Exploiting Unlabeled Data with Vision and Language Models for Object Detection (2207.08954v1)

Abstract: Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations for thousands of categories at a large scale. We propose a novel method that leverages the rich semantics available in recent vision and LLMs to localize and classify objects in unlabeled images, effectively generating pseudo labels for object detection. Starting with a generic and class-agnostic region proposal mechanism, we use vision and LLMs to categorize each region of an image into any object category that is required for downstream tasks. We demonstrate the value of the generated pseudo labels in two specific tasks, open-vocabulary detection, where a model needs to generalize to unseen object categories, and semi-supervised object detection, where additional unlabeled images can be used to improve the model. Our empirical evaluation shows the effectiveness of the pseudo labels in both tasks, where we outperform competitive baselines and achieve a novel state-of-the-art for open-vocabulary object detection. Our code is available at https://github.com/xiaofeng94/VL-PLM.

Insights on Exploiting Unlabeled Data with Vision and LLMs for Object Detection

The paper "Exploiting Unlabeled Data with Vision and LLMs for Object Detection" introduces an innovative approach that effectively leverages the semantic capacity of vision and language (V{content}L) models to improve object detection using unlabeled data. The research primarily focuses on generating pseudo labels to benefit tasks like open-vocabulary detection (OVD) and semi-supervised object detection (SSOD).

The motivation for this work stems from the costly nature of acquiring annotations for large-scale datasets in object detection. The authors recognize that although human-annotated datasets are extensive, the distribution of object categories is naturally long-tailed, making the collection of exhaustive annotations challenging. They propose using recent developments in V{content}L models to automatically generate pseudo labels for unlabeled images, a method that introduces cost-efficiency in tapping into large, unlabeled datasets.

Methodological Approach

The core approach involves utilizing a class-agnostic region proposal mechanism alongside V{content}L models such as CLIP to categorize and localize objects in unlabeled images. This process generates pseudo labels with high semantic relevance, which are then used to train object detection models for both OVD and SSOD tasks. In OVD, the detection model must generalize to unseen object categories, while in SSOD, unlabeled data aids in refining the detection capabilities for known objects.

The authors enhance pseudo-label accuracy by integrating region proposal network (RPN) scores with V{content}L model scores to filter regions with high confidence. They further refine localization by repeatedly applying a region of interest (RoI) head to the proposals. The effectiveness of this method is underscored by empirical evaluations showcasing its superiority over contemporary baselines across both open-vocabulary and semi-supervised detection tasks.

Numerical and Empirical Results

The numerical results in the paper emphasize the effectiveness of the proposed method. Specifically, in the open-vocabulary detection task on the COCO dataset, the pseudo-label guided detection framework achieves a notable gain, surpassing prior state-of-the-art methods, such as ViLD, by +6.8 AP in novel category detection. For semi-supervised tasks, the framework utilizing these pseudo labels shows marked improvements over baselines like STAC by replacing pseudo labels.

Implications and Future Directions

The implications of this work are twofold. Practically, it provides a cost-effective means to harness large-scale unlabeled datasets, reducing the dependency on annotations while achieving competitive performance in object detection. Theoretically, it positions V{content}L models as pivotal tools that can bridge the gap between visual and linguistic data domains, offering paths to richer semantic understanding in machine learning models.

The research opens avenues for the adoption of more advanced V{content}L models such as ALIGN, which could potentially improve pseudo label quality further. Moreover, this approach could be extended to other dense prediction tasks, such as zero-shot semantic segmentation, which would be an exciting development for future research efforts.

In summary, this paper presents a critical advancement in object detection by cleverly integrating V{content}L models to efficiently exploit unlabeled data, setting a foundation for future research and application along these lines.