arXiv:2411.13578v1 Announce Type: new Abstract: How can models effectively detect out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining? Existing OOD detection methods struggle to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings, often requiring large amounts of training data and failing to generalize to unseen label combinations. While large language models have revolutionized zero-shot OOD detection, they primarily focus on single-label scenarios, leaving a critical gap in handling real-world tasks where samples can be associated with multiple interdependent labels. To address these challenges, we introduce COOD, a novel zero-shot multi-label OOD detection framework. COOD leverages pre-trained vision-language models, enhancing them with a concept-based label expansion strategy and a new scoring function. By enriching the semantic space with both positive and negative concepts for each label, our approach models complex label dependencies, precisely differentiating OOD samples without the need for additional training. Extensive experiments demonstrate that our method significantly outperforms existing approaches, achieving approximately 95% average AUROC on both VOC and COCO datasets, while maintaining robust performance across varying numbers of labels and different types of OOD samples.
The article “COOD: Zero-Shot Multi-Label Out-of-Distribution Detection with Concept-Based Label Expansion” addresses the challenge of effectively detecting out-of-distribution (OOD) samples in complex, multi-label settings without extensive retraining. Existing methods struggle to capture the intricate semantic relationships and label co-occurrences in such settings, making it difficult to generalize to unseen label combinations. While large language models have made strides in zero-shot OOD detection, they primarily focus on single-label scenarios, leaving a critical gap in real-world tasks where samples can be associated with multiple interdependent labels. To bridge this gap, the authors introduce COOD, a novel zero-shot multi-label OOD detection framework. COOD leverages pre-trained vision-language models, enhancing them with a concept-based label expansion strategy and a new scoring function. By enriching the semantic space with both positive and negative concepts for each label, COOD effectively models complex label dependencies and accurately differentiates OOD samples without the need for additional training. Extensive experiments demonstrate that COOD outperforms existing approaches, achieving approximately 95% average AUROC on both VOC and COCO datasets, while maintaining robust performance across varying numbers of labels and different types of OOD samples.
Introducing COOD: A Novel Approach to Zero-Shot Multi-Label Out-of-Distribution Detection
Out-of-distribution (OOD) detection is a critical task in machine learning, as it helps models identify samples that are significantly different from the training data. Existing OOD detection methods struggle in the complex, multi-label settings, where samples can be associated with multiple interdependent labels. This often leads to poor generalization and the need for extensive retraining. In this article, we introduce COOD, a novel zero-shot multi-label OOD detection framework that overcomes these challenges.
The Challenge of Multi-Label OOD Detection
Multi-label classification tasks involve assigning multiple labels to a single sample. However, existing OOD detection methods primarily focus on single-label scenarios, lacking the ability to capture the intricate semantic relationships and label co-occurrences inherent in multi-label settings. Consequently, these methods struggle when faced with unseen label combinations and require large amounts of training data for effective detection.
The Power of Language Models in Zero-Shot OOD Detection
Language models have revolutionized zero-shot OOD detection, allowing models to detect out-of-distribution samples without explicitly training on them. These models are trained on a massive corpus of text data and learn to generate coherent and semantically meaningful responses. While they have been successful in handling single-label scenarios, they fall short in the complex world of multi-label classification.
The COOD Framework: A Breakthrough in Multi-Label OOD Detection
COOD addresses the limitations of existing OOD detection methods by leveraging pre-trained vision-language models. Our framework enhances these models with a concept-based label expansion strategy and a new scoring function. By enriching the semantic space with both positive and negative concepts for each label, COOD models complex label dependencies and precisely differentiates OOD samples without the need for additional training.
Concept-based Label Expansion
In COOD, we expand the label space by introducing positive and negative concepts for each label. By including concepts that are related and unrelated to the label, we provide the model with additional information to capture interdependencies between labels. This enrichment of the semantic space enables more accurate detection of OOD samples.
A New Scoring Function
To effectively differentiate OOD samples from in-distribution samples, COOD introduces a new scoring function. This function takes into account the presence or absence of relevant positive and negative concepts for each label in a sample. By comparing the scores for different labels, COOD accurately identifies OOD samples based on the absence of relevant positive concepts or the presence of relevant negative concepts.
Promising Results and Robust Performance
We conducted extensive experiments to evaluate the performance of COOD. We used two popular multi-label datasets, VOC and COCO, and achieved approximately 95% average AUROC on both datasets. Furthermore, COOD demonstrated robust performance across varying numbers of labels and different types of OOD samples. These results highlight the effectiveness and versatility of COOD in multi-label OOD detection.
Conclusion
The introduction of COOD, a novel zero-shot multi-label OOD detection framework, brings significant advancements to the field of machine learning. By leveraging pre-trained vision-language models, enriching the semantic space with concept-based label expansion, and introducing a new scoring function, COOD successfully addresses the challenges of capturing intricate label dependencies and generalizing to unseen label combinations. The promising results and robust performance of COOD demonstrate its potential in real-world tasks requiring multi-label OOD detection.
The paper introduces a novel framework called COOD, which addresses the challenge of effectively detecting out-of-distribution (OOD) samples in complex, multi-label settings without the need for extensive retraining. The existing OOD detection methods have struggled with capturing the intricate semantic relationships and label co-occurrences present in multi-label settings. Furthermore, they often require large amounts of training data and fail to generalize to unseen label combinations.
The authors highlight that while large language models have made significant advancements in zero-shot OOD detection, they primarily focus on single-label scenarios, leaving a critical gap in handling real-world tasks where samples can be associated with multiple interdependent labels. To bridge this gap, COOD leverages pre-trained vision-language models and enhances them with a concept-based label expansion strategy and a new scoring function.
One of the key contributions of COOD is its ability to enrich the semantic space with both positive and negative concepts for each label. This enrichment allows for modeling complex label dependencies, thereby enabling the precise differentiation of OOD samples without the need for additional training. By incorporating positive and negative concepts, COOD can effectively capture the semantic relationships and label co-occurrences that are crucial in multi-label settings.
The paper presents extensive experiments to validate the effectiveness of COOD. The results demonstrate that COOD outperforms existing approaches, achieving an average AUROC of approximately 95% on both the VOC and COCO datasets. It is worth noting that COOD maintains robust performance across varying numbers of labels and different types of OOD samples.
Overall, COOD presents a promising solution to the challenge of OOD detection in complex, multi-label settings. By leveraging pre-trained vision-language models and incorporating positive and negative concepts, COOD effectively captures label dependencies and achieves superior performance compared to existing methods. This framework has the potential to enhance the accuracy and reliability of OOD detection in real-world tasks where multiple interdependent labels are involved.
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