DiffusionRet: Generative Text-Video Retrieval with Diffusion Model (2303.09867v2)

Abstract: Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query). This is accomplished through a diffusion-based text-video retrieval framework (DiffusionRet), which models the retrieval task as a process of gradually generating joint distribution from noise. During training, DiffusionRet is optimized from both the generation and discrimination perspectives, with the generator being optimized by generation loss and the feature extractor trained with contrastive loss. In this way, DiffusionRet cleverly leverages the strengths of both generative and discriminative methods. Extensive experiments on five commonly used text-video retrieval benchmarks, including MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo, with superior performances, justify the efficacy of our method. More encouragingly, without any modification, DiffusionRet even performs well in out-domain retrieval settings. We believe this work brings fundamental insights into the related fields. Code is available at https://github.com/jpthu17/DiffusionRet.

• The paper introduces a novel generative framework that models the joint probability of text and video data using diffusion models.
• It combines generative sampling with contrastive loss in a hybrid training objective, boosting robustness in varied data scenarios.
• The approach incorporates text-frame attention and iterative denoising, resulting in state-of-the-art retrieval performance across multiple benchmarks.

Generative Text-Video Retrieval with Diffusion Models: An Analysis of DiffusionRet

The paper "DiffusionRet: Generative Text-Video Retrieval with Diffusion Model" presents an innovative approach to text-video retrieval by leveraging the capabilities of generative diffusion models. Traditional methods for text-video retrieval predominantly utilize discriminant models that focus on conditional probability. This approach, while effective for in-domain tasks, demonstrates limitations when encountering out-of-distribution data due to its failure to fully capture the underlying data distribution. The authors address this limitation by modeling the retrieval problem from a generative perspective, leveraging diffusion models for joint probability generation. This shift allows for more robust performance across both seen and unseen data.

Methodological Advancements

The DiffusionRet framework represents a novel application of diffusion models in cross-modal retrieval tasks. The framework treats text-video retrieval as the process of generating a joint distribution from Gaussian noise, thus capturing the intrinsic data characteristics often missed by purely discriminant models. DiffusionRet employs a dual approach by optimizing from both generative and discriminative perspectives:

1. Generative Modeling: The retrieval problem is recast as generating the joint probability distribution of the text and video data. The authors employ diffusion models for this task, known for their ability to gradually remove noise to reveal the underlying data structure. This paradigm shift from conditional likelihood to joint distribution modeling presents an advancement in handling unseen data.
2. Text-Frame Attention Encoder: The method employs a text-frame attention mechanism, which integrates textual and visual data to form a cohesive multi-modal representation. This approach helps in capturing the semantic alignment between text and video frames effectively.
3. Query-Candidate Attention Denoising Network: This component enhances the generative framework by refining text and video correspondence over multiple iterations, providing a robust mechanism for improving retrieval performance from a noise-laden starting point.
4. Hybrid Training Objective: By integrating contrastive loss into the training process, the framework benefits from discriminative feature representation learning, thereby balancing generative modeling's flexibility with discriminative performance optimization.

Experimental Evaluation

DiffusionRet demonstrates state-of-the-art performances across multiple benchmark datasets such as MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo. The framework's effectiveness is attributed to its inherent generalizability and transferability, which allow it to perform admirably in out-of-domain retrieval settings without any architecture adjustments. This feature is particularly promising for applications requiring adaptable and robust retrieval systems in dynamic environments.

The experimental results validate the hypothesis that diffusion models can be leveraged beyond generative tasks, offering new insights into cross-modal retrieval applications. DiffusionRet's performance improvement in out-domain settings over competitive baselines highlights its potential for broad applicability across diverse datasets and conditions.

Implications and Future Directions

The introduction of diffusion models into the retrieval paradigm offers several key theoretical and practical implications. Theoretically, it challenges the prevailing discriminant-centric view and suggests a more comprehensive framework that leverages joint probability modeling. Practically, this may lead to more robust systems capable of real-world application where data variance is the norm.

Future research could explore scaling these methodologies to other multi-modal retrieval tasks, such as image-audio retrieval, or delve into pure generative training to further capitalize on diffusion models' full potential. Additionally, the interplay between generative and discriminative paradigms could inspire hybrid architectures that marry the strengths of both approaches, driving continued advancements in artificial intelligence and machine learning.

In conclusion, the DiffusionRet framework not only advances the state of text-video retrieval but also opens the door to broader research opportunities within generative model applications in multi-modal contexts.