Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data (1511.05284v2)

Abstract: While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentence captions to describe objects in context. In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating descriptions of novel objects which are not present in paired image-sentence datasets. Our method achieves this by leveraging large object recognition datasets and external text corpora and by transferring knowledge between semantically similar concepts. Current deep caption models can only describe objects contained in paired image-sentence corpora, despite the fact that they are pre-trained with large object recognition datasets, namely ImageNet. In contrast, our model can compose sentences that describe novel objects and their interactions with other objects. We demonstrate our model's ability to describe novel concepts by empirically evaluating its performance on MSCOCO and show qualitative results on ImageNet images of objects for which no paired image-caption data exist. Further, we extend our approach to generate descriptions of objects in video clips. Our results show that DCC has distinct advantages over existing image and video captioning approaches for generating descriptions of new objects in context.

Deep Compositional Captioning: Expanding the Horizons of Image and Video Description

The paper "Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data" introduces an innovative methodology aimed at overcoming the limitations of existing image captioning models that primarily rely on large paired image-sentence datasets. Most prevalent captioning models are adept at describing familiar objects seen in their training datasets but falter when tasked with novel objects absent in such paired data. This paper proposes the Deep Compositional Captioner (DCC), a model that leverages both large object recognition datasets and independent text corpora to describe new objects without being constrained by the availability of paired image-sentence data.

Methodology and Innovations

The DCC model integrates three core components: a deep lexical classifier for visual concept recognition, a LLM trained with unpaired text data to learn sentence structures, and a multimodal framework that synergizes these elements to generate coherent descriptions. By decoupling the tasks of visual concept detection and language structure learning, the DCC model introduces a mechanism where semantic transfer is realized between known objects and novel, unseen ones. This is especially beneficial in scenarios where paired datasets fail to encompass the vast diversity of the object recognition datasets like ImageNet.

A salient feature of this approach is its capability to construct sentences about novel objects by transferring knowledge from semantically similar known concepts. This capability is achieved through two distinct transfer learning mechanisms: direct transfer and delta transfer, both centered on leveraging word embeddings and relationships learned from extensive text corpora.

Numerical Results and Evaluation

The paper presents a comprehensive evaluation on the MSCOCO dataset, systematically excluding training data for certain object classes to test DCC's ability to describe new objects. The empirical results demonstrate a significant improvement in F1 scores, reflecting the model's adeptness at incorporating novel vocabulary into descriptions. Moreover, METEOR and BLEU-1 scores indicate enhanced sentence fluency and accuracy, even for objects the model had no paired training data for.

By conducting qualitative analyses on ImageNet and ILSVRC datasets, the paper showcases DCC's prowess in generating contextually relevant descriptions for objects never before encountered in the training phase. This ability stems from DCC's design which allows seamless integration and adaptation to unpaired data sources, highlighting its application potential in dynamic datasets where object categories frequently expand.

Implications and Future Directions

The implications of this research are profound, particularly in advancing the fields of computer vision and natural language processing. By demonstrating a practical method for breaking free from the constraints of paired datasets, DCC opens doors to more robust and flexible AI systems capable of operating in environments where data availability is heterogeneous. This approach can significantly benefit applications such as automated video description, assistive technologies, and real-time image interpretation in evolving contexts.

Future developments could focus on refining the semantic similarity measures and transfer mechanisms to further enhance the adaptability and savvy context understanding of such models. Additionally, extending this framework to handle more intricate sentence structures or adopting multi-lingual text corpora could yield even richer descriptions. As AI systems continue to penetrate diverse fields, methodologies like DCC will be crucial in ensuring they are both scalable and contextually aware.