance to the single-modal SOTA models, alongside the highest valid response rates among all compared models. LLaVA-v1.6 struggles with image recognition, achieving an accuracy of just $2.25\%$. Most responses from LLaVA-v1.6 incorrectly identify ID images as OoD, resulting in poor classification accuracy and OoDD performance.

Figure: Figure 3: Further analysis on the ImageNet200 benchmark. (a) FPR across different TPR thresholds, (b) OoD score distribution, (c) OoD detectability based on model sizes, and (d) ID classification accuracy and OoDD performance of InternVL2-26B according to the given class order
Refer to caption: x4.png

### 3.6 Further Analysis

Scalability with image resolution.
We assess the input resolution scalability of LVLMs using the CIFAR10 benchmark.
Most models demonstrate higher performance on the CIFAR10 benchmark than on the ImageNet200 benchmark, with a higher ratio of valid responses. Although the smaller number of class candidates and coarser class contribute to better performance, it indicates that the LVLMs handle lower-resolution images effectively. As with the ImageNet200 benchmark, proprietary models consistently outperform open-source models across all datasets, demonstrating superior performance even on low-resolution images.
However, in terms of valid response rate, only GPT-4o shows a decrease, excluding GLM-4v which is significantly less accurate overall. This suggests that GPT-4o has more difficulty handling low-resolution images compared to high-resolution images. Detailed results can be found in Appendix B.2.

Highly biased confidence scores.
One common observation across both benchmarks is an FPR of $100\%$.
As shown in Fig. 3, LLaVA-v1.6 consistently yields $100\%$ FPR across different TPR thresholds. This phenomenon is observed in most open-source models, but not in proprietary models. To understand the cause of this issue, we examine the distribution of OoD scores. Fig. 3 depicts the OoD score distribution of InternVL2-76B and GPT-4o on the ImageNet200 benchmark. We find that the compared models tend to produce highly biased OoD scores, with most OoD scores being either $0.0$ or $100.00$. A detailed explanation can be found in Appendix B.2.
This highlights the need for calibration of confidence scores in the responses of open-source models.

**Table 2: Reasoning behind the predictions of GPT-4o and InternVL2-26B on the ImageNet200 benchmark. Bold indicates the predicted class and its confidence score before applying softmax. Bold italic highlights notable points. Classes with a confidence score of $0.0$ are omitted.**
|  |  | Prediction |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  |  | ID | OoD |  |  |  |
| Actual | ID | GPT-4o |  | $\bullet$ Ground-truth: Bald eagle<br>$\bullet$ {bald eagle: 98.75, vulture: 1.0, none of these classes: 0.25}<br>$\bullet$ The bird in the image is a clear match to the bald eagle class, recognizable by its distinctive white head, yellow beak, and large wingspan. Hence, a high confidence is assigned to this class. |  | $\bullet$ Ground-truth: Centipede<br>$\bullet$ {centipede: 40.00, snail: 45.00, none of these classes: 80.00}<br>$\bullet$ The image appears to resemble a millipede-like structure, but none of the given 200 classes directly match this observation. Thus, the prediction is ‘none of these classes’ with a high confidence. |
| InternVL2-26B |  | $\bullet$ Ground-truth: Tree frog<br>$\bullet$ {tree frog: 99.99}<br>$\bullet$ The image shows a green frog with a yellow eye, which is characteristic of a tree frog. The frog is perched on a leaf, and its coloration and eye pattern are consistent with tree frogs. |  | $\bullet$ Ground-truth: Bucket<br>$\bullet$ {none of these classes: 100.00}<br>$\bullet$ The image shows a person in a swimsuit, bending over a bucket, which appears to be in a grassy area. The image does not clearly depict any of the 200 classes provided, so the prediction is ‘none of these classes’ with a confidence of 100.00. |  |  |
| OoD | GPT-4o |  | $\bullet$ Ground-truth: N/A (Openimage-O)<br>$\bullet$ {goldfinch: 5.67, junco: 2.93, bald eagle: 1.42, lorikeet: 94.72, toucan: 8.65, …, snail: 0.09, golden retriever: 0.14, ambulance: 0.18, space shuttle: 0.14, none of these classes: 0.13}<br>$\bullet$ The bird in the image closely resembles a lorikeet, with its vibrant green plumage and colorful facial markings. Other birds like toucans and goldfinches also show some resemblance, but the lorikeet class has the highest confidence due to the specific combination of colors and features. |  | $\bullet$ Ground-truth: N/A (Openimage-O)<br>$\bullet$ {none of these classes: 99.99}<br>$\bullet$ The image shows purple ornamental flowers (Allium), which do not match any of the given 200 classes. |  |
| InternVL2-26B |  | $\bullet$ Ground-truth: Araneus gemma (NINCO)<br>$\bullet$ {spider web: 99.99}<br>$\bullet$ The image shows a spider on a green plant, with a web visible in the background. The spider’s body and legs are clearly visible, and the web’s intricate pattern is evident. The confidence in the prediction of ‘spider web’ is high due to the clear visibility of the web and the spider’s position within it. |  | $\bullet$ Ground-truth: Caracal (NINCO)<br>$\bullet$ {none of these classes: 99.00}<br>$\bullet$ The animal in the image is a caracal, which is a mediumsized wild cat native to Africa and parts of the Middle East. It is characterized by its reddishbrown coat, long legs, and distinctive blacktufted ears. The caracal is known for its agility and is often found in grasslands and savannas. The image shows an animal that matches these characteristics, making it a “none of these classes" with high confidence. |  |  |

**Table 3: Comparison of the quality of confidence scores in ID inputs. Bold indicates the best performance excluding OpenCLIP.**
| Models | ImageNet200 | CIFAR10 |  |  |
| --- | --- | --- | --- | --- |
| ECE | AURC | ECE | AURC |  |
| OpenCLIP | 2.39 | 21.95 | 2.39 | 11.82 |
| GPT-4o | 2.42 | 18.64 | 1.66 | 15.86 |
| LLaVA-v1.6 | 2.11 | 893.55 | 5.58 | 48.03 |
| GLM-4 | 3.24 | 87.34 | 0.59 | 283.69 |
| QWEN | - | - | 76.96 | 705.77 |
| InternVL2-76B | 5.52 | 75.25 | 2.36 | 37.34 |

Reasoning.
We analyze the contribution of the model’s visual feature interpretability to its OoDD capability by examining the rationale behind its predictions. The prompt used for reasoning can be found in Appendix B.6.
Tab. 2 presents the reasoning results from GPT-4o and InternVL2-26B for four different cases on the ImageNet200 benchmark. The results indicate that high interpretability of visual features contributes to stronger OoDD capabilities. GPT-4o provides detailed descriptions of images, leading to predictions in fine-grained categories. InternVL2-26B also describes objects in a given image effectively, but not with the same level of detail as GPT-4o. A detailed explanation including the reasoning results for LLaVA-v1.6 can be found in Appendix B.3.

Confidence scores on ID.
We assess confidence scores for ID to rigorously explore the expressiveness of LVLMs in generating confidence estimates. To measure the quality of confidence scores, we employ ECE (Pakdaman Naeini et al., ) and AURC (Geifman et al., ). As shown in Tab. 3, LVLMs exhibit relatively low ECE despite their highly biased confidence scores. GPT-4o and OpenCLIP demonstrate good quality in predictive confidence for ID, showing lower ECE and AURC compared to other models. Although InternVL2-76B achieves classification accuracy comparable to GPT-4o, its ECE and AURC are significantly worse, indicating that InternVL2-76B assigns high confidence scores to misclassified inputs.

Scaling law in terms of model size.
We examine the relationship between OoDD capabilities and model size in terms of the number of parameters. Fig. 3 shows AUROC and FPR for InternVL2-4B, 26B, and 76B in distinguishing all OoD from ID on the ImageNet200 benchmark. We observe that OoD detectability improves as model size increases. This observation aligns with our results and the OpenVLM leaderboard, where larger models generally demonstrate superior performance.

Class order in the prompt.
To examine the influence of input class order in the prompt, we evaluate InternVL2-26B with different class orders. Detailed experimental settings for class order can be found in Appendix B.4.
Fig. 3 shows the ID classification accuracy and AUROC (ID vs. all OoD). “Random”, “Similar First”, and “Similar Last” refer to random ordering, placing similar classes to input images first, and placing similar classes last, respectively. “Similar First” improves both accuracy and OoDD performance, while “Random” and “Similar Last” result in a decrease compared to the baseline. Based on these results, we can infer that the model tends to prioritize inputs received earlier over those received later. We assume that this tendency arises from the structure of the data on which InternVL2-26B is trained. It is observed that in the text of the image-text pair dataset used for the InternVL2-26B training, words referring to objects in the image are predominantly positioned at the beginning of the text (Li et al., ; Wang et al., ; Shahroudy et al., ). This may have given InternVL2-26B an implicit bias regarding word order in the given prompts.

Response failure.
Despite providing guidelines to mitigate failures, we still observe failures across all models.
A common issue is the mismatch between the predicted class and the class with the highest confidence score. For most proprietary models, the inability to identify the given image is the most frequent failure case. This suggests that proprietary models are more conservative in their responses, often declining to provide answers when outcomes are uncertain. One contributing factor to this inability is their refusal to respond to harmful or inappropriate inputs.
All three proprietary models decline to answer questions about these inputs, while none of the open-source models do. Thus, this characteristic of proprietary models may contribute to their low valid response rates. Failures are not confined to any specific dataset, indicating that they stem from both datasets and the underlying mechanisms of models. Detailed results for failure cases and reject responses can be found on in Appendix B.5.

Figure: Figure 4: Framework of the proposed Reflexive Guidance for OoDD
Refer to caption: x10.png

## 4 Reflexive Guidance

We introduce a simple and model-agnostic prompting strategy, Reflexive Guidance (ReGuide), to enhance the OoD detectability of LVLMs. The LVLM’s strong generalization ability has been demonstrated through its performance across various downstream tasks. Therefore, we leverage the LVLM itself to obtain guidance for OoDD from its powerful zero-shot visual recognition capabilities.
Fig. 4 illustrates the overall framework of ReGuide, which is implemented in a two-stage process. Details on the prompts for each stage can be found in Appendix C.5.

Stage 1: Image-adaptive class suggestions.
In the first stage, the LVLM is asked to suggest $2N$ class names $\mathcal{A}_{\text{aux}}$ derived from the given image. Specifically, we request two groups of class names: 1) $N$ classes that are visually similar to the image denoted as $\mathcal{A}_{\text{aux}}^{\text{near}}$, and 2) $N$ classes that are visually dissimilar or belong to different domains denoted as $\mathcal{A}_{\text{aux}}^{\text{far}}$. In the context of ID, $\mathcal{A}_{\text{aux}}^{\text{near}}$ and $\mathcal{A}_{\text{aux}}^{\text{far}}$ can provide classes conceptually corresponding to near-OoD and far-OoD, respectively. If the input image is OoD, $\mathcal{A}_{\text{aux}}=\mathcal{A}_{\text{aux}}^{\text{near}}\cup\mathcal{A}
_{\text{aux}}^{\text{far}}$ can offer potential ground-truth label or class names closely related to the ground-truth label. Similar to the prompt used in Sec. 3.5, we include guidelines and response examples in the prompt. Additionally, we employ a simple post-processing step to refine the suggested classes, as detailed in Appendix C.1.

Stage 2: OoDD with suggested classes.
Stage 2 follows a similar procedure to the original OoDD evaluation presented in Fig. 2. The major difference is that $\mathcal{A}_{\text{aux}}$ is employed as auxiliary OoD classes. NegLabel (Jiang et al., ) and EOE (Cao et al., ) also use auxiliary OoD classes for improving OoDD performance, but they rely on texts to obtain negative concepts. In contrast, our approach utilizes the LVLM, allowing images to be utilized to obtain negative concepts for auxiliary OoD classes. More importantly, existing methods such as NegLabel and EOE utilize static negative concepts based on ID class texts, while our proposed ReGuide leverages adaptive negative concepts derived from individual input images. Given the strong zero-shot visual recognition capabilities of LVLMs, it is expected that OoD input images can be assigned higher confidence scores for $\mathcal{A}_{\text{aux}}$ than for $\mathcal{Y}$, since $\mathcal{A}_{\text{aux}}$ is derived from the input image itself. The rejection class none of these classes is retained as a fallback in case $\mathcal{A}_{\text{aux}}$ does not adequately function as auxiliary OoD classes.

OoD score design.
To evaluate the OoDD performance of ReGuide, we employ the same OoD score as in Sec. 3. Since ReGuide leverages auxiliary OoD classes $\mathcal{A}_{\text{aux}}$, we compare different OoD scores considered in Jiang et al. () and Cao et al. (). However, we observe that they yield similar outcomes.
The comparative results of the different OoD scores can be found in Appendix C.4.

### 4.1 Results

We evaluate ReGuide with GPT-4o and InternVL2-26B/-76B on a $5\%$ subset of the ImageNet200 benchmark due to computational and API costs.
Since ReGuide benefits from the strong image recognition capabilities of LVLMs, we exclude LLaVA-v1.6 and GLM-4v from the comparison. For this experiment, we set the number of negative class suggestions for each group $N$ to $20$.

**Table 4: ReGuide effects on the ImageNet200 benchmark. ‘Valid’ indicates the ratio of valid responses out of a total of $4,170$ image-prompt queries. Bold highlights the best performance among the results from each LVLM.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | IN200 | NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |
| Valid | ACC ($\uparrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| InternVL2-26B | 61.49 (2,564) | 91.23 | 82.73 / 58.31 | 94.34 / 52.51 | 38.03 / 80.66 | 28.86 / 85.25 | 47.91 / 75.72 | 73.12 / 63.11 |
| + GPT-text | 69.42 (2,895) | 89.58 | 69.44 / 62.14 | 85.64 / 53.57 | 28.00 / 84.70 | 28.68 / 83.77 | 39.39 / 78.08 | 62.45 / 65.96 |
| + ReGuide | 78.97 (3,293) | 93.73 | 22.92 / 86.60 | 16.25 / 89.98 | 1.23 / 98.19 | 3.81 / 97.15 | 2.23 / 97.67 | 10.79 / 93.01 |
| InternVL2-76B | 99.63(4,056) | 89.09 | 100.00 / 77.83 | 100.00 / 70.43 | 100.00 / 96.65 | 100.00 / 91.80 | 100.00 / 93.35 | 100.00 / 80.70 |
| +ReGuide | 95.80 (3,995) | 90.93 | 56.36 / 91.35 | 66.65 / 87.65 | 59.75 / 95.35 | 60.00 / 93.38 | 65.46 / 93.95 | 64.36 / 90.60 |
| GPT-4o | 87.58 (3,652) | 90.08 | 11.90 / 94.41 | 33.69 / 85.05 | 2.03 / 97.95 | 6.03 / 94.48 | 3.63 / 97.51 | 18.76 / 91.08 |
| + ReGuide | 79.42 (3,312) | 91.50 | 25.12 / 96.73 | 37.66 / 92.52 | 23.47 / 97.05 | 32.60 / 95.93 | 28.26 / 96.81 | 32.57 / 94.57 |

As shown in Tab. 4, ReGuide significantly improves various aspects of the LVLM’s performance. Note that the results in Tab. 4 (and in Sec. 3.5) reflect strong baseline performance achieved through carefully designed prompts aimed at maximizing performance, such as providing a task description, a rejection class, detailed guidelines, and a few examples for a structured output.

Surprisingly, ID classification accuracy improves even though $\mathcal{A}_{\text{aux}}$ includes class names that are visually similar to ID, i.e., $\mathcal{A}_{\text{aux}}^{\text{near}}$. We speculate that considering similar classes collectively assists in classifying images with otherwise ambiguous predictions, rather than hindering classification ability. Similar to fine-grained classification training, considering similar classes together may offer an opportunity to compare features between them and reason about their relevance. This analytical process enables the model to categorize hard-to-classify images more accurately.
We investigate $\mathcal{A}_{\text{aux}}$ for dog breed class images where ReGuide correctly changes predictions of GPT-4o. The $\mathcal{A}_{\text{aux}}$ for samples with changed predictions include highly fine-grained classes. For example, $\mathcal{A}_{\text{aux}}$ for a toy poodle image includes shihpoo, cockapoo, and cavapoo, all of which are mixed breeds involving poodle.
While this is not sufficient to fully support the hypothesis, the inclusion of highly relevant classes may have contributed to improved classification accuracy.

**Table 5: Comparison of the ratio of OoD inputs predicted to non-ID classes**
| Models | Baseline | +ReGuide |
| --- | --- | --- |
| InternVL2-26B | 26.88% (601 / 2,236) | 89.13% (2,651 / 2,974) |
| InternVL2-76B | 55.54% (2,039 / 3,671) | 91.07% (3,297 / 3,620) |
| GPT-4o | 83.92% (2,751 / 3,278) | 95.97% (2,885 / 3,006) |

In addition, with ReGuide, InternVL2-26B/76B demonstrate performance comparable or superior to the SOTA OoDD single-modal models listed in Tab. 1. We find that the model successfully classifies OoD inputs into one of $\mathcal{A}_{\text{aux}}$, resulting in a clearer separation between ID and OoD inputs. As shown in Tab. 5, the ratio of OoD images predicted as non-ID classes (i.e., $\mathcal{A}_{\text{aux}}$ + the rejection class for ReGuide and only the rejection class for the baseline) increases with ReGuide, notably by $62.25\%$ on InternVL2-26B. Near-OoD detection benefits the most from ReGuide across all compared models, with InternVL2-26B/-76B and GPT-4o showing average improvements of $32.88\%$, $15.37\%$ and $4.9\%$ in AUROC, respectively. We further examine how ReGuide alters the predictions of OoD images that were previously classified as one of the ID classes in InternVL2-26B due to semantic similarities. ReGuide correctly classifies these misclassified images into $\mathcal{A}_{\text{aux}}$ closely related to the ground-truth label. For example, an image of donut previously misclassified as the ID class bagel is now correctly classified as chocolate dipped donut in $\mathcal{A}_{\text{aux}}$ with ReGuide. This demonstrates the effectiveness of leveraging the image understanding capabilities of LVLMs. Detailed results of ReGuide can be found in Appendix C.2.

GPT-4o displays slightly different patterns with ReGuide compared to the InternVL2 family. ReGuide also enhances the OoDD performance of GPT-4o. However, in contrast to both InternVL2 models, GPT-4o+ReGuide shows a worse FPR than GPT-4o alone. We observe that ReGuide tends to make the model overconfident for some image inputs, producing a slightly more biased OoD score distribution compared to the model without ReGuide. Upon examining the ROC curve (see Appendix C.3) for GPT-4o+ReGuide, we find that the optimal separation between ID and OoD inputs appears at a lower TPR threshold than without ReGuide due to this overconfidence. At the 90% TPR, GPT-4o+ReGuide achieves an FPR of $4.02\%$, compared to $16.84\%$ for GPT-4o without ReGuide.
We infer that this observation is due to the powerful visual recognition ability of GPT-4o. As demonstrated in Tab. 2, GPT-4o is capable of suggesting highly relevant classes for a given image, which may lead to low confidence score assignments for ID inputs. This results in the misalignment of OoD scores between ID and OoD. In contrast, the FPR change of the InternVL2 models differs from that of GPT-4o, as InternVL2 models show lower FPR with ReGuide. The ROC curve and OoD score distribution for GPT-4o and InternVL2 models can be found in Sec. C.3.

On the other hand, the suggested classes that are very close to a given image is helpful in detecting near-OoD as discussed above. By including classes derived from near-OoD images, these images are more likely to be classified into one of the suggested classes rather than ID classes, leading to improved near-OoD detection performance. The significant improvement in near-OoD detection compared to far-OoD detection with GPT-4o+ReGuide highlights this effect.

Image-adaptive vs. text-adaptive.
We compare ReGuide with an approach based on EOE (Cao et al., ). EOE leverages $\mathcal{A}_{\text{aux}}$ suggested by LLMs, so we ask GPT-4o to provide
$\mathcal{A}_{\text{aux}}^{\text{near}}$ and $\mathcal{A}_{\text{aux}}^{\text{far}}$
referencing the ID class names. We denote this approach as GPT-text, as shown in Tab. 4. The prompt used for GPT-text can be found in Appendix C.5. The key difference between GPT-text and ReGuide is that GPT-text obtains class suggestions via ID class names in text form, whereas ReGuide utilizes visual information. As shown in Tab. 4, although GPT-text also enhances InternVL2-26B, the improvement is not as significant as that achieved by ReGuide. We infer that this difference arises from the aforementioned key distinction. When images are used, the model can provide diverse class concepts based on the context of the given image, as each image serves as a unique input. In contrast, when text is used, providing diverse concepts becomes challenging because the ID class set is static. This highlights the effectiveness of image-adaptive OoD class suggestions.

## 5 Conclusion and Limitations

In this paper, we address the lack of rigorous evaluation and comparison of the OoDD performance of LVLMs. To tackle this, we establish a framework to evaluate and compare various proprietary and open-source LVLMs. Our comparative analysis provides interesting takeaways into how LVLMs represent confidence scores through their generated natural language responses. Overall, proprietary LVLMs outperform open-source LVLMs in both image classification and OoDD tasks, demonstrating comparable or even superior OoDD performance relative to SOTA single-modal OoDD models. Additionally, open-source LVLMs tend to be overconfident in their response, highlighting the need for confidence calibration. Analyzing the rationale behind LVLM predictions reveals that their visual interpretation capabilities impact their OoDD performance. Based on our findings, we propose ReGuide, a self-guided prompting approach that enhances the OoDD capabilities of LVLMs by leveraging self-generated, image-adaptive concepts. Experimental results demonstrate that ReGuide significantly boosts the OoDD performance of both proprietary and open-source LVLMs. We hope our findings contribute to enhancing the reliability of vision-language foundation models for practical deployment.

Limitations of this study include the challenges of exerting precise control over LVLM behavior and the insufficient effectiveness of guidelines to mitigate unintended outputs. Additionally, the image-adaptive nature of ReGuide may lead to suboptimal class suggestions based on image context. A detailed discussion of these limitations can be found in Appendix C.6.

## Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00352184).

## Appendix A Implementation Details

All experiments are implemented with Python 3.9 and PyTorch 1.9, using NVIDIA A100 80GB GPUs. For the InternVL2-llama3-76B model, due to its significant computational requirements, we employ 4 A100 GPUs with the VLLM (Kwon et al., ) library to reduce time overhead. For the remaining models, a single A100 GPU is sufficient to run the experiments.

## Appendix B Experimental Details for Sec. 3

### B.1 Dataset

We adopt the CIFAR10 benchmark and the ImageNet200 benchmark of the standard OoD setting in OpenOOD v1.5. The detailed composition of each benchmark is as follows:

The CIFAR10 benchmark consists of CIFAR10 (Krizhevsky et al., ) as the ID dataset, CIFAR100 as the near-OoD dataset, and five datasets—MNIST, SVHN (Netzer et al., ), Places365 (Zhou et al., ), Textures (Cimpoi et al., ), and Tiny ImageNet (Le & Yang, )—as the far-OoD datasets.

The ImageNet200 benchmark consists of ImageNet200 (Russakovsky et al., ) as the ID dataset, two datasets—NINCO (Bitterwolf et al., ) and SSB Hard (Vaze et al., ) as the near-OoD datasets, and three datasets—iNaturalist (Van Horn et al., ), Textures, and Openimage-O (Wang et al., )—as the far-OoD datasets.

We sample 25% from each dataset while maintaining the original proportion of samples for each label across the benchmarks.

### B.2 Comprehensive Experimental Results

Tab. B.2.1 and B.2.2 provide comprehensive results for experimental results on the ImageNet200 and CIFAR10 benchmark, respectively. For the CIFAR10 benchmark, we employ only GPT-4o as the proprietary model.

CLIP on GPT-4o valid query set.
We evaluate OpenCLIP on inputs where GPT-4o generates valid responses to analyze whether GPT-4o’s superior results are influenced by input bias, such as the rejection of difficult inputs. OpenCLIP (GPT-4o valid) shows slightly better performance than the overall performance on the entire $25\%$ subset. However, despite this improvement, GPT-4o still outperforms OpenCLIP on both tasks. This demonstrates GPT-4o’s superior visual recognition capability and its ability to produce better confidence scores through its generated responses.

Scalability with image resolution.
As shown in Tab. B.2.2, GPT-4o consistently outperforms OpenCLIP in both image recognition and OoDD tasks, particularly excelling in OoD distinguishability with an AUROC of $98.26\%$ and an FPR of $2.62\%$. However, GPT-4o returns the lowest number of valid responses among all compared models. QWEN processes low-resolution images better than high-resolution ones, although its overall performance remains poor. In terms of AUROC on OoD datasets, QWEN assigns higher confidence scores to ID classes for OoD inputs compared to ID inputs. Similarly, LLaVA-v1.6 processes low-resolution images better than high-resolution images. In contrast, GLM-4v performs worse on the CIFAR10 benchmark than on the ImageNet200 benchmark, including the valid response rate. One notable observation is that InternVL2-26B outperforms InternVL2-76B overall on the CIFAR10 benchmark.

Highly biased confidence scores.
The compared models tend to produce OoD scores of either $0.0$ or $100.00$, as shown in Fig. 3. This indicates that the models typically classify input images as either ID or OoD with extremely high certainty, e.g., assigning $100.00$ to only one class and $0.00$ to all other classes. InternVL2-76B exhibits this tendency more strongly than GPT-4o. It produces almost no responses with OoD scores between $0.0$ and $100.00$ whereas GPT-4o generates some responses in this range.

**Table B.2.1: Comprehensive results on the ImageNet200 benchmark. For clarity, the full names of the compared models are provided in the footnotes below the table.^∗ $\downarrow$ and $\uparrow$ mean that lower and higher values are better, respectively. The AURC values are multiplied by $10^{3}$, and the other values are percentages. ‘Valid’ indicates the ratio of valid responses out of a total of $23,031$ image-prompt queries. The number in brackets represents the number of valid responses. Bold represents the best performance among generative LVLMs.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | ImageNet200 | NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |  |  |
| Valid | ACC ($\uparrow$) | ECE ($\downarrow$) | AURC ($\downarrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| OpenCLIP | 100.00 (23,031) | 87.41 | 2.39 | 21.95 | 62.42 / 85.31 | 71.55 / 78.36 | 42.92 / 92.49 | 47.98 / 89.62 | 47.49 / 90.68 | 61.50 / 83.54 |
| OpenCLIP (GPT-4o valid) | 85.49 (19,690) | 88.08 | 2.66 | 19.81 | 59.52 / 86.12 | 70.56 / 78.21 | 40.26 / 93.24 | 45.66 / 90.30 | 44.85 / 91.59 | 58.75 / 84.32 |
| GPT-4o | 85.49 (19,690) | 89.78 | 2.42 | 18.64 | 22.30 / 92.08 | 38.94 / 81.41 | 2.06 / 97.58 | 7.45 / 95.85 | 3.78 / 97.17 | 23.50 / 88.50 |
| Claude 3.5 Sonnet | 80.39 (18,515) | 86.06 | 7.20 | 78.10 | 52.92 / 72.18 | 78.41 / 58.09 | 9.23 / 94.93 | 10.17 / 94.28 | 18.31 / 89.71 | 49.01 / 73.69 |
| Gemini Pro 1.5 | 91.92 (21,170) | 88.84 | 2.42 | 25.83 | 21.55 / 91.97 | 55.88 / 81.39 | 1.33 / 97.71 | 6.00 / 95.30 | 5.01 / 96.55 | 33.31 / 88.09 |
| LLaVA-v1.6 | 71.63 (16,496) | 2.45 | 2.11 | 893.55 | 100.00 / 50.85 | 100.00 / 48.95 | 100.00 / 50.05 | 100.00 / 59.26 | 100.00 / 49.23 | 100.00 / 50.11 |
| GLM-4v | 89.00 (20,498) | 69.48 | 3.24 | 87.34 | 100.00 / 79.23 | 100.00 / 74.35 | 100.00 / 83.01 | 100.00 / 83.45 | 100.00 / 83.11 | 100.00 / 77.86 |
| QWEN^∗∗ | 0.46 (107) | - | - | - | - / - | - / - | - / - | - / - | - / - | - / - |
| InternVL2-4B | 70.97 (16,344) | 63.46 | 22.48 | 282.95 | 92.75 / 55.69 | 97.88 / 52.49 | 83.67 / 62.06 | 44.89 / 76.42 | 79.39 / 59.19 | 88.25 / 56.94 |
| InternVL2-26B | 61.64 (14,197) | 90.39 | 8.78 | 92.03 | 82.07 / 58.55 | 94.13 / 52.52 | 36.69 / 81.24 | 28.08 / 85.54 | 50.70 / 74.23 | 75.69 / 61.74 |
| InternVL2-76B | 97.36 (22,424) | 88.30 | 5.52 | 75.25 | 100.00 / 72.27 | 100.00 / 62.39 | 100.00 / 95.57 | 100.00 / 91.62 | 100.00 / 90.12 | 100.00 / 74.14 |

**Table B.2.2: Comprehensive results on the CIFAR10 benchmark. For clarity, the full names of the compared models are provided in the footnotes below the table.^∗ $\downarrow$ and $\uparrow$ mean that lower and higher values are better, respectively. The AURC values are multiplied by $10^{3}$, and the other values are percentages. ‘Valid’ indicates the ratio of valid responses out of a total of $40,568$ image-prompt queries. The number in brackets represents the number of valid responses. Bold represents the best performance among the compared models among generative LVLMs.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | CIFAR10 | CIFAR100 | MNIST | SVHN | Textures | Places365 | Tiny ImageNet |  |  |  |  |
| Valid ($\uparrow$) | ACC ($\uparrow$) | ECE ($\downarrow$) | AURC ($\downarrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |  |
| OpenCLIP | 100.00 (40,568) | 93.33 | 2.39 | 11.82 | 55.15 / 91.60 | 15.74 / 97.87 | 16.88 / 97.89 | 43.12 / 94.45 | 41.64 / 92.93 | 46.99 / 92.65 | 26.76 / 95.98 |
| GPT-4o | 66.42 (26,946) | 94.39 | 1.66 | 15.86 | 5.57 / 96.64 | 0.55 / 99.32 | 2.67 / 98.19 | 0.51 / 99.53 | 5.40 / 96.82 | 4.15 / 97.39 | 2.62 / 98.26 |
| LLaVA-v1.6 | 92.50 (37,525) | 83.51 | 5.58 | 48.03 | 100.00 / 84.87 | 100.00 / 95.75 | 100.00 / 94.46 | 100.00 / 94.76 | 100.00 / 92.39 | 100.00 / 87.73 | 100.00 / 93.66 |
| GLM-4v | 73.81 (40,098) | 35.96 | 0.59 | 283.69 | 100.00 / 67.77 | 100.00 / 68.34 | 100.00 / 68.31 | 100.00 / 68.30 | 100.00 / 66.21 | 100.00 / 67.87 | 100.00 / 67.79 |
| QWEN | 72.81 (29,537) | 15.90 | 76.96 | 705.77 | 100.00 / 51.91 | 81.80 / 28.91 | 99.63 / 28.02 | 78.39 / 53.73 | 96.77 / 27.95 | 96.98 / 28.21 | 92.01 / 31.01 |
| InternVL2-4B | 96.49 (39,143) | 81.84 | 13.49 | 113.77 | 70.21 / 69.71 | 42.62 / 92.75 | 39.25 / 92.94 | 17.45 / 93.94 | 44.53 / 86.11 | 58.87 / 78.75 | 43.83 / 89.43 |
| InternVL2-26B | 99.80 (40,487) | 96.22 | 1.87 | 24.47 | 22.65 / 87.72 | 0.27 / 98.91 | 0.00 / 99.04 | 1.35 / 98.37 | 12.01 / 93.03 | 10.95 / 93.61 | 4.77 / 96.66 |
| InternVL2-76B | 99.95 (40,549) | 90.93 | 2.36 | 37.34 | 100.00 / 85.91 | 100.00 / 95.99 | 100.00 / 96.64 | 100.00 / 95.62 | 100.00 / 92.02 | 100.00 / 90.34 | 100.00 / 94.29 |

### B.3 Detailed Results for Reasoning in Sec. 3.6

In Sec. 3.6, we provide the reasoning results of GPT-4o and InternVL2-26B on the ImageNet200 benchmark. As shown in Tab. 2, GPT-4o demonstrates strong visual recognition abilities. It provides detailed descriptions of images, including aspects such as color, object shape, and relationships with other classes, leading to predictions in fine-grained categories. For example, GPT-4o correctly identifies the bald eagle image by describing its distinctive features, such as the white head, yellow beak, and large wingspan. For the image classified as lorikeet, GPT-4o provides highly detailed descriptions of the object’s visual features, including its vibrant green plumage, and arrives at its conclusion by comparing these features with other possible classes among the provided options, such as toucans or goldfinches. Due to its impressive visual recognition ability, GPT-4o’s misclassified predictions often involve classes closely related to the ID class, such as centipede and millipede.

In contrast, the reasoning provided by InternVL2-26B is less detailed than that of GPT-4o. Its descriptions are much more general, and at times, its explanations are based on common knowledge about the object in the image. For example, for an image of araneus gemma, InternVL2-26B describes it as a spider on a green plant with a web. Similarly, for an image of a caracal, the explanation provides mainly general information, such as the species’ native land or habitat. The analysis results indicate that a high interpretability of visual features contributes to stronger OoDD capabilities.

We additionally present the reasoning results of LLaVA-v1.6 on the same benchmark to analyze its low performance, despite reporting valid response rates comparable to other models. As shown in Tab. B.3.1, LLaVA-v1.6 shows a less detailed image interpretation compared to GPT-4o. It primarily predicts images into coarse-level categories, such as birds or flowers, and its explanations rely more on common sense than on visual features. This results in incorrect predictions, as evidenced in the cases where actual ID $\rightarrow$ prediction OoD. It suggests that better interpretability of visual features is crucial for enhancing OoDD capabilities.

**Table B.3.1: Reasoning behind predictions of LLaVA-v1.6 on the ImageNet200 benchmark, excluding the case where the actual input is OoD but the prediction is ID. Due to the low valid response rate, none of LLaVA-v1.6’s responses corresponds to this case. Bold highlights the predicted class and its confidence score before applying softmax. Bold italic highlights notable points. Classes with a confidence score of $0.0$ are omitted.**
|  |  | Prediction |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | ID | OoD |  |  |
| Actual | ID |  | $\bullet$ Ground-truth: Ostrich<br>$\bullet$ {ostrich: 99.99, none of these classes: 0.00}<br>$\bullet$ The image provided is an ostrich, which is a large flightless bird native to Africa. It is the largest living bird and is known for its long neck and legs, as well as its distinctive appearance. The ostrich is not a fish, a reptile, a mammal, a bird, or an insect, so it does not belong to any of the other classes provided. Therefore, the prediction is ’ostrich’ with a confidence of 100.00. |  | $\bullet$ Ground-truth: Junco<br>$\bullet$ {none of these classes: 100.00}<br>$\bullet$ The image provided is a bird, which does not belong to any of the given 200 classes. Therefore, the prediction is ’none of these classes’ with a confidence of 100.00. |
| OoD | N/A |  | $\bullet$ Ground-truth: N/A (Openimage-O)<br>$\bullet$ {none of these classes: 99.99, all the other classes: 0.01}<br>$\bullet$ The image provided is a flower, which does not belong to any of the given 200 classes. Therefore, the prediction is ’none of these classes’ with a confidence of 100.00. |  |  |

### B.4 Implementation Details for Class Order

To examine the influence of class order in the prompt, we evaluate InternVL2-26B with three different strategies: 1) Random, 2) Similar First, and 3) Similar Last. For Random, we randomly shuffle classes for every query. For Similar First and Similar Last, we group $200$ classes into broad categories based on their conceptual similarity (e.g., aquatic animals, birds, domestic dogs, and vehicles). We request GPT-4o to group the given classes based on their conceptual similarity. Tab. B.4.1 presents the class grouping results by GPT-4o, which are used for class order experiments in Sec. 3.6. The prompt used for grouping classes can be found in Fig. B.6.3.

By leveraging class groups, Similar First places the group containing the ground truth label of the input image at the beginning, while Similar Last places that group at the end. For OoD input images, we randomly shuffle the class order, since ground-truth label information is not available for OoD inputs. We evaluate these class order strategies on InternVL2-26B using a $5\%$ subset of the ImageNet200 benchmark, sampled in the same manner as the $25\%$ subset.

**Table B.4.1: Grouping results on the ImageNet200 benchmark suggested by GPT-4o**
| Group | Classes |
| --- | --- |
| Aquatic Animals | goldfish, great white shark, hammerhead, stingray, eel, clown fish, puffer fish, grey whale, killer whale, sea lion, jellyfish, starfish, lobster, hermit crab, newt, axolotl, tree frog, snail |
| Birds | bald eagle, vulture, flamingo, american egret, pelican, king penguin, duck, goose, black swan, hen, ostrich, peacock, lorikeet, hummingbird, toucan, goldfinch, junco |
| Reptiles & Amphibians | iguana, African chameleon, cobra, scorpion, tarantula, centipede |
| Insects | ladybug, fly, bee, ant, grasshopper, cockroach, mantis, dragonfly, monarch butterfly |
| Domestic Dogs | chihuahua, shih tzu, yorkshire terrier, boston terrier, scottish terrier, west highland white terrier, pug, pomeranian, toy poodle, beagle, basset hound, cocker spaniels, french bulldog, pembroke welsh corgi, afghan hound, bloodhound, weimaraner, golden retriever, labrador retriever, collie, border collie, rottweiler, german shepherd dog, boxer, saint bernard, husky, dalmatian, chow chow, standard poodle, italian greyhound, whippet |
| Wild Mammals | timber wolf, hyena, red fox, tabby cat, leopard, snow leopard, lion, tiger, cheetah, wood rabbit, porcupine, fox squirrel, beaver, guinea pig, zebra, pig, hippopotamus, bison, gazelle, llama, skunk, badger, polar bear, orangutan, gorilla, chimpanzee, gibbon, baboon, meerkat, panda, koala |
| Vehicles & Transportation | ambulance, jeep, school bus, pickup truck, tractor, steam locomotive, fire engine, canoe, pirate ship, schooner, submarine, space shuttle, military aircraft, parachute |
| Weapons & Military | assault rifle, cannon, missile, revolver, tank |
| Sports Equipment | basketball, rugby ball, soccer ball, tennis ball, scuba diver, baseball player |
| Musical Instruments | accordion, electric guitar, flute, grand piano, harmonica, harp, saxophone, trombone, violin |
| Food & Drinks | ice cream, bagel, pretzel, cheeseburger, hotdog, pizza, burrito, espresso, cabbage, broccoli, cucumber, bell pepper, mushroom, Granny Smith, strawberry, lemon, pineapple, banana, pomegranate, acorn |
| Household | backpack, wheelbarrow, bathtub, beer glass, binoculars, birdhouse, bow tie, broom, bucket, cauldron, candle, mobile phone, cowboy hat, gasmask, joystick, lab coat, lawn mower, lipstick, mailbox, mitten, sandal, shield, spider web, scarf, vase, wine bottle |
| Landmarks & Structures | barn, lighthouse, carousel, castle, volcano |
| Tools & Machines | hammer, hatchet, guillotine |

### B.5 Detailed Results for Failure Cases

The number of each failure case.
Tab. B.5.1 summarises the frequency of each failure case. Common failure cases can be categorized into six groups: 1) a mismatch between the predicted class and the class with the maximum confidence score, 2) failure to provide confidence scores for all classes, 3) responses that are not structured as the given format, 4) responses indicating an inability to identify the given image, 5) inclusion of classes not listed among the given, and 6) class duplication. For Case 2, we post-process the responses by assigning a confidence score of $0.0$ to the missing classes, as $200$ classes can be considered a substantial number to process effectively. Each failure case occurs uniformly across all datasets.

**Table B.5.1: The number of each failure case on each model with the ImageNet200 benchmark**
| Models | (1) | (2) | (3) | (4) | (5) | (6) |
| --- | --- | --- | --- | --- | --- | --- |
| GPT-4o | 105 | 1 | 2 | 3,225 | 10 | 0 |
| Claude 3.5 Sonnet | 28 | 1 | 0 | 2,550 | 1,940 | 0 |
| Gemini Pro 1.5 | 1558 | 0 | 0 | 208 | 71 | 22 |
| LLaVA-v1.6 | 6,533 | 2 | 0 | 0 | 0 | 0 |
| GLM-4v | 775 | 0 | 0 | 1,419 | 339 | 0 |
| QWEN | 180 | 2,937 | 13,160 | 0 | 7 | 5,857 |
| InternVL2-76B | 212 | 0 | 0 | 0 | 353 | 42 |

Example responses for each failure case.
Tab. B.5.2 provides two examples for each failure case. Case 2 is omitted as it is self-explanatory. Bold text indicates the elements that contribute to the response being classified as a failure case.

**Table B.5.2: Example responses for each failure case**
| Failure case | Example |
| --- | --- |
| 1) A mismatch between the predicted class and the class with the maximum confidence score | •<br>Prediction: ‘great white shark’ Confidence: $\{$‘goldfish’: 0.0, ‘great white shark’: 0.0, ‘hammerhead’: 100.0, $\cdots$, ‘ostrich’: 12.1, ‘none of these classes’: 0.0$\}$<br>•<br>Prediction: ‘goldfinch’ Confidence: $\{$‘goldfish’: 0.0, ‘great white shark’: 0.0, ‘junco’: 99.98, $\cdots$, ‘ostrich’: 12.1, ‘none of these classes’: 0.0$\}$ |
| 3) Responses that are not structured according to the given format | •<br>Example: goldfish<br>•<br>Prediction: $\{$goldfish, great white shark, hammerhead, stingray, hen, ostrich, goldfinch, junco, bald eagle, vulture, newt, axolotl, tree frog, iguana, African chameleon, centipede, sea lion, none of these classes$\}$ |
| 4) Responses indicating an inability to identify the given image | •<br>I’m sorry, I can’t identify specific elements in the image beyond what you’ve provided in the description.<br>•<br>I apologize, but I do not feel comfortable classifying or describing this image in detail. |
| 5) Inclusion of classes not listed among the given | •<br>Prediction: basketball player<br>•<br>Prediction: birthday |
| 6) Class duplication | •<br>Confidence: $\{$‘goldfish’: 0.0, ‘great white shark’: 0.0, $\cdots$, ‘ostrich’: 99.9, $\cdots$, ‘ostrich’: 12.1, ‘none of these classes’: 0.0$\}$<br>•<br>Confidence: $\{$‘goldfish’: 0.0, ‘great white shark’: 0.0, $\cdots$, ‘boxer’: 9.5, $\cdots$, ‘boxer’: 10.0, ‘none of these classes’: 0.0$\}$ |

Examples for inappropriate content.
Tab. B.5.3 shows examples of images and their responses from each proprietary model. All inappropriate or harmful images we found are from SSB Hard. As discussed in Sec. 3.6, All three proprietary models decline to follow a given prompt if an input image contains such content, instead they return responses with “I’m sorry, I can’t help with that” or “I apologize, but I do not feel comfortable classifying or describing this image in detail”. Claude 3.5 Sonnet rarely generates responses corresponding to Case 4, but its frequency increases for the inappropriate classes in SSB Hard, such as undies or uzi. However, all open-source models generate predictions for them.

**Table B.5.3: Examples of inappropriate images and the corresponding refusal responses from proprietary models**
| Image | Model | Response |
| --- | --- | --- |
|  | GPT-4o | I’m sorry, I can’t help with that. |
| Claude 3.5 Sonnet | I apologize, but I don’t actually see an image attached to analyze in this conversation. |  |
| undies | Gemini 1.5 Pro | I cannot assist with this request as it is sexually explicit in nature. |
|  | GPT-4o | I’m sorry, I can’t help with identifying or analyzing this image. |
| uzi | Claude 3.5 Sonnet | I apologize, but I do not feel comfortable classifying or describing this image in detail. |

### B.6 Prompt Format

We provide the full prompt formats used for experiments in Sec. 3.5 and 3.6. In the prompt format, yellow, green, red, and blue boxes indicate a task description, an explanation of the reject class, guidelines, and examples for response format, respectively.

#### B.6.1 Prompt for Baseline in Sec. 3.5

Fig. B.6.1 illustrates the baseline prompt used for experiments in Sec. 3.5. First, we define and explain the task that the LVLM will serve. We provide all $\mathcal{Y}$ in the prompt format and ask the LVLM to answer the prediction and confidence scores of each $y_{i}$ for $\mathbf{x}$. Note that the sum of the confidence scores is not limited to a constant value since we empirically observe that the removal of the limit leads to enhanced outputs including much proper confidence score representations and improved prediction, particularly for OoD inputs. The red text indicates that the contents vary depending on the choice of benchmark.

Figure: Figure B.6.1: Prompt used for experiments in Sec. 3.5.
Refer to caption: x15.png

#### B.6.2 Prompt for Reasoning

Fig. B.6.2 illustrates the prompt used for reasoning in Sec. 3.6. The red text indicates that the contents vary depending on the choice of benchmark. The text highlighted in yellow corresponds to the parts added for reasoning in the baseline prompt.

Figure: Figure B.6.2: Prompt used for reasoning in Sec. 3.6.
Refer to caption: x16.png

#### B.6.3 Prompt for Class order

Fig. B.6.3 illustrates the prompt used for grouping classes in the ImageNet200 dataset based on their conceptual similarity.

Figure: Figure B.6.3: Prompt used for GPT-4o to group classes for class order in Sec. 3.6.
Refer to caption: x17.png

## Appendix C Experimental Details for Reflexive Guide

### C.1 Suggested Class Post-processing

Despite the guidelines provided in Stage 1, responses that deviate from the guidelines are observed. These failure cases can be broadly categorized into four cases: 1) inclusion of ID class names, 2) failure to suggest exactly $N$ classes for each near- and far-OoD group, 3) duplicated classes, and 4) no suggestion due to failure in recognizing the image. We post-process the suggested answers to refine them into a feasible set of auxiliary OoD classes, except for Case 4. For Case 1, we exclude suggestions that are character-level identical to ID classes. For Case 2, if the total number of suggestions exceeds $2N$, we sample only $2N$ classes from the suggestions. If the total number of suggestions is less than $2N$, we use the suggestions as is without any supplementation. For Case 3, we remove one of the duplicated classes.

### C.2 Detailed Results for Sec. 4.1

Tab. C.2.1 presents the comprehensive results for ReGuide.

**Table C.2.1: ReGuide results on $5\%$ subset of the ImageNet200 benchmark. The AURC values are multiplied by $10^{3}$, and the other values are percentages. ‘Valid’ indicates the ratio of valid responses out of a total of $4,170$ image-prompt queries. Bold denotes the best performance among the results from each model.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | ImageNet200 | NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |  |  |
| Valid | ACC ($\uparrow$) | ECE ($\downarrow$) | AURC ($\downarrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| InternVL2-26B | 61.49 (2,564) | 91.23 | 8.12 | 92.27 | 82.73 / 58.31 | 94.34 / 52.51 | 38.03 / 80.66 | 28.86 / 85.25 | 47.91 / 75.72 | 73.12 / 63.11 |
| + GPT-text | 69.42 (2,895) | 89.58 | 11.84 | 83.87 | 69.44 / 62.14 | 85.64 / 53.57 | 28.00 / 84.70 | 28.68 / 83.77 | 39.39 / 78.08 | 62.45 / 65.96 |
| + ReGuide | 78.97 (3,293) | 93.73 | 12.42 | 59.23 | 22.92 / 86.60 | 16.25 / 89.98 | 1.23 / 98.19 | 3.81 / 97.15 | 2.23 / 97.67 | 10.79 / 93.01 |
| InternVL2-76B | 99.63 (4,056) | 89.09 | 5.93 | 48.20 | 100.00 / 77.83 | 100.00 / 70.43 | 100.00 / 96.65 | 100.00 / 91.80 | 100.00 / 93.35 | 100.00 / 80.70 |
| +ReGuide | 95.80 (3,995) | 90.93 | 1.54 | 9.28 | 56.36 / 91.35 | 66.65 / 87.65 | 59.75 / 95.35 | 60.00 / 93.38 | 65.46 / 93.95 | 64.36 / 90.60 |
| GPT-4o | 87.58 (3,652) | 90.08 | 3.30 | 7.94 | 11.90 / 94.41 | 33.69 / 85.05 | 2.03 / 97.95 | 6.03 / 94.48 | 3.63 / 97.51 | 18.76 / 91.08 |
| + ReGuide | 79.42 (3,312) | 91.50 | 1.55 | 8.66 | 25.12 / 96.73 | 37.66 / 92.52 | 23.47 / 97.05 | 32.60 / 95.93 | 28.26 / 96.81 | 32.57 / 94.57 |

Tab. C.2.2 and C.2.3 present example responses of each models in Tab. 4. The first row in Tab. C.2.2 indicates the classes suggested by GPT-4o when ID class names are provided (i.e., GPT-text). The prompt used to obtain class suggestions of GPT-text can be found in Sec. C.5. A class name below each image is the ground truth of the corresponding image. The class suggestion set in GPT-text remains fixed regardless of the input images, as ID classes are static information, whereas ReGuide’s suggestions change based on the input image due to its image-adaptive nature.

The results on InternVL2-26B/-76B and GPT-4o demonstrate the effectiveness of ReGuide. Firstly, ReGuide suggests helpful classes for OoDD. For example, as shown in Tab. C.2.2, InternVL2+ReGuide suggests near-OoD classes for dotted image, such as polka dot, cherry red, and salmon-colored, which are highly close to its ground-truth. These suggested classes successfully guide the model to classify OoD inputs into one of the auxiliary OoD classes. For the softball image, the prediction of ReGuide is also closest to the ground-truth.

To ensure a fairer comparison between the models, we re-evaluate their performance based on the inputs that produce valid responses across all three models utilizing ReGuide. As shown in Table C.2.4, the effect of ReGuide is similar to that observed in Table C.2.1.

**Table C.2.2: Example responses of InternVL2-26B on $5\%$ subset of ImageNet200 benchmark.**
|  | Near-OoD | Far-OoD | Prediction<br>InternVL2-26B<br>+ GPT-text<br>+ ReGuide |  |
| --- | --- | --- | --- | --- |
|  | GPT-text suggested OoD classes |  |  |  |
|  | siamese cat, bengal tiger cub, cheetah cub, lynx, arctic wolf, manatee, walrus, otter, arctic fox, albatross, macaw, snowy owl, peregrine falcon, arctic hare, hedgehog, woodpecker, puffin, seahorse, swordfish, caracal | motorcycle, bulldozer, windmill, skyscraper, piano keyboard, surfboard, basketball hoop, bookshelf, drone, traffic light, skis, space rover, diving board, ferris wheel, telescope, typewriter, electric fan, hot air balloon, sewing machine, xylophone |  |  |
|  |  | ReGuide suggested OoD classes |  |  |
| ImageNet200 |  | marlin, bonito, remora, great white shark, sailfish, tiger shark, swordtail, flying fish, barracouta, menhaden, electric eel, smelt, pilotfish, thresher shark, squid, tuna, herring, bluefin tuna, mackerel, pufferfish | nautilus, sea urchin, sand dollar, starfish, oyster, krill, cuttlefish, sea spider, snail, horseshoe crab, mahimahi, conch, guppy, copepod, abalone, sunfish, barracuda, octopus, brittle star, mullet | great white shark |
|  |  | swordfish |  |  |
| hammerhead |  |  | hammerhead |  |
| SSB Hard |  | baseball diamond, baseball uniform, baseball field, baseball helmet, umpire, athlete, baseball game, softball player, baseball team, baseball, outfielder, baseball bat, baseball jersey, second baseman, pitcher | baseball pants, fielder, catcher, shortstop, baseball stadium, sports player, first baseman, baseball glove, baseball cap, baseball card, third baseman, coach, baseball mitt, baseball player, baseball cleats, batter, referee | baseball player |
|  |  | baseball player |  |  |
| softball |  |  | softball player |  |
| Textures |  | polka dot, cerise, cherry red, blush, cherry blossom, fuchsia, garnet, coral, rose, peach, roseate, salmoncolored | cherry, pink, vermilion, crimson, wine red, salmonpink, scarlet, magenta, ruby, maroon, burgundy, salmon pink, mauve | scarf |
|  |  | none of these classes |  |  |
| dotted |  |  | polka dot |  |

**Table C.2.3: Example responses of GPT-4o on $5\%$ subset of ImageNet200 benchmark.**
|  | ReGuide suggested OoD classes | Prediction |  |  |
| --- | --- | --- | --- | --- |
|  | Near-OoD | Far-OoD | GPT-4o<br>+ ReGuide |  |
| ImageNet200 |  | greyhound, lurcher, saluki, rhodesian ridgeback, vizsla, basenji, irish wolfhound, doberman, ibizan hound, deerhound, basque shepherd, great dane, galgo, pharaoh hound, borzoi | umbrella, wristwatch, bicycle, electric fan, soccer cleat, refrigerator, telescope, skateboard, basketball hoop, vacuum cleaner, washing machine, piano, coffee maker, laptop, microwave, tennis racket, desk chair, bookcase, alarm clock | greyhound |
| whippet |  |  | whippet |  |
| SSB Hard |  | oriole, bunting, grosbeak, vireo, sparrow, kinglet, finch, linnet, wren, pine siskin, nuthatch, phoebe, treecreeper, tanager, canary, starling, chickadee, warbler, titmouse | umbrella, wristwatch, bicycle, electric fan, soccer cleat, alarm clock, tennis racket, refrigerator, telescope, skateboard, basketball hoop, vacuum cleaner, washing machine, piano, laptop, microwave, desk chair, bookcase, coffee maker | goldfinch |
| yellowhammer |  |  | none of these classes |  |
| Textures |  | fish scales, coral, chainmail, turtle shell, reptile skin, armadillo shell, rocky surface, crocodile skin, bark texture, mesh fabric, mineral formation, mosaic, sandstone, fossil texture, lizard skin, dragon scales, pangolin scales, pebbles, snake skin, textured leather | umbrella, wristwatch, bicycle, electric fan, soccer cleat, alarm clock, tennis racket, refrigerator, telescope, skateboard, basketball hoop, vacuum cleaner, washing machine, piano, laptop, microwave, desk chair, bookcase, coffee maker | goldfish |
| scaly |  |  | fish scales |  |

**Table C.2.4: ReGuide results on inputs that produce valid responses across three models on $5\%$ subset of the ImageNet200 benchmark. The AURC values are multiplied by $10^{3}$, and the other values are percentages. ‘Valid’ indicates the ratio of valid responses out of a total of $4,170$ image-prompt queries. Bold denotes the best performance among the results from each model.**
| Models |  | ID | Near-OoD | Far-OoD | All OoD |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | ImageNet200 | NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |  |  |
| Valid | ACC ($\uparrow$) | ECE ($\downarrow$) | AURC ($\downarrow$) | FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| InternVL2-26B + ReGuide | 65.46 (2,730) | 95.1 | 10.67 | 36.65 | 23.87 / 86.42 | 15.02 / 90.92 | 1.54 / 98.3 | 3.21 / 97.80 | 2.11 / 97.99 | 10.22 / 93.6 |
| InternVL2-76B + ReGuide | 65.46 (2,730) | 93.47 | 1.33 | 7.76 | 56.13 / 92.46 | 66.8 / 96.13 | 63.58 / 96.13 | 58.85 / 94.23 | 64.88 / 94.97 | 64.71 / 92.01 |
| GPT-4o + ReGuide | 65.46 (2,730) | 91.43 | 1.33 | 10.13 | 98.06 / 96.16 | 99.31 / 96.6 | 99.69 / 96.6 | 98.93 / 95.24 | 98.27 / 96.29 | 99.09 / 94.01 |

### C.3 Analysis on OoD Scores with ReGuide

Fig. C.3.1 presents the ROC curves for InternVL2-26B and GPT-4o, both with and without ReGuide. As shown in Fig. C.3.1(d), GPT-4o+ReGuide becomes slightly overconfident compared to GPT-4o without ReGuide. At TPR values near $100.00\%$ and $0.0\%$, the ROC curve indicates cases where OoD inputs are assigned higher confidence scores than ID inputs. Similarly, ROC on InternVL2-26B+ReGuide (Fig. C.3.1(b)) follows a pattern similar to that of GPT-4o+ReGuide. However, the FPR for InternVL2-26B+ReGuide is lower than that of InternVL2-26B without ReGuide, as the latter shows more instances where ID inputs receive lower confidence scores than OoD inputs, as depicted in Fig. C.3.1(a).

Tab.  C.3.1 presents cases where the model exhibits overconfidence, specifically highlighting samples at the two extremes –$100.00\%$ and $0.0\%$– of the ROC curve. These cases of overconfidence can be broadly categorized into two main issues. The first issue is OpenOoD labeling noise. As shown in Tab.  C.3.1, there are cases where an object occupying a small portion of the image is labeled as the ground truth instead of the dominant object. For example, InternVL2-26B suggests flower-related classes, while the ground truth is a bee. In the case of label noise, employing ReGuide can result in assigning high confidence to incorrect classes, leading to overconfidence. The second issue relates to the inherent characteristics of LVLMs. The models tend to assign high confidence to the most probable class rather than distributing confidence across multiple plausible classes. For instance, although many plausible classes are suggested in the Near-OoD category, GPT-4o assigns high confidence to only one class. InternVL2-26B shows a similar tendency. This overconfidence in a single class, rather than a broader distribution of confidence across possible classes, highlights a fundamental limitation of LVLMs.

Figure: (a) InternVL2-26B
Refer to caption: x22.png

**Table C.3.1: Samples at the extremes of the ROC curves for the ImageNet200 benchmark. Bold indicates the predicted class and its confidence score before applying softmax. Classes with a confidence score of $0.0$ have been omitted.**
|  |  | Model |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | GPT-4o | InternVL2-26B |  |  |
| ROC curve | 100.00% | grand piano | $\bullet$ Dataset: ImageNet200<br>$\bullet$ Near-OoD: accordion keys, harpsichord, harmonium, headphones, electric organ, concert grand, synthesizer, keyboard, grand piano mechanism<br>$\bullet$ Far-OoD: umbrella, hammer action, wristwatch, bicycle, hairdryer, lamp, electric fan, tennis racket, blender, refrigerator<br>$\bullet$ Prediction: grand piano mechanism<br>$\bullet$ {grand piano mechanism : 99.99, none of these classes: 0.00} | bee | $\bullet$ Dataset: ImageNet200<br>$\bullet$ Near-OoD: sunflower, dandelion, marigold, daisy, tulip, rose, lily, orchid, iris, carnation, peony, chrysanthemum, pansy, violet, hyacinth<br>$\bullet$ Far-OoD: dandelion clock, aloe vera, dandelion head<br>$\bullet$ Prediction: cactus flower<br>$\bullet$ {cactus flower: 100.00, none of these classes: 0.00} |
| 0.00% | ball | $\bullet$ Dataset: SSB Hard<br>$\bullet$ Near-OoD: tree, mountain, baseball game, baseball diamond, baseball uniform, baseball cap, baseball glove<br>$\bullet$ Far-OoD: umbrella, hammer action, wristwatch, bicycle, hairdryer, lamp, electric fan, tennis racket, blender, refrigerator<br>$\bullet$ Prediction: baseball player<br>$\bullet$ {baseball player: 100.00, none of these classes: 0.00} | amphiuma means | $\bullet$ Dataset: NINCO<br>$\bullet$ Near-OoD: conger, moray, catfish, pike, salmon, tuna, swordfish, marlin<br>$\bullet$ Far-OoD: umbrella, hammer action, wristwatch, elephant, rhinoceros, walrus, narwhal, tuskfish, horn<br>$\bullet$ Prediction: eel<br>$\bullet$ {eel: 100.00, none of these classes: 0.00} |  |

Fig. C.3.2(a) and C.3.2(b) depict the OoD score distributions for InternVL2-26B and GPT-4o, respectively. As shown in Fig. C.3.2(a), with ReGuide, InternVL2-26B produces OoD scores ranging from $0.00$ and $100.00$, which is not observed without ReGuide. However, the effect of ReGuide on GPT-4o differs from that on InternVL2-26B. The OoD score distribution for GPT-4o becomes more biased with ReGuide, as illustrated in Fig. C.3.2(b). The number of responses with OoD scores between $0.00$ and $100.00$ decreases significantly.
We infer that this outcome is due to the strong visual recognition ability of GPT-4o. As shown in Tab. 2, the suggestions from GPT-4o include classes that are highly similar to the given image.

Figure: (a) InternVL2-26B
Refer to caption: x26.png

For a more detailed analysis of the ReGuide effect, we examine the OoD score distributions for ID and OoD inputs separately. Fig. C.3.3 displays the OoD score distributions for ID and OoD inputs on InternVL2-26B and GPT-4o. A common observation between InternVL2-26B and GPT-4o is the shift in the OoD score distribution of OoD inputs. As shown in Fig. C.3.3(b) and C.3.3(d), ReGuide increases the number of OoD inputs assigned an OoD score of 0 0, indicating that the models successfully classify these inputs into one of the suggested OoD classes, thereby enhancing OoD detectability. This trend is also observed, although to a lesser extent, for ID inputs in both models, as presented in Fig. C.3.3(a) and C.3.3(c). This reveals the underlying cause of the overconfidence behaviors observed in Fig. C.3.1(b) and Fig. C.3.1(d). The increasing number of ID inputs with low OoD scores leads to a misalignment between the OoD scores of ID and OoD inputs. We infer that this is due to some near-OoD class suggestions being overly similar or nearly synonymous with the ID classes, causing ID inputs to be misclassified into the suggested OoD classes. Despite this, ID accuracy with ReGuide improves compared to the models without ReGuide, as shown in Tab. 4.

Figure: (a) InternVL2-26B ID
Refer to caption: x28.png

### C.4 Comparison of OoD Score Functions

When incorporating available auxiliary OoD classes, one effective scoring strategy is to include confidence estimates for these auxiliary OoD classes. For example, NegLabel (Jiang et al., ) uses a scoring function that calculates the ratio of the sum of ID softmax values to the sum of both ID and OoD softmax values, while EOE (Cao et al., ) utilizes the difference between the maximum ID softmax and the maximum OoD softmax.

We compare three different scoring functions: 1) the maximum ID softmax, 2) the difference between the sum of ID softmax and OoD softmax, and 3) the difference between the maximum ID softmax and maximum OoD softmax. Each scoring function is represented in order as follows:

$$ $\mathcal{S}_{\max}(\mathbf{x},\mathcal{Y},\mathcal{A}_{\text{aux}})=\max_{i\in \mathcal{Y}}\frac{e^{s_{i}/\tau}}{\sum_{k\in\mathcal{Y}\cup\mathcal{A}_{\text{ aux}}}{e^{s_{k}/\tau}}}$ (1) $$

$$ $\mathcal{S}_{\text{sumsub}}(\mathbf{x},\mathcal{Y},\mathcal{A}_{\text{aux}})= \sum_{i\in\mathcal{Y}}\frac{e^{s_{i}/\tau}}{\sum_{k\in\mathcal{Y}\cup\mathcal{ A}_{\text{aux}}}{e^{s_{k}/\tau}}}-\sum_{j\in\mathcal{A}_{\text{aux}}}\frac{e^{ s_{j}/\tau}}{\sum_{k\in\mathcal{Y}\cup\mathcal{A}_{\text{aux}}}{e^{s_{k}/\tau}}}$ (2) $$

$$ $\mathcal{S}_{\text{maxsub}}(\mathbf{x},\mathcal{Y},\mathcal{A}_{\text{aux}})= \max_{i\in\mathcal{Y}}\frac{e^{s_{i}/\tau}}{\sum_{k\in\mathcal{Y}\cup\mathcal{ A}_{\text{aux}}}{e^{s_{k}/\tau}}}-\max_{j\in\mathcal{A}_{\text{aux}}}\frac{e^{ s_{j}/\tau}}{\sum_{k\in\mathcal{Y}\cup\mathcal{A}_{\text{aux}}}{e^{s_{k}/\tau}}}$ (3) $$

where $\tau$ is a temperature.

Tab. C.4.1 presents the results using these scoring functions. All results are based on a $5\%$ subset of the ImageNet200 benchmark using InternVL2-26B+ReGuide. No significant differences are observed among the scoring functions. LVLMs tend to assign high confidence to a single predicted class, resulting in similar results regardless of the different scoring designs. Therefore, all results reported in our paper are measured by $\mathcal{S}_{\text{max}}$.

**Table C.4.1: Comparison results of different scoring functions on a $5\%$ subset of ImageNet200 benchmark using InternVL2-26B**
|  | Near-OoD | Far-OoD | All OoD |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| NINCO | SSB-Hard | iNaturalist | Textures | Openimage-O |  |  |
| FPR@95%TPR ($\downarrow$) / AUROC ($\uparrow$) |  |  |  |  |  |  |
| $\mathcal{S}_{\text{max}}$ | 22.92 / 86.60 | 16.25 / 89.98 | 1.23 / 98.19 | 3.81 / 97.15 | 2.23 / 97.67 | 10.79 / 93.01 |
| $\mathcal{S}_{\text{sumsub}}$ | 23.44 / 86.70 | 16.25 / 90.23 | 10.84 / 97.75 | 3.33 / 97.21 | 2.55 / 97.82 | 12.17 / 93.12 |
| $\mathcal{S}_{\text{maxsub}}$ | 23.44 / 86.73 | 16.32 / 90.23 | 1.23 / 98.29 | 3.81 / 97.22 | 2.23 / 97.82 | 10.86 / 93.20 |

### C.5 Prompt Format

We provide the full prompts used for experiments in Sec. 4.1. In the prompt, yellow, green, red, and blue boxes indicate a task description, an explanation of the rejection class, guidelines, and examples for the response format, respectively.

#### C.5.1 Prompt for Class Suggestion of GPT-text

Fig. C.5.1 illustrates the prompt used for class suggestion of GPT-text. The guidelines consist of two key instructions: first, to avoid suggesting classes that are too similar to the image, and second, to avoid suggesting classes that are identical to the ID class. The red text indicates that the contents vary depending on the selected benchmark.

Figure: Figure C.5.1: Prompt used for class suggestion of GPT-text
Refer to caption: x32.png

#### C.5.2 Prompt for ReGuide Stage 1

Fig. C.5.2 illustrates the prompt used for class suggestion of ReGuide Stage 1.
Similar to the original prompt used for evaluating OoDD and the prompt used for class suggestion in GPT-text, we provide guidelines to prevent uninformative class names, such as ID class names, overly similar classes, or simple repetitions of class names. Without an example, the response format of image-based suggestions tends to deviate more than that of text-based suggestions. Thus, we include the response format as an illustrative example. The red text indicates that the contents vary depending on the selected benchmark.

Figure: Figure C.5.2: Prompt used for ReGuide Stage 1
Refer to caption: x33.png

#### C.5.3 Prompt for ReGuide Stage 2

Fig. C.5.3 illustrates the prompt used for ReGuide Stage 2. The red text indicates that the contents vary depending on the selected benchmark. The blue highlights the locations of the suggested OoD classes from Stage 1. The highlighted text in yellow indicates modifications made to incorporate these suggested classes. GPT-text employs the same prompt format for the classes suggested by GPT-4o.

Figure: Figure C.5.3: Prompt used for ReGuide Stage 2
Refer to caption: x34.png

### C.6 Limitations of ReGuide

While the proposed ReGuide improves performance in both image classification and OoDD tasks, there remain limitations. These challenges are primarily caused by the difficulty in exerting precise control over the behavior of the VLM. Due to the inherent complexity of understanding the implicit dynamics of LVLMs, controlling their behavior through prompting does not always ensure the desired outcome. Although we provide additional guidelines based on the model’s failure cases to mitigate unintended responses, this approach is insufficient for full control over outputs. To address unintended outputs, we can utilize LLMs to iteratively refine prompts based on the model’s responses. Leveraging external LLMs for this purpose is expected to function similarly to model ensemble principles, resulting in prompts better suited to the model compared to heuristic refinements.

From a visual perspective, ReGuide relies on the image understanding capabilities of LVLMs. Any potential improvement depends on the model’s ability to recognize visuals accurately. Additionally, since our proposed approach is image-adaptive, the quality of the suggested class set is influenced by the image context. For instance, if the target object occupies only a small portion of the image, an LVLM may focus more on other objects in the background. This tendency can result in class suggestions that are either uninformative or overly broad.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 3
presentation: 2
questions: - Why we need to evaluate the capabilities 
- as LLM gets huge weekly, the measurement still makes sense?
soundness: 2
strengths: - easy to read
- easy to duplicate
summary: The goal for the paper is to evaluate the out-of-distribution detection (OoDD) capabilities of LLMs (some private and open models)
weaknesses: - motivation for the work
- limited impacts
- extending from a prior work in text-based method.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: see weakness
soundness: 3
strengths: Strengths:
•This paper represents the first effort to extend zero-shot Out-of-Distribution Detection (OoDD) to generative Large Vision-Language Models (LVLMs) in reference to CLIP. Building upon this foundation, the authors define the task and propose corresponding evaluation methodologies, which offer valuable insights for future research；
•The paper evaluates and analyzes the OoDD capabilities of various proprietary and open-source LVLMs. Furthermore, the authors propose a two-stage self-guided prompting approach, termed Reflexive Guidance (ReGuide), to unlock the potential of LVLMs. Experimental results demonstrate that the ReGuide enhances the performance of LVLMs in both image classification and OoDD tasks.
summary: This work proposes a self-guided prompting approach, termed Reflexive Guidance (ReGuide), aimed at enhancing the OoDD capability of LVLMs by leveraging self-generated image-adaptive concept suggestions. Experimental results demonstrate that our ReGuide enhances the performance of current LVLMs in both image classification and OoDD tasks
weaknesses: Weaknesses:
•The paper introduces new tasks and evaluation metrics. However, certain details in this regard are somewhat lacking. For instance, the paper mentions using only 25% of the benchmark subsets but does not specify which portions are utilized. It would be preferable to provide a list of these subsets to facilitate the replication and comparison of subsequent work. Additionally, other similar details need to be verified.

•In the experimental evaluation, the ‘valid ratio’ varies across different methods. And only valid responses are included when measuring performance, which results in inconsistencies in the samples used to calculate metrics for each method. This may not be justifiable. Have you considered adopting a more consistent evaluation approach?

•The novelty of this paper is somewhat limited.  Firstly, it introduces the OoDD task for LVLMs; however, the rationale behind this task remains debatable. Considering the robust capabilities of LVLMs and their ongoing advancements, LVLMs are anticipated to directly recognize objects within images. In comparison, the method proposed in this paper is overly complex and excessively reliant on prompts. Secondly, the approach presented in the paper appears to be more akin to prompt engineering, lacking substantial innovative contributions.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Please see Weaknesses.
soundness: 3
strengths: [Promising research topic] This paper focuses on an under-explored topic, the reliability of large vision-language models. [Benchmark contribution] This paper define the concept of Out-of-Distribution Detection on LVLMs, and conduct extensive experiments to build a benchmark and give insightful analysis. [Method contribution] Furthermore, they propose a method ReGuide on the prompt to boost the performance of LVLMs. They verify the effectiveness of the proposed method on GPT-4o and InternVL.
summary: Overall this is a solid paper. This paper addresses the underexplored trustworthiness of foundation models, particularly focusing on the out-of-distribution detection (OoDD) capabilities of large vision-language models (LVLMs). The authors highlight the gap between the demonstrated potential and practical reliability of LVLMs, raising concerns about their safe deployment. To address this, the researchers evaluate and analyze the OoDD capabilities of various proprietary and open-source LVLMs. They propose a novel approach called Reflexive Guidance (ReGuide), which aims to enhance the OoDD capability of LVLMs by using self-generated image-adaptive concept suggestions. Experimental results show that ReGuide improves the performance of current LVLMs in both image classification and OoDD tasks.
weaknesses: Since the out-of-distribution detection on vision-language foundation models is a new definition proposed in this paper, I would suggest to add a demonstration figure to show this concept in a visually straightforward way. You can compare the conventional OOD, OoDD on CLIP and OoDD on LVLMs.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 2
questions: N/A
soundness: 2
strengths: 1. The introduction of the ReGuide method is interesting. By using image-adaptive concept generation, the authors provide a unique solution to enhance OoD detection in LVLMs. This approach leverages the strong visual recognition capabilities of these models, offering a novel way to bridge gaps in OoD detectability.

2. Comprehensive Evaluation: This paper comprehensively evaluates both proprietary and open-source LVLMs, providing a balanced comparison across different models. This may be helpful for future research.

3. Detailed Analysis: The authors provide a well-rounded analysis, including experimental results, visual feature interpretation, and the effectiveness of ReGuide on near- and far-OoD datasets. This depth of analysis gives credibility to their findings and demonstrates the robustness of their proposed method. Furthermore, it demonstrates the potential of introducing LVLMs to the area,
summary: This paper explores to enhance out-of-distribution detection (OoDD) in large vision-language models (LVLMs). The authors emphasize the increasing importance of these models in various domains and highlight the challenges related to their trustworthiness, specifically in dealing with out-of-distribution (OoD) inputs. To address this, they propose a self-guided prompting approach called Reflexive Guidance (ReGuide), which leverages LVLMs’ visual interpretation capabilities to improve OoD detection by generating image-adaptive concept suggestions. The experiments demonstrate that ReGuide significantly boosts the performance of both open-source and proprietary models in OoD detection tasks.
weaknesses: 1. Limited Explanation of Failures: Although the paper provides examples of failure cases, it does not fully delve into the underlying causes or offer detailed solutions for these issues. While the authors acknowledge the problem of overconfidence in models such as GPT-4o with ReGuide, further exploration of these limitations could strengthen the study. There is still not a robust method to effectively introduce LVLMs to the OoD tasks.

2. Model-Specific Insights: The paper focuses on generic findings across models, but a deeper investigation into how specific models (e.g., GPT-4o vs. InternVL2) behave differently when ReGuide is applied could add nuance to the conclusions. For example, the differences in false positive rates (FPR) between models with and without ReGuide should be presented for a better comparison. 

3. Scalability and Practicality: While the ReGuide method shows a promising direction, the computational overhead and API limitations mentioned in the paper could present challenges for practical, large-scale implementation. This issue is touched upon but not sufficiently addressed in terms of how ReGuide might be optimized for deployment at scale. Meanwhile, the inference cost analysis can further improve the paper's quality and inspire further work.

Meta-review: This paper aims at improving the OoDD capability of large language models by adopting a self-guided prompting approach, which is somewhat solid.

Reviewers propose many concerns, such as the defination of this new task, the details of experimental evaluations, and these concerns are well addressed during th rebuttal period. The authors are required to update/revise their paper according to the reviewer's suggestions.

Three reviewers lean to accept this paper and one reviewer remain suggest to reject this work; however, their confidence scores were not very high (scores: 3, 4, 3, 3). After carefully reading the discussion content and the paper, I decide to ignore the negative score from Reviewer Mv3D due to its low quality and finally make the acceptance recommendation to this work.

Decision: Accept (Poster)