TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-27 08:16:35

Abstract: Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing methods for out-of-distribution (OOD) detection, a critical area where AI models encounter unseen data in real-world scenarios, rely heavily on whole-image features. These image-level features can include irrelevant information that hinders the detection of OOD samples, ultimately limiting overall performance. In this paper, we propose \textbf{TagOOD}, a novel approach for OOD detection that leverages vision-language representations to achieve label-free object feature decoupling from whole images. This decomposition enables a more focused analysis of object semantics, enhancing OOD detection performance. Subsequently, TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection. Finally, our approach efficiently detects OOD samples by calculating distance-based metrics as OOD scores between learned centers and test samples. We conduct extensive experiments to evaluate TagOOD on several benchmark datasets and demonstrate its superior performance compared to existing OOD detection methods. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, with potential applications across various tasks. Code will be available.

Main paper: 
 related work 2.1 vision-language models for image tagging: 
 Recent advancements [1, 10, 44] in large language models (LLMs) have revolutionized natural language processing (NLP). In computer vision (CV), vision-language models (VLMs) [29, 56] bridge the gap between visual and textual data, leveraging the strengths of both modalities for tasks like image tagging. VLMs fall into two main categories: generation-based and alignment-based. Generation models create captions for images, with recent advancements in language models [7, 35] making this approach dominant. Unlike earlier methods [9] that relied solely on recognizing tags and then composing captions, these models [5, 26, 50] leverage powerful language models for text generation conditioned on visual information. Alignment models, on the other hand, aim to determine if an image and its description match. Many works [21, 22, 35] typically rely on aligning features from both modalities, often using a dual-encoder or fusion-encoder architecture. Image Tagging, a crucial computer vision task that involves assigning multiple relevant labels to an image, plays a vital role in VLM performance.Traditionally, classifiers and loss functions were used for image tagging. Recent work [30, 39] utilizes transformers and robust loss functions to address challenges like missing data and unbalanced classes. Our work utilizes this powerful VLM technology to enhance OOD detection performance.Out-of-distribution detection aims to distinguish OOD samples from IND data. Numerous methods have been proposed for OOD detection. Maximum softmax probability (MSP) [16] serves as a common baseline, utilizing the highest score across all classes as an OOD indicator. ODIN [28] builds upon MSP by introducing input perturbations and adjusting logits through rescaling. Gaussian discriminant analysis (GDA) has also been employed for OOD detection in works by [25, 52] . ReAct [42] leverages rectified activation to mitigate model overconfidence in OOD data. In recent developments, several multi-modal methods [8, 33, 34, 48] have emerged to address the challenge of OOD detection, utilizing the generalized representations acquired through CLIP [11] . CLIPN [48] introduces an extra text encoder to CLIP, aligning entire images with two types of textual information and enabling the model to respond with "no" when faced with OOD samples. MCM [34] aligns imagelevel features with their corresponding textual description features, enhancing the capabilities of IND estimates and consequently improving OOD detection performance. While CLIP demonstrates remarkable zero-shot OOD detection capabilities, these OOD detection methods utilizing CLIP still neglect to address the challenge of similar object confusion between IND and OOD images. Different from these CLIP-based methods, we attempt to exploit an image tagging model to generate multiple tags in terms of mitigating the impact of confused objects. 
method 3.1 preliminary: 
 Out EQUATIONAs shown in Equation ( 1), the function outputs 0 if the image likely belongs to the OOD data, and 1 if likely belongs to the IND data.In practice, the OOD score function provides a continuous value between 0 and 1, with a higher score indicating a higher likelihood of the data point being IND. This score is then used to classify the test data into IND and OOD categories.Our proposed TagOOD approach aims to address a key challenge in OOD detection: distinguish the confusing OOD samples, which contain objects similar to objects found in the training data. These objects in test samples can be particularly problematic because they are non-labeled but occur in in-distribution data. TagOOD leverages a pre-trained vision-language model to identify and label objects within an image, enabling feature decomposition.In practice, we use RAM [56] , which combines three components for generating image features, tags, and corresponding attention maps required for feature decomposition: 1) Image Encoder 𝒇 captures features from the input image, capturing visual information about all of the objects within the image. 2) Text Encoder 𝒈 based on the CLIP model [38] encodes potential object tags from a pre-defined vocabulary 𝑇 into a common feature space. This vocabulary 𝑇 = {𝑡 𝑖 } 𝑀 𝑖=1 , provided by RAM, containing 𝑀 object used for more detailed image labeling. 3) Tagging Head 𝝓 combines the image features and tag information to predict the final result. The result contains the tags 𝑇 𝑥 = {𝑡 } for the objects within the image and corresponding attention maps 𝐴 𝑥 = {𝑎}, where 𝑎 ∈ R 𝐻 ×𝑊 . In practice, we utilize the last cross-attention module within the Tagging Head to generate attention maps. Eq. 2 summarizes how the tagging model operates:EQUATIONWhen the vision-language model processes an image 𝑥, we will obtain an image feature 𝒇 (𝑥), predicted tags 𝑇 𝑥 for the objects within the image, and corresponding attention maps 𝐴 𝑥 for each predicted tag. We then filter 𝑇 𝑥 to retain only the tags belonging to our IND vocabulary 𝑇 𝑖𝑛 . TagOOD leverages the attention maps 𝐴 𝑥 to achieve image feature decomposition by selecting IND object features from the full image feature 𝒇 (𝑥). In Figure 2 (a), Our filter applies a threshold 𝜏 to the attention map values corresponding to the object. This essentially creates a binary mask (values above 𝜏 become 1, others become 0), highlighting the most influential image regions of each object. Instead of element-wise multiplication, TagOOD directly selects features from the original image representation corresponding to locations with 1 in the mask. This effectively chooses the parts of the image feature that are most relevant to the objects based on the predicted tag. When multiple IND objects are detected, their corresponding object features are combined into a single feature representation, denoted as 𝑧. This combined feature vector then undergoes reshaping to prepare it for the next step of the model. Through the process described above, we can obtain a training dataset consisting of object features and their corresponding labels. This dataset is denoted as 𝒵 𝑡𝑟𝑎𝑖𝑛 𝐼 𝑁 𝐷 = {(𝑧 𝑖 , 𝑦 𝑖 )} 𝑛 𝑖=1 , where 𝑧 𝑖 represents the object features for the 𝑖-th sample, 𝑦 𝑖 represents the corresponding label for the 𝑖-th sample, indicating its IND category.Since the original vocabulary 𝑇 might not encompass all IND labels, we construct an IND vocabulary which is a subset of 𝑇 , denoted by 𝑇 𝑖𝑛 . Before training TagOOD, we go through a process to select the most relevant tags 𝑇 𝑖𝑛 representing the IND data from 𝑇 . 𝑇 𝑖𝑛 are used during training to establish the central tendencies of objects within each category. We collect all the tags associated with the objects within our training data. To ensure the selected tags accurately represent the ground truth, human experts review the results after identifying the most frequent tag within each category. Through this process, we obtain a set of IND tags 𝑇 𝑖𝑛 from the predefined vocabulary 𝑇 that best corresponds to the object categories present in the training data. 2(b) , the model receives the object features 𝑧, which can have varying sizes depending on the specific objects. The projection model transforms these features 𝑧 into a common feature space. This ensures consistency when comparing the projected features with 𝜇. The object feature 𝑧 𝑐 and its label 𝑦 𝑐 of category 𝑐 are used to calculate a combined loss function: TagOOD utilizes an exponential moving average (EMA) [13] with 𝒑(𝑧) to achieve this update. EMA helps smooth out fluctuations in the projected features and provides a more stable estimate of the central tendencies for each IND category. According to the process we described above, the parameter of projection model 𝜃 𝑝 and the class centers {𝜇 𝑖 } 𝐾 𝑖=1 are updated by the following equations:EQUATIONEQUATIONEQUATIONwhere 𝛾 1 , and 𝛾 2 are learning rates controlling the update speed. EQUATIONIf the tagging model predicts no tags in 𝑇 𝑖𝑛 for a given image, TagOOD directly classifies the sample as OOD. Overall, by comparing the test sample feature (𝒑 supports both image feature or object feature) to the learned class centers using cosine similarity, TagOOD can effectively distinguish between in-distribution and out-of-distribution data, enhancing the model's robustness in real-world applications. 
experiments 4.1 experimental setup: 
 In-distribtuion Dataset. We adopt the well-established ImageNet-1K [40] dataset, a standard dataset for OOD detection, as our indistribution data. ImageNet-1K is a large-scale visual recognition dataset containing 1000 object categories and 1281167 images.Out-of-Distribution Dataset. Based on ImageNet as IND datasets, we follow Huang et al. [20] to test our TagOOD with iNaturalist [46] , SUN [53] , Places365 [57] , and Texture [6] . To further explore the generalization ability of our approach, we follow Wang et al. [49] and employ another two OOD datasets, OpenImage-O [24] and ImageNet-O [17] to evaluate our method. Evaluation Metrics. We evaluate our approach using common OOD detection metrics [20, 34, 42, 54, 55] : Area Under the ROC Curve (AUROC). This metric summarizes the model's ability to distinguish between IND and OOD samples. Higher AUROC indicates better overall performance. False Positive Rate at 95% True Positive Rate (FPR95). This metric measures the model's Training Details. We leverage RAM [56] as the underlying visionlanguage model for OOD sample detection. For consistency across all compared methods, the feature extractor within RAM, swinlarge [32] , is used for all approaches. Our lightweight projection model employs two serially connected self-attention block layers. The projected feature space has a dimensionality of 512. ImageNet-1k is used for training, with a total of 100 epochs, and the class centers are initialized with Gaussian noise for the 1000 ImageNet categories. The hyperparameters 𝛼, 𝛽, 𝛾 1 , 𝛾 2 are set to 1, 0.1, 1, 1 × 10 -4 , respectively. These values were chosen based on our experimental results. During training, The Adam optimizer [23] is used during training with an initial learning rate of 0.01, a Cosine Annealing learning rate scheduler, and a batch size of 256. All the experiments are performed using PyTorch [36] with default parameters on two NVIDIA V100 GPUs.Standard evaluation on ImageNet. We compare the performance of our TagOOD against seven popular OOD detection approaches, including MSP [16] , ODIN [28] , Energy [31] , ReAct [42] , MCM [34] , LHAct [55] , FeatureNorm [54] . All methods, except MCM, utilize Swin-L as the backbone extractor for a fair comparison. MCM employs a larger and more complex CLIP-based ViT-L model. As shown in Table 1 , our method achieves better AUROC and FPR95 metrics. We highlight that TagOOD achieves 27.85% on FPR95, which outperforms the previous best method ReAct [42] by 7.42% across various OOD datasets. While MCM [34] exhibits higher AUROC and lower FPR95 on SUN and Places datasets due to its significantly larger CLIP ViT-L backbone, which is 1.7 times the size of Swin-L, Ta-gOOD maintains the best average performance across all datasets. Our analysis suggests that LHAct [55] and FeatureNorm [54] might suffer limitations due to hyperparameter settings optimized for a different backbone network. This mismatch between hyperparameters and the chosen Swin-L model can lead to suboptimal performance on these methods. The study by Bitterwolf et al. [2] highlights a crucial aspect of OOD detection: the overlap between training and OOD data. The commonly used datasets in our evaluation (detailed in Section 4.1) exhibit varying degrees of overlap with the ImageNet-1k training data. This overlap can potentially influence the evaluation of a model to identify OOD samples. In Figure 3 , the black-tagged images are from ImageNet for reference and represent a true IND class sample. The samples with red tags, selected from the SUN and Places datasets, contain IND class objects. These red tags indicate the corresponding OOD scores assigned by our method. TagOOD correctly assigns them OOD scores with high confidence, indicating it is reliable to accurately distinguish IND objects. This highlights the robust OOD detection performance of TagOOD, leveraging the power of the vision-language model. Evaluation on more challenging OOD datasets.To further assess the limitations and robustness of our TagOOD, we conducted experiments on two challenging datasets: OpenImage-O [24] and ImageNet-O [17] . OpenImage-O contains a diverse set of OOD samples, while ImageNet-O specifically includes adversarial examples designed to fool detection models. As shown in Table 2 , TagOOD demonstrates strong performance on OpenImage-O, indicating its effectiveness in handling general OOD data. However, on ImageNet-O, which contains specifically crafted adversarial examples, the performance of TagOOD shows some limitations while still achieving the best AUROC score. This suggests that further exploration is needed to enhance TagOOD's resilience against adversarial attacks.How does image feature decomposition affect the performance? We investigate the influence of the hyperparameter 𝜏 on image feature decomposition for our projection model training. Figure 4 visualizes the effects of varying 𝜏 values on model performance. Specifically, we set 𝜏 = {0, 0.3, 0.5, 0.7}. Higher 𝜏 values correspond to a more selective feature subset, prioritizing those with strong alignment to IND object classes. Conversely, 𝜏 = 0 represents a baseline where all image features are directly used for object model training. We observe that the optimal performance across all the OOD datasets used in our evaluation is achieved at 𝜏 = 0.5. This finding suggests a critical balance between feature selectivity and information retention for effective OOD detection. Interestingly, the performance suffers more significantly when 𝜏 decreases from 0.5. In contrast, increasing 𝜏 beyond 0.5 leads to only a slight performance degradation. We hypothesize that this occurs because higher 𝜏 values eliminate some redundant information without discarding critically important features for accurate OOD classification. In essence, our model demonstrates the ability to retain the most crucial information from image samples while discarding irrelevant features at 𝜏 = 0.5. This selectivity contributes to the reliable performance of TagOOD in identifying OOD data. What is the effect of the projection model and class centers? Table 3 and Figure 5 systematically explore the influence of the projection model and class centers on the performance of TagOOD. This analysis helps us understand how each step contributes to the final OOD detection effectiveness. Tag Score in Table 3 To gain deeper insights into the operation of TagOOD, Figure 5 utilizes T-SNE [45] for visualization. The data points represent object features, and colors encode their corresponding IND class labels. The gray 'X' markers represent OOD samples. A critical observation is the improved feature separation achieved through projection. Features that are directly procured from the tagging model, shown on the left, are likely to be scattered and overlapping. On the right side, we see the features after the projection operation in TagOOD. These features form tighter and more distinct clusters, with clearer boundaries between different IND classes. This enhanced separation in the projected feature space allows TagOOD to assign more distinguishable OOD scores. This facilitates the identification of OOD samples because their features deviate significantly from the learned representations of known object classes. Is the performance of TagOOD enhanced when training utilizes a single ground truth tag as opposed to multiple predicted tags? This ablation experiment explores the influence of tagging strategies on object feature selection for OOD detection. We evaluate two strategies: utilize a single tag corresponding to the ground truth for feature decomposition or leverage the predictions of the tagging model, generating multiple tags for each image. The decomposition is then guided by the attention masks constructed based on these predicted tags. As shown in Table 5 , the multiple tags strategy achieves superior performance compared to using the single ground-truth tag. We hypothesize that relying solely on the ground truth might lead to inaccurate feature decomposition due to potential tagging model errors. These errors could introduce "missing information" or "offset issues" in the decomposed features, hindering the ability to capture essential characteristics for effective OOD detection. In contrast, the multiple tags strategy incorporates the predictions of the tagging model, potentially offering a richer representation of the image content. This can lead to more accurate feature decomposition and the extraction of more informative features, ultimately enhancing OOD detection capabilities.  
limitations: 
 While TagOOD demonstrates promising results, there are areas where further exploration could enhance its capabilities. The performance of the tagging model can influence TagOOD's ability to accurately detect OOD data. Currently, TagOOD solely leverages the backbone network of the tagging model for feature extraction. This limits the potential for exploring alternative feature extractors that might be better suited for OOD detection tasks. Opening doors for investigating alternative feature extraction techniques will be an exciting future direction for this research. 
conclusion: 
 Existing OOD detection methods often rely on whole-image features, which can incorporate irrelevant information beyond IND objects. This paper introduces TagOOD, a novel approach that leverages the power of vision-language models (VLMs) to achieve more focused OOD detection. TagOOD tackles the challenge of OOD detection by decoupling images from their corresponding classes and generating class centers within a common feature space. This innovative approach offers many advantages: TagOOD employs a VLM to decouple images from their associated classes. This allows the model to focus on the essential semantic information and learn the IND area of each image that truly corresponds to the label, resulting in a more refined IND distribution for enhanced OOD detection. TagOOD generates object-level class centers for each category within the IND data. These class centers serve as reference points in the common feature space, enabling the model to distinguish OOD samples effectively. A distance-based metric is used between the test sample feature and class centers for OOD detection. This simple yet effective approach allows the model to identify samples that fall outside the expected distribution of the IND data. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, we hope our work contributes to the advancement of robust and reliable AI systems.  
object feature space: 
 Figure 1 : This toy sample illustrates a challenge in OOD detection using a 3D feature space. The features associated with "Bamboo" and "Withered grass" in the IND image are spatially close to the OOD image's features in this space. This proximity can lead the model to misclassify the IND image, mistaking it for containing similar objects to the OOD image.but also raise alerts to humans when encountering unknown samples. OOD detection is the task of determining whether a test sample falls within the in-distribution (IND) or not. This task finds broad applications in autonomous driving [3, 4] , fraud detection [37] , medical image analysis [12, 41, 51] , etc.Current OOD detection methods [18, 19, 25, 28, 31, 42, 43, 52 ] often rely on image-level features to construct a score function for identifying OOD samples. In these approaches, a test sample is classified as OOD if its score surpasses a threshold, or vice versa. A major limitation of these approaches is that a single label in the training data fails to fully describe the content of an image, especially when the image contains multiple objects. In such cases, the model can learn "OOD-like" features from the IND data, leading to misclassifications. In essence, the model can learn some "OODlike" characteristics even without encountering any actual OOD samples during training. A toy example is illustrated in Figure 1 . We utilize an image tagging model to generate tags for both an IND image labeled as "orangutan" and an OOD image. Splitting the image based on these tags exposes a critical insight for OOD detection: a part of the IND image shares surprising feature similarity with the OOD image. For example, tags like "bamboo" and "withered grass" might be present in both. This observation highlights a crucial disconnect between the model-captured visual features and the semantic meaning of the image. While the "orangutan" label might be accurate, the presence of "bamboo" and "withered grass" suggests the image includes background elements that could be commonly found in OOD data. This disconnect motivates our exploration of vision-language representations for OOD detection. Our approach integrates semantic information with visual features and learns the IND area of each image that truly corresponds to the label, resulting in a more refined IND distribution for enhanced OOD detection.In this paper, we propose TagOOD, a novel approach for Outof-Distribution detection that leverages vision-language representations. TagOOD specifically addresses the challenge of confusing OOD samples, which may contain objects visually similar to objects found in the in-distribution data. These OOD objects are particularly problematic because they not be explicitly labeled within the IND data. TagOOD tackles this challenge through two key mechanisms: 1) decoupling image features using a tagging model to focus on semantic content beyond the object, and 2) generating objectlevel class centers to capture the central tendencies of IND objects, minimizing the influence of irrelevant background features.TagOOD leverages a pre-trained tagging model, typically a large vision-language model trained on extensive image-text datasets. This model goes beyond single-label classification by identifying and assigning multiple semantic tags to objects within an image. By incorporating the tagging model, TagOOD focuses on object-level features for OOD detection. Specifically, for a given in-distribution image, the tagging model generates a set of object tags along with their corresponding features. The object features representing IND objects are then fed into a lightweight network for projection into a common feature space. This allows for the subsequent generation of trainable class centers, representing the central tendencies of each object category within the IND data. Finally, TagOOD calculates an OOD score based on the cosine similarity between these learned class centers and a test sample. A high cosine similarity indicates the test sample is likely IND data, as it closely resembles the central tendencies learned from the training data. Conversely, a low cosine similarity suggests the test sample is likely OOD.The contributions of this paper are summarized as follows:• Decoupling image features using a vision-language model: allows TagOOD to focus on the semantic content of the object, providing a deeper understanding of the IND data and mitigating the influence of irrelevant background features.• Generating object-level class centers: by capturing the central tendencies of objects within the in-distribution data, TagOOD can effectively distinguish between IND and OOD samples, even if they contain similar objects. • To investigate the effectiveness of TagOOD, we conducted comprehensive experiments and ablation studies on the popular ImageNet benchmark. These studies demonstrate competitive performance and reveal the importance of both decoupling features and generating object-level class centers for TagOOD's superior performance. 
 
 Appendix:  


Decision: The paper introduces TagOOD, a new method for out-of-distribution (OOD) detection that uses vision-language representations to isolate object features from whole images, improving the accuracy of OOD detection by focusing on object semantics and minimizing irrelevant features. Most of the reviewers recognise the clarity of writing and the innovative use of lvlm for OOD detection, and they all suggested to accept the paper. Therefore, I intent to accept the paper. The quality of the paper can be further improved to include more experimental results and have more clear comparison with previous works.

Reviews: review: paper_summary: paper_summary: The paper presents a new method for detecting out-of-distribution samples by leveraging the capabilities of vision-language models and learning representative class centers. The proposed TagOOD approach involves using a pre-trained tagging model to generate semantic tags for objects within an image, decoupling these object features from the entire image, and then training a lightweight network to learn class centers that capture the central tendencies of in-distribution object classes. This method aims to minimize the influence of irrelevant background features and improve the accuracy of OOD detection. The effectiveness of TagOOD is evaluated through extensive experiments on several benchmark datasets.
strength_weakness: strength_weakness: Detailed Methodology: The paper provides a thorough and well-documented description of the TagOOD method, outlining each step from the utilization of a vision-language model for tagging to the training of a lightweight network for class center learning.

Innovative Use of Vision-Language Models: The approach effectively leverages vision-language models to decouple object features from images, enabling a more focused analysis of object semantics and enhancing OOD detection performance.

Extensive Experimental Evaluation: The authors conduct comprehensive experiments on multiple benchmark datasets, comparing TagOOD with several existing OOD detection methods.
limitations: limitations: Lack of Novelty: The key idea of this paper is to minimize the influence of irrelevant background features.  Similar idea has been studied in [1]. However, the authors do not discuss this paper.

Insufficient Comparison with Training-Based Methods: The paper primarily compares TagOOD with post-hoc methods, neglecting a detailed comparison with other training-based OOD detection approaches. This limits the ability to fully gauge the relative advantages of TagOOD.

Modest Performance Gains: The improvements in performance metrics reported in the paper are not substantial enough to decisively establish the superiority of TagOOD. The performance gains are relatively modest and require further analysis to substantiate the claimed benefits.

Inadequate Ablation Studies: The ablation experiments provided in the paper are not comprehensive. There is a need for more granular analysis of the impact of individual components and parameters on the overall performance of TagOOD.

[1]: LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning, NeurIPS 2023
paper_summary: paper_summary: This paper proposes TagOOD method by employing the power of RAM to improve the OOD detection performance. TagOOD mainly decouples images from the corresponding tags generated by RAM, and generates object-level class centers within a common feature space of IND data. Based on the class centers, the test image can be recognized as either in-distribution or out-of-distribution data by computing the similarity between the prediction and class centers. The main contributions lie in object-level class center generation and object feature filtering. Experimental results show the effectiveness of TagOOD for OOD detection in multiple benchmark datasets.
strength_weakness: strength_weakness: 1. Clear motivation of TagOOD by integrating semantic information with visual features.

2. Well written and easy to follow the proposed method.

3. Comprehensive experiments to evaluate the effectiveness of the proposed TagOOD.
limitations: limitations: 1. The distinguishment ability between IND and OOD in TagOOD is from the strong tagging ability of RAM. What is the effect of replacing RAM with CLIP? In addition, I think the performance also can be improved if other method employs RAM.

2. Not SOTA performance, compared to other OOD detection methods, such as comparison with MCM in Tab.1 and ReAct in Tab. 2.
paper_summary: paper_summary: The paper introduces TagOOD, a novel approach to out-of-distribution (OOD) detection that leverages vision-language representations and class center learning. Unlike traditional methods that rely on whole-image features, TagOOD focuses on object-level features by using a tagging model to generate multiple semantic tags for each object within an image. It then trains a lightweight network to learn representative class centers that capture the central tendencies of in-distribution (IND) objects, minimizing the influence of irrelevant features. Finally, TagOOD efficiently detects OOD samples by calculating distance-based metrics between the learned class centers and test samples. The paper demonstrates TagOOD's superior performance over existing methods through extensive experiments on various benchmark datasets, showcasing its potential for robust and reliable AI systems.
strength_weakness: strength_weakness: + The authors conduct extensive experiments and ablation studies on the ImageNet benchmark, which is a widely recognized dataset for evaluating OOD detection methods.

+ TagOOD introduces a new perspective on OOD detection by leveraging vision-language representations.

+ The paper is well-structured.
limitations: limitations: - Missing some hyperparametric ablation experiments such as $α$,  $λ$, etc.