Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts (2405.14874v4)

Abstract: The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-LLMs (VLMs) have recently achieved groundbreaking results. VLM-based open-vocabulary object detection extends the capabilities of traditional object detection frameworks, enabling the recognition and classification of objects beyond predefined categories. Investigating OOD robustness in recent open-vocabulary object detection is essential to increase the trustworthiness of these models. This study presents a comprehensive robustness evaluation of the zero-shot capabilities of three recent open-vocabulary (OV) foundation object detection models: OWL-ViT, YOLO World, and Grounding DINO. Experiments carried out on the robustness benchmarks COCO-O, COCO-DC, and COCO-C encompassing distribution shifts due to information loss, corruption, adversarial attacks, and geometrical deformation, highlighting the challenges of the model's robustness to foster the research for achieving robustness. Project page: https://prakashchhipa.github.io/projects/ovod_robustness

• The paper demonstrates significant performance drops in OV detectors when exposed to various distribution shifts.
• It employs zero-shot evaluations on COCO-O, COCO-DC, and COCO-C benchmarks to compare OWL-ViT, YOLO World, and Grounding DINO's robustness.
• The paper emphasizes the need for more resilient training strategies to enhance detection performance in real-world out-of-distribution scenarios.

Overview of "Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts"

This paper presents a critical evaluation of the robustness of open-vocabulary (OV) object detection models under distribution shifts. In the context of computer vision, OV object detectors aim to identify objects not confined to a predefined set of categories, leveraging the advancements made by vision-LLMs (VLMs). The paper focuses on three notable OV models: OWL-ViT, YOLO World, and Grounding DINO, assessing their resilience in scenarios that extend beyond their original training distributions. It seeks to underscore the importance of robustness, especially as these models are poised to transition from experimental to practical deployment.

Methodology

The paper evaluates the robustness of the selected OV models using zero-shot capabilities across three benchmarks: COCO-O, COCO-DC, and COCO-C, which include various distribution shifts such as corruption, adversarial attacks, and geometrical deformations. These benchmarks test the models' abilities to generalize and maintain performance under unexpected conditions. The metrics evaluated include Average Precision (AP) and mean Average Precision (mAP) across different Intersection over Union (IoU) thresholds, with particular attention paid to the effective robustness measure which provides insights into how well models maintain performance relative to their in-distribution results.

Key Findings

The evaluation reveals several critical insights:

1. Performance Degradation: All models showed significant performance degradation when applied to images with altered object representations or corrupted data. For instance, on COCO-O, despite Grounding DINO's more favorable robustness, the deviations highlight inherent challenges across OV models.
2. Model Comparisons: Despite high performance on the original COCO dataset, each model suffers from a decline in mAP on OOD datasets. Grounding DINO exhibits superior robustness, maintaining better performance amid corruption and adversarial settings compared to OWL-ViT and YOLO World.
3. Challenges with Severity: Increased levels of noise, blur, and other distortions (from COCO-C) progressively hinder each model's prediction capabilities, emphasizing the need for more resilient learning strategies.

Implications

This research has significant implications for both theoretical understanding and practical applications in AI:

• Theoretical Implications: The analysis highlights the fundamental challenges facing OV object detectors amidst distribution shifts. It invites further theoretical exploration into how robustness can be ingrained more deeply within the learning paradigms of OV models.
• Practical Implications: Practically, the insights from this paper drive the need for OV detectors to evolve further to handle OOD scenarios more effectively. This capability is indispensable for real-world applications where data does not always conform to neatly categorized training sets.

Future Directions

Future developments might focus on enhancing the robustness of OV object detectors through:

• Improved integration of VLM strategies with zero-shot learning techniques
• Robust training methodologies that utilize diversified and augmented datasets
• Algorithmic innovations that bolster models against adversarial conditions without compromising on inference efficiency

Overall, the paper presents a comprehensive examination of the robustness landscape for open-vocabulary object detectors. It acts as a motivator for ongoing research aimed at developing AI systems that can robustly interpret and respond to the complexities of the visual world, fostering greater trust and applicability across diverse sectors.