Object-aware Video-language Pre-training for Retrieval (2112.00656v6)

Abstract: Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations. The key idea is to leverage the bounding boxes and object tags to guide the training process. We evaluate our model on three standard sub-tasks of video-text matching on four widely used benchmarks. We also provide deep analysis and detailed ablation about the proposed method. We show clear improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a video-language architecture. The code will be released at \url{https://github.com/FingerRec/OA-Transformer}.

Object-aware Video-language Pre-training for Retrieval

This paper presents a novel approach to video-language pre-training, focusing on improving semantic alignment in video-text retrieval applications. The research introduces the Object-aware Transformer (OA-Trans), which extends traditional video-language transformers by integrating object-centric representations. The motivation is to address the limitations of existing models that neglect fine-grained semantic alignment, hindering further improvements in retrieval tasks.

Contributions and Methodology

The primary contributions of the paper can be summarized as follows:

• Object-aware Transformer (OA-Trans): The paper presents a dual-encoder model enhanced by object representations. By incorporating object tags and bounding box information, the OA-Trans encourages the model to focus on salient video regions and specific semantic entities within the video-text data.
• Efficient Object Integration: The OA-Trans adopts a single anchor frame, which captures essential object information. This approach balances the computational cost and matching recall by concentrating object extraction on a single frame rather than the entire video. The method leverages object-guided masking to improve fine-grained interaction between video and text embeddings.
• Object-aware Contrastive (OAC) Loss: The OAC loss is a novel approach to utilizing object information during training. This loss contrasts object tags with video frames and aligns masked anchor images with text descriptions, reinforcing semantic alignment without increasing computational overhead during inference.
• Enhanced Retrieval Performance: By integrating object-centric information, the OA-Trans demonstrates significant improvements in retrieval tasks across multiple benchmark datasets. For instance, on the MSVD dataset, the approach achieved an improvement in Recall@1 from 46.2% to 51.4%, showcasing the efficacy of object-aware representations.

Results and Implications

The proposed Object-aware Transformer was evaluated on several standard video-text matching benchmarks, including MSRVTT, DiDeMo, MSVD, and LSMDC. The method showed marked improvements in retrieval performance, reinforcing the value of incorporating object-level information in video-LLMs. Furthermore, the model showcased robust zero-shot capabilities, suggesting enhanced generalization beyond the training datasets.

In the broader landscape of artificial intelligence, this research offers a pivotal step toward bridging video and text modalities with greater semantic precision. The integration of object detection techniques within pre-training frameworks sets a precedent for future studies on multimodal learning architectures. As AI continues to evolve, one prospective direction could be the development of self-supervised methods for object region identification, reducing the reliance on externally provided annotations and further streamlining the pre-training phase. Such advancements could lead to even more efficient and adaptable video-LLMs, expanding their applicability across diverse domains.

In conclusion, this paper provides valuable insights into the enhancement of semantic alignment in video-LLMs, contributing to the efficiency and accuracy of retrieval tasks. The Object-aware Transformer model represents a promising development in the field of AI, paving the way for more sophisticated and semantically aware multimodal learning systems.