Leveraging Weak Labels for Multi-View Video-Based Learning Framework

Główne pojęcia

Utilizing weak labels for frame-level perception tasks in multi-view learning is challenging but achievable through a novel learning framework.

Streszczenie

The content discusses a novel learning framework that leverages weak labels for frame-level perception tasks in multi-view video-based learning. Annotating frame-level labels is tedious, leading to the proposal of a two-step approach using weak labels to train a base model and downstream models. The base model incorporates view-specific latent embeddings trained with a novel latent loss function. The downstream models integrate these embeddings for improved accuracy in action detection and recognition tasks. Experimental evaluation on the MM Office dataset demonstrates the effectiveness of the proposed framework compared to baseline algorithms. The study contributes a new perspective on utilizing weak labels efficiently in multi-view learning.

Statystyki

For training the base model, we propose a novel latent loss function.
The proposed framework is evaluated using the MM Office dataset.
The results show that the proposed base model is effectively trained using weak labels.
The proposed framework outperforms baseline algorithms in accuracy improvement.

Cytaty

"The proposed framework utilizes easily available weak labels for frame-level perception tasks."
"The integration of learned latent embeddings enhances accuracy in downstream models."
"Comparative analysis shows advantages of the proposed method over baseline frameworks."

Kluczowe wnioski z

Multi-View Video-Based Learning

by Vijay John,Y... o arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11616.pdf

Głębsze pytania

How can this novel framework be extended to handle multimodal multi-view data?

To extend this novel framework for handling multimodal multi-view data, one approach could involve incorporating additional sensor modalities such as audio or depth information. By integrating these diverse sources of data, the model can gain a more comprehensive understanding of the environment and improve its perception capabilities. The framework may need to be adapted to process and fuse multiple types of input data effectively. This adaptation could include modifying the shared encoder to accommodate different modalities, adjusting the embedding modules to incorporate features from various sensors, and enhancing the transformer module to handle the fusion of multimodal inputs efficiently.

What are potential limitations or drawbacks of relying on weak labels for training?

Relying on weak labels for training poses several limitations and drawbacks. One significant challenge is that weak labels provide less precise information compared to strong annotations at the frame level. This ambiguity in labeling can lead to suboptimal model performance and difficulty in accurately capturing fine-grained details in complex tasks like action recognition. Weak labels may also introduce noise into the training process, potentially hindering convergence and affecting overall model generalization.
Furthermore, using weak labels might restrict the model's ability to learn intricate patterns present in the data due to limited supervision signals provided by coarse annotations. As a result, there is a risk of underfitting or overfitting when training with weakly labeled data, impacting both model accuracy and robustness.

How might incorporating additional modalities or sensor data impact the performance of the proposed framework?

Incorporating additional modalities or sensor data into the proposed framework has the potential to significantly enhance its performance across various tasks related to multi-view video-based learning. By integrating diverse sources of information such as audio cues, depth measurements, or other sensor inputs alongside visual data from multiple views, the model gains access to richer contextual clues for better understanding complex scenarios.
The inclusion of supplementary modalities can improve feature representation learning by providing complementary perspectives on events or actions captured in videos. This holistic view enables more robust inference about activities taking place within a scene while reducing ambiguities that may arise from visual input alone.
Moreover, leveraging multiple modalities allows for cross-modal correlation learning which can help disambiguate challenging scenarios where visual cues alone may not suffice. By fusing information from different sensors intelligently within an integrated framework like this one presented here, it becomes possible to achieve higher accuracy levels in tasks like action recognition and event detection through enhanced context awareness and improved discriminative power.