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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

Published 16 May 2024 in cs.CV | (2405.10053v1)

Abstract: Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class; ii) it integrates these categories into hierarchy-aware sentences; iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by LLMs. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

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References (73)
  1. Three ways to Improve Feature Alignment for Open Vocabulary Detection. arXiv:2303.13518, 2023.
  2. Hierarchy-based Image Embeddings for Semantic Image Retrieval. In WACV, 2019.
  3. Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks. In CVPR, 2020.
  4. Language Models are Few-Shot Learners. In NeurIPS, 2020.
  5. End-to-end object detection with transformers. In ECCV, 2020.
  6. On Label Granularity and Object Localization. In ECCV, 2022.
  7. ImageNet: a Large-Scale Hierarchical Image Database. In CVPR, 2009.
  8. What Does Classifying more than 10,000 Image Categories Tell Us? In ECCV, 2010.
  9. Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model. In CVPR, 2022.
  10. Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector. In CVPR, 2020.
  11. Christiane Fellbaum. WordNet: an Electronic Lexical Database. MIT Press, 1998.
  12. PromptDet: Towards Open-vocabulary Detection using Uncurated Images. In ECCV, 2022.
  13. DeViSE: A Deep Visual-Semantic Embedding Model. In NeurIPS, 2013.
  14. Improving Zero-shot Generalization and Robustness of Multi-modal Models. In CVPR, 2023.
  15. Principal Component Analysis: A Natural Approach to Data Exploration. ACM Computing Surveys (CSUR), 54(4):1–34, 2021.
  16. Joshua Goodman. Classes for Fast Maximum Entropy Training. In ICASSP, 2001.
  17. A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. arXiv:2307.12980, 2023.
  18. Open-vocabulary Object Detection via Vision and Language Knowledge Distillation. In ICLR, 2022.
  19. LVIS: A Dataset for Large Vocabulary Instance Segmentation. In CVPR, 2019.
  20. Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays. In MICCAI, 2023.
  21. Deep Residual Learning for Image Recognition. In CVPR, 2016.
  22. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021.
  23. Multi-Modal Classifiers for Open-Vocabulary Object Detection. In ICML, 2023.
  24. MaPLe: Multi-modal Prompt Learning. In CVPR, 2023.
  25. John C. Knight. Safety Critical Systems: Challenges and Directions. In ICSE, 2002.
  26. F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models. In ICLR, 2023.
  27. The Open Images Dataset V4. IJCV, 128:1956–1981, 2020.
  28. Classification of Text Documents. The Computer Journal, 41(8):537–546, 1998.
  29. Interpreting Word Embeddings with Eigenvector Analysis. In ICLR, 2023a.
  30. Microsoft COCO: Common Objects in Context. In ECCV, 2014.
  31. MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge. In ICCV, 2023b.
  32. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In ICCV, 2021.
  33. CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection. In NeurIPS, 2023.
  34. Autonomous Vehicles: Theoretical and Practical Challenges. Transportation Research Procedia, 33:275–282, 2018.
  35. Visual Classification via Description from Large Language Models. In ICLR, 2023.
  36. Scaling Open-Vocabulary Object Detection. In NeurIPS, 2023.
  37. Hierarchical Probabilistic Neural Network Language Model. In International Workshop on Artificial Intelligence and Statistics, 2005.
  38. CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. In ICML, 2023.
  39. OpenAI. ChatGPT: A Large-Scale GPT-3.5-Based Model. https://openai.com/blog/chatgpt, 2022.
  40. Prompting Scientific Names for Zero-Shot Species Recognition. In EMNLP, 2023.
  41. What Does a Platypus Look Like? Generating Customized Prompts for Zero-shot Image Classification. In ICCV, 2023.
  42. Learning Transferable Visual Models From Natural Language Supervision. In ICML, 2021.
  43. Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. In NeurIPS, 2023.
  44. ImageNet-21K Pretraining for the Masses. In NeurIPS, 2021.
  45. Waffling around for Performance: Visual Classification with Random Words and Broad Concepts. In ICCV, 2023.
  46. A Higher Level Classification of All Living Organisms. PLOS ONE, 10(4):e0119248, 2015.
  47. Hierarchical Text Categorization Using Neural Networks. Information retrieval, 5(1):87–118, 2002.
  48. BREEDS: Benchmarks for Subpopulation Shift. In ICLR, 2021.
  49. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning. In ACL, 2018.
  50. Interpreting Word Embeddings with Eigenvector Analysis. In NeurIPS, IRASL workshop, 2018.
  51. Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images. Remote Sensing, 12(17):2734, 2020.
  52. Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. In NeurIPS, 2022.
  53. A Survey of Hierarchical Classification across Different Application Domains. Data Mining and Knowledge Discovery, 22:31–72, 2011.
  54. A Survey of Zero Shot Detection: Methods and Applications. Cognitive Robotics, 1:159–167, 2021.
  55. The iNaturalist Species Classification and Detection Dataset. In CVPR, 2018.
  56. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR-2011-001, California Institute of Technology, 2011.
  57. TransHP: Image Classification with Hierarchical Prompting. In NeurIPS, 2023.
  58. Learning Classifiers Using Hierarchically Structured Class Taxonomies. In SARA, 2005.
  59. Towards Open Vocabulary Learning: A Survey. IEEE TPAMI, 2024.
  60. Aligning Bag of Regions for Open-Vocabulary Object Detection. In CVPR, 2023a.
  61. CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching. In CVPR, 2023b.
  62. Learning Concise and Descriptive Attributes for Visual Recognition. In ICCV, 2023.
  63. DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection. In NeurIPS, 2022.
  64. Open-Vocabulary DETR with Conditional Matching. In ECCV, 2022.
  65. Open-Vocabulary Object Detection Using Captions. In CVPR, 2021.
  66. Multi-event Video-Text Retrieval. In ICCV, 2023a.
  67. A Simple Framework for Open-Vocabulary Segmentation and Detection. In ICCV, 2023b.
  68. RegionCLIP: Region-based Language-Image Pretraining. In CVPR, 2022.
  69. Conditional Prompt Learning for Vision-Language Models. In CVPR, 2022a.
  70. Learning to Prompt for Vision-Language Models. IJCV, 130(7):2337–2348, 2022b.
  71. Probabilistic two-stage Detection. arXiv:2103.07461, 2021.
  72. Detecting Twenty-thousand Classes using Image-level Supervision. In ECCV, 2022c.
  73. A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future. arXiv:2307.09220, 2023.
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Summary

  • The paper introduces a hierarchical approach for open-vocabulary object detection by incorporating semantic relationships into the classifier framework.
  • It demonstrates significant gains, boosting mAP50 by up to 31.9% with ground-truth hierarchies and improving CLIP zero-shot accuracy by 2.8% on ImageNet-1k.
  • The method operates offline without adding inference overhead, enhancing detection consistency for real-world applications in fields like autonomous driving and medical imaging.

How Semantic Hierarchies Enhance Open-Vocabulary Object Detection

Introduction

Ever heard of open-vocabulary object detection (OvOD)? It's where object detection meets language, allowing models to identify objects that weren't part of their original training set. Think of it like a universal translator for object names—super useful, right? But here's the catch: OvOD systems can be pretty inconsistent when dealing with different levels of vocabulary granularity. For example, recognizing a "Labrador" versus just recognizing it as a "Dog" can throw these systems for a loop. That's where the paper "Semantic Hierarchy Nexus (SHiNe)" steps in, aiming to iron out these inconsistencies by tapping into semantic hierarchies. Let's break down how it works, why it works, and what it means for the future of AI.

The Problem with Existing OvOD Systems

Current OvOD systems are undeniably cool; they use LLMs and vision-LLMs to do some genuinely impressive stuff. However, they're not perfect. Their performance tends to fluctuate when faced with varying semantic granularities.

Imagine needing a system to detect whether an image contains a "Dog," a "Labrador," or an "Animal." Ideally, the classifier should perform consistently no matter how specific or general the target class is. Unfortunately, this isn't always the case. Most OvOD systems struggle because they fail to account for the inherent relationships within vocabularies. This is especially problematic for real-world applications, such as autonomous driving or medical diagnosis, where consistency isn't just a "nice-to-have" but a necessity.

Enter SHiNe: A Hierarchical Approach

SHiNe (Semantic Hierarchy Nexus) introduces a hierarchical approach to OvOD. The core idea is simple yet effective: use hierarchical relationships between classes to guide the detection process. Here's a step-by-step breakdown of how SHiNe does it:

  1. Retrieve Relevant Categories: For each class of interest (CoI), SHiNe first retrieves its related super- and sub-categories from a semantic hierarchy.
  2. Integrate Categories into Sentences: These categories are then integrated into hierarchy-aware sentences. For example, rather than just saying "Labrador," it would say, "A Labrador is a dog, which is an animal."
  3. Generate a Nexus Classifier Vector: These sentences are encoded into vector embeddings, which are then aggregated to form what's called a "nexus classifier vector."

This process leverages semantic knowledge to create more robust and context-aware classifiers, and it's all done "offline," meaning it doesn't add any computational overhead during inference. Cool, right?

Strong Numerical Results

The paper showcases some pretty impressive numbers to back up SHiNe's efficacy:

  • On various benchmarks, SHiNe boosted mean Average Precision at an IoU threshold of 50% (mAP50) by up to 31.9% when using ground truth hierarchies.
  • Even when using hierarchies generated by LLMs (which aren't perfect), SHiNe still showed substantial improvements.

What's even more noteworthy? SHiNe also demonstrated robust performance in open-vocabulary classification tasks. On ImageNet-1k, it improved the CLIP zero-shot baseline by 2.8% in accuracy. That's a significant bump in a field where even small improvements are celebrated.

Practical and Theoretical Implications

So, what does this mean for the future of AI and object detection?

From a practical standpoint, SHiNe's approach can make OvOD systems much more reliable in real-world applications. Imagine safer self-driving cars that can recognize objects more consistently, or more accurate medical imaging systems that can differentiate between various conditions more reliably.

Theoretically, SHiNe opens the door for further research into how semantic relationships can be leveraged in AI. It's a reminder that sometimes, the best way to teach a machine is to help it understand how things are related, not just what they are.

Speculations on Future Developments

Looking forward, the principles behind SHiNe could be expanded into other realms of AI. Here are some speculative but exciting possibilities:

  • Enhanced NLP Systems: Imagine chatbots that understand more nuanced phrases by considering hierarchical relationships between concepts.
  • Better Generalization in AI: By incorporating semantic hierarchies, models could generalize better across domains, making them more versatile and useful in varied applications.
  • Improved Training Efficiency: Semantic hierarchies could help in reducing the amount of data needed to train effective models, making AI development faster and more cost-efficient.

Conclusion

In essence, SHiNe takes a significant step toward making OvOD systems more robust and reliable. By leveraging the inherent semantic relationships within vocabularies, it not only improves performance but also brings consistency to how these systems handle varying levels of granularity. It's an exciting development that leaves us with plenty to look forward to in the ever-evolving field of AI.

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