Dual-Level Collaborative Transformer for Image Captioning (2101.06462v2)

Abstract: Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novelDual-way Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i.e., 133.8% CIDEr-D on Karpathy split and 135.4% CIDEr on the official split. Code is available at https://github.com/luo3300612/image-captioning-DLCT.

Dual-Level Collaborative Transformer for Image Captioning

The paper introduces the Dual-Level Collaborative Transformer (DLCT), which addresses limitations in image captioning by integrating region and grid features. Region features, extracted by object detection networks like Faster R-CNN, have been pivotal in image captioning advancements. However, they often lack comprehensive contextual information and fine-grained details that grid features provide. The DLCT framework aims to unite these complementary features effectively.

Methodology

At the core of DLCT is the Dual-Way Self-Attention (DWSA) module, which focuses on capturing the intrinsic properties of both region and grid features. The Comprehensive Relation Attention (CRA) component within the DWSA module enhances the integration process by embedding both absolute and relative geometric information, thus enlisting the positional attributes crucial for detailed representation.

Following this, the paper introduces the Locality-Constrained Cross Attention (LCCA) module to mitigate semantic noise—a common issue arising from the direct fusion of disparate features. By constructing a geometric alignment graph, the LCCA ensures semantic alignment, facilitating precise interaction between region and grid features. This not only allows the transfer of higher-level object information from region features to grids but also contributes detailed contextual information back to regions.

Results

The DLCT was evaluated using the MS-COCO dataset, a standard benchmark in image captioning. The model achieved state-of-the-art results, attaining 133.8% CIDEr on the Karpathy split and 135.4% CIDEr on the official test set. These outcomes underscore the effectiveness of the proposed approach in harnessing the complementary strengths of region and grid features.

Implications and Future Directions

Practically, DLCT enhances image captioning outputs by improving the semantic richness and precision of the generated descriptions. The framework’s ability to blend object-level information with grid-level details could inspire applications beyond captioning, such as in video analysis and more complex vision-language tasks.

Theoretically, the integration of dual-level features through mechanisms like the CRA and LCCA extends our understanding of multi-source feature fusion. Future developments may explore using the DLCT framework in real-time systems or expanding the architecture to accommodate additional types of visual features. Furthermore, the approach could inform transformer-based methodologies in other domains where analogous fusion challenges arise.

Conclusion

The Dual-Level Collaborative Transformer represents a significant advancement in image captioning, addressing the critical challenge of effective feature integration. By successfully combining region and grid features while attentively managing semantic noise, DLCT sets a new benchmark in the field and opens avenues for future research and application.