Video-Conditioned Text Representations for Improved Activity Recognition

Khái niệm cốt lõi

Video-conditioned text representations can be more effective than just enhancing visual embeddings when adapting image-text models to the video domain, enabling better generalization to complex activity recognition tasks.

Tóm tắt

The paper introduces VicTR, a framework for adapting image-text models (such as CLIP) to the video domain, with a focus on video-conditioned text representations.
Key highlights:

Current approaches to adapting image-text models to video tend to focus on enhancing visual embeddings, while keeping text embeddings unchanged or discarded.
VicTR instead focuses on adapting the text embeddings, generating video-conditioned text representations that are unique for each video.
This allows the text embeddings to better align with the video embeddings in the shared latent space, improving the model's ability to reason about activities.
VicTR can also leverage freely-available auxiliary semantic information (e.g. object, scene, human-subject) in the form of visually-grounded text to further guide the latent space optimization.
VicTR is evaluated on few-shot, zero-shot, short-form and long-form activity recognition benchmarks, showing strong performance compared to prior video-text models.
The results highlight the importance of updating text representations, rather than just visual embeddings, when adapting image-text models to video understanding tasks.

Thống kê

"Video understanding poses significant challenges, often adding to the complications in image domain such as model complexity and annotation costs."
"Activity Recognition (i.e., classification) in particular— as the prominent task in video understanding— has long been explored by the community in these research directions."
"More recently, with the availability of internet-scale paired image-text data, the direction of vision-language models (VLMs) have emerged dominant, achieving strong generalization across numerous benchmarks."
"However, the progress of VLMs in the video domain is yet to be caught-up to its full potential."

Trích dẫn

"Video-conditioned Text Representations (VicTR): a form of text embeddings optimized w.r.t. visual embeddings, creating a more-flexible contrastive latent space."
"Our video-conditioned text embeddings that are unique to each video, allowing more-flexibility to move in the latent space and generalize to complex downstream tasks."

Thông tin chi tiết chính được chắt lọc từ

VicTR

by Kumara Kahat... lúc arxiv.org 04-01-2024

https://arxiv.org/pdf/2304.02560.pdf

Yêu cầu sâu hơn

How can the video-conditioned text representations be further improved, for example by incorporating temporal dynamics or multi-modal cues beyond just vision

To further enhance video-conditioned text representations, incorporating temporal dynamics and multi-modal cues beyond vision can be beneficial. One approach is to introduce recurrent or transformer-based models that can capture temporal dependencies in the text embeddings. By considering the sequential nature of video frames and the corresponding text descriptions, the model can learn to align text representations with specific moments in the video. Additionally, integrating audio information can provide complementary cues to enhance the understanding of activities in videos. By incorporating audio-text embeddings alongside visual-text embeddings, the model can capture a richer representation of the content and context within the video.

What are the potential limitations or drawbacks of the video-conditioned text approach, and how could they be addressed

While the video-conditioned text approach offers significant advantages, there are potential limitations and drawbacks that need to be addressed. One limitation is the reliance on pretraining data, which may not fully capture the diversity and complexity of real-world video data. To mitigate this, strategies such as data augmentation, domain adaptation, or semi-supervised learning can be employed to enhance the model's generalization capabilities. Another drawback is the computational complexity of processing multiple modalities simultaneously. Optimizing the model architecture and leveraging efficient attention mechanisms can help address this issue. Additionally, ensuring the interpretability and explainability of the learned representations is crucial, especially in applications where decision-making based on these representations is critical.

How could the insights from adapting image-text models to video be applied to other domains that involve reasoning over sequences, such as language modeling or robotics

The insights gained from adapting image-text models to video can be applied to other domains that involve reasoning over sequences, such as language modeling or robotics. In language modeling, the concept of joint image-text embeddings can be extended to incorporate temporal dynamics in text sequences. By aligning textual descriptions with corresponding images or video frames, the model can learn to generate more contextually relevant and coherent text. This can improve tasks like image captioning, where generating descriptive text based on visual content is essential. In robotics, the idea of video-conditioned text representations can be utilized for tasks that involve understanding and interpreting sequential data from sensors or cameras. By integrating textual descriptions with visual and sensor data, robots can better comprehend their environment and perform complex tasks with improved accuracy and efficiency.