DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection (2406.14924v1)

Abstract: Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discovery methods that leverage basic visual cues to identify salient objects, consistently achieving a high recall rate remains difficult due to the diversity of object types and their contextual complexity. In this work, we investigate using vision-LLMs (VLMs) to enhance object detection via a self-supervised prompt learning strategy. Our initial findings indicate that manually crafted text queries often result in undetected objects, primarily because detection confidence diminishes when the query words exhibit semantic overlap. To address this, we propose a Dispersing Prompt Expansion (DiPEx) approach. DiPEx progressively learns to expand a set of distinct, non-overlapping hyperspherical prompts to enhance recall rates, thereby improving performance in downstream tasks such as out-of-distribution OD. Specifically, DiPEx initiates the process by self-training generic parent prompts and selecting the one with the highest semantic uncertainty for further expansion. The resulting child prompts are expected to inherit semantics from their parent prompts while capturing more fine-grained semantics. We apply dispersion losses to ensure high inter-class discrepancy among child prompts while preserving semantic consistency between parent-child prompt pairs. To prevent excessive growth of the prompt sets, we utilize the maximum angular coverage (MAC) of the semantic space as a criterion for early termination. We demonstrate the effectiveness of DiPEx through extensive class-agnostic OD and OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by up to 20.1% in AR and achieving a 21.3% AP improvement over SAM. The code is available at https://github.com/jason-lim26/DiPEx.

DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection

The paper "DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection" introduces a novel methodology aimed at enhancing the performance of vision-LLMs (VLMs) in the tasks of class-agnostic object detection (OD) and out-of-distribution object detection (OOD-OD). In essence, it addresses a persistent challenge in computer vision: achieving high recall rates in identifying diverse object types without predefined class labels.

The researchers target the limitations of current OD methods, which fail to consistently achieve high recall rates due to the complexity and variety of object appearances and contexts. Despite the advancements made by bottom-up and multi-object discovery methods, these approaches often struggle due to their reliance on basic visual cues, which constrains their scalability and precision.

Key Contributions

Self-Supervised Prompt Learning Strategy

The core innovation of DiPEx lies in using VLMs to improve object detection via a self-supervised prompt learning strategy. The paper critiques the conventional practice of manually crafting text queries, which often results in undetected objects due to semantic overlaps between queries. To circumvent this, the authors propose a method for progressively learning non-overlapping, hyperspherical prompts that aim to maximize recall rates by extending the semantic coverage of detection prompts.

Dispersing Prompt Expansion (DiPEx)

DiPEx stands out in its approach to learning a set of distinct, non-overlapping prompts. The methodology involves:

1. Initialization: Starting with a generic parent prompt.
2. Expansion: Identifying parent prompts with high semantic uncertainty and expanding them into finer, non-overlapping child prompts.
3. Optimization: Using dispersion losses to maintain high inter-class discrepancy while preserving semantic consistency.
4. Termination Criterion: Employing maximum angular coverage (MAC) to prevent unnecessary prompt expansion and balance computational overhead.

Empirical Validation

The effectiveness of DiPEx is empirically validated through extensive experiments on benchmark datasets like MS-COCO and LVIS. The method showcases superior performance over existing approaches, achieving improvements of up to 20.1% in average recall (AR) and 21.3% in average precision (AP) compared to the segment anything model (SAM). These results are particularly notable in enhancing recall for small objects, an area traditionally fraught with challenges.

Experimental Insights

Class-Agnostic OD

Evaluations on MS-COCO and LVIS datasets reveal that DiPEx outperforms traditional methods and even state-of-the-art prompting methods:

• MS-COCO: DiPEx achieves the highest performance across all evaluated metrics, demonstrating significant improvements in detecting small objects and providing a robust generalization capacity for diverse object types.
• LVIS: DiPEx outperforms SAM by 13.3% in AR and 21.3% in AP after only four epochs of self-training, highlighting its efficacy in environments with a long-tailed class distribution.

Downstream OOD-OD

In downstream OOD-OD tasks, DiPEx demonstrates a significant improvement by 38.3% in AR over baseline methods, showcasing its ability to generalize well in scenarios that include both known and unknown objects.

Theoretical and Practical Implications

Theoretical Contributions

DiPEx introduces a new dimension to prompt tuning for VLMs in OD tasks. By leveraging non-overlapping, hyperspherical prompts, the methodology not only enhances recall and precision but also establishes a framework for understanding the relationships between prompt semantics and detection performance.

Practical Applications

Practically, DiPEx can be highly beneficial for applications requiring dynamic and robust object detection capabilities, such as autonomous driving, surveillance systems, and robotic vision. The ability to detect a wide array of objects without exhaustive class-specific training makes DiPEx a promising tool for real-world applications.

Future Directions

• Hierarchical Prompt Learning: Future work could explore end-to-end training strategies for learning hierarchical prompts in a single pass, potentially reducing computational costs while maintaining or even improving performance.
• Broader Evaluation: Extending benchmarks to include more varied downstream tasks, such as open-vocabulary and open-world detection, would further validate the versatility of DiPEx.

Conclusion

The paper presents a compelling advancement in the field of class-agnostic OD. DiPEx's unique approach to self-supervised prompt expansion addresses longstanding challenges in the field, offering robust performance improvements and laying the groundwork for future innovations in AI-driven object detection. The balance it strikes between comprehensive semantic coverage and computational efficiency marks a significant step forward in the applicability of VLMs for complex OD tasks.