PROB: Probabilistic Objectness for Open World Object Detection (2212.01424v1)

Abstract: Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection ($\sim 2\times$ unknown recall) and known object detection ($\sim 10\%$ mAP). Our code will be made available upon publication at https://github.com/orrzohar/PROB.

• The paper introduces a novel probabilistic framework that improves objectness estimation for detecting both known and unknown objects in open world settings.
• It employs an alternative optimization process that alternates between estimating probability distributions and maximizing objectness likelihood within a transformer-based detector.
• Experiments demonstrate significant gains with a 100-300% boost in unknown recall and roughly a 10% improvement in mAP for known objects.

An Examination of PROB: Probabilistic Objectness for Open World Object Detection

The paper "PROB: Probabilistic Objectness for Open World Object Detection" presents an innovative approach to addressing the challenges inherent in Open World Object Detection (OWOD). Traditional Object Detection (OD) methods often fail in an open-world setting due to their inability to detect unknown objects, typically classifying novel inputs as background. This paper proposes a probabilistic method to enhance objectness estimation, thereby improving the detection of both known and unknown objects.

OWOD tasks require algorithms that not only recognize labeled, known objects but also identify and learn from novel, unseen classes. Current methods attempt to tackle this through pseudo-labeling strategies; however, these suffer from low efficacy in unknown object recall. The authors introduce a novel framework leveraging probabilistic models, specifically focusing on an alternate optimization process involving probability distribution estimation and objectness likelihood maximization within an embedded feature space.

The proposed model, named PROB, integrates this probabilistic framework into transformer-based object detectors, namely a modified deformable DETR model. PROB incorporates a probabilistic objectness prediction head that distinguishes objects from background without requiring explicit negative samples, thus mitigating the confusion between unknown objects and background. During training, the framework alternates between estimating distribution parameters and maximizing the likelihood of matched query embeddings, effectively tuning the model to better handle both known and unknown detections.

PROB demonstrates state-of-the-art performance on OWOD benchmarks, with significant improvements in both unknown object recall and mean average precision (mAP) for known objects. Particularly, the model shows a relative gain in unknown recall by 100-300% compared to prior methods and a ∼10% enhancement in generating known objects' mAP. These results underscore PROB's efficacy in addressing key OWOD challenges, including maintaining performance on known classes while expanding capabilities to unknown objects.

From a theoretical standpoint, this approach provides an intriguing extension of probabilistic and generative modeling techniques into the field of open-world applications. By decoupling object presence and class probability, the authors simplify the detection task and allow for a more fluid adaptation to new classes over time. Moreover, the probabilistic framework sets a precedent for future works aiming to further improve the unknown object detection via more sophisticated probabilistic models.

Implications of this research are broad and significant, impacting domains such as autonomous systems, where an ability to respond to unknown environmental factors is crucial. Additionally, the paper opens avenues for future exploration into more robust generative models that can deal with ambiguities inherent in real-world object detection tasks.

In conclusion, the integration of probabilistic objectness in OWOD represents a major stride forward in object detection research, addressing existing shortfalls in unknown object detection and suggesting potential pathways for expanding machine learning applications in dynamic, real-world settings. Future work could build upon this foundation, exploring deeper probabilistic frameworks or combining them with other modeling techniques to further elevate OWOD performance and versatility.