Learning Object-Language Alignments for Open-Vocabulary Object Detection (2211.14843v1)

Abstract: Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet.

Learning Object-Language Alignments for Open-Vocabulary Object Detection

The paper "Learning Object-Language Alignments for Open-Vocabulary Object Detection" introduces an innovative approach to the field of open-vocabulary object detection (OVOD), addressing the limitations of existing object detection frameworks that rely heavily on predefined vocabulary and costly annotated data. The authors propose a novel method that directly learns from image-text pairs, thereby facilitating zero-shot recognition of novel categories. This paper outlines a framework for learning fine-grained object-language alignments by formulating the task as a bipartite matching problem, achieved through set matching between image region features and word embeddings.

Summary and Methodology

The core idea is to leverage the widely available image-text pair data to extend object detectors' vocabulary beyond predefined categories in datasets like COCO and LVIS. Unlike typical object detection methods that demand extensive annotations, this paper circumvents such requirements by using natural language as supervision, offering a more scalable and cost-effective method.

The authors introduce their framework, VLDet, which employs a two-stage detection model. It adapts the conventional object detector, Faster R-CNN, replacing the classification head with word embeddings generated via a pre-trained LLM, CLIP. The approach innovatively aligns regions and words from unannotated image-text pairs by framing it as a set matching problem. Through the use of the Hungarian algorithm, an optimal bipartite matching is achieved, assigning each image region a corresponding word, further enabling the model to learn object-language alignments directly.

This document's substantial numerical results illustrate the efficacy of the proposed method. Significant performance improvements on benchmark datasets such as COCO and LVIS are reported, achieving 32.0% mean Average Precision (mAP) on COCO and 21.7% mask mAP on LVIS. These results surpass the existing state-of-the-art methods like PB-OVD, demonstrating the framework's efficacy in recognizing novel categories.

Implications and Future Directions

From a practical perspective, this approach greatly reduces the dependency on laborious bounding box annotations, allowing for a more inclusive and expansive object detection system capable of handling a diverse range of objects not present in the initial training data. Theoretically, it lays a foundation for further exploration into multimodal learning, specifically how language can be effectively integrated with vision to enhance recognition capabilities in machine learning models.

Future research in this domain could focus on tackling the inherent biases present in vision-and-language datasets, extending this work to even larger datasets beyond those like Conceptual Captions 3M, and refining the alignment processes to handle complex multi-word expressions or imperfect captions. Additionally, investigating how noise within image-text data pairs affects learning and strategies to mitigate it could further enhance the robustness of such models.

This paper makes a significant contribution by demonstrating a feasible path towards generalizing object detection systems using natural language, indicating a promising direction for future advancements within open-vocabulary recognition tasks.