insight - Computer Vision - # Robust Open-Vocabulary Action Recognition

Improving Robustness of Open-Vocabulary Action Recognition by Denoising Noisy Class Descriptions

Q: How can the DENOISER framework be extended to handle other types of noises beyond misspellings and typos, such as semantic shifts or ambiguities in class descriptions

To extend the DENOISER framework to handle other types of noises beyond misspellings and typos in class descriptions, such as semantic shifts or ambiguities, several approaches can be considered: Semantic Embeddings: Incorporating semantic embeddings of class descriptions can help capture the underlying meaning of the text, allowing the model to identify and correct semantic shifts or ambiguities in the descriptions. Contextual Information: Utilizing contextual information from surrounding words or phrases in the class descriptions can provide additional context for the denoising process, helping the model disambiguate between similar words or phrases. Language Models: Leveraging advanced language models like BERT or GPT can aid in understanding the context and semantics of the noisy class descriptions, enabling the model to make more informed corrections. Knowledge Graphs: Integrating knowledge graphs or ontologies can assist in resolving semantic ambiguities by providing structured information about the relationships between different concepts or entities mentioned in the class descriptions. By incorporating these strategies, the DENOISER framework can be enhanced to handle a broader range of noise types in class descriptions, ensuring more accurate and robust denoising capabilities.

Q: How can the proposed iterative optimization between text denoising and action recognition be further improved in terms of convergence and efficiency

To improve the iterative optimization between text denoising and action recognition in terms of convergence and efficiency, the following enhancements can be implemented: Dynamic Learning Rates: Implementing adaptive learning rate schedules can help optimize the convergence of the iterative process by adjusting the learning rates based on the progress of the optimization. Early Stopping: Introducing early stopping criteria based on validation performance can prevent overfitting and improve efficiency by stopping the iterations when the model starts to overfit on the training data. Batch Processing: Batch processing of text denoising and action recognition tasks can improve efficiency by processing multiple samples simultaneously, reducing computational overhead and speeding up the optimization process. Parallel Processing: Utilizing parallel processing techniques can further enhance efficiency by distributing the workload across multiple processors or GPUs, enabling faster convergence and optimization. By incorporating these strategies, the iterative optimization process in the DENOISER framework can be enhanced for improved convergence and efficiency.

Q: What are the potential applications of the robust OVAR capability enabled by the DENOISER framework beyond action recognition, such as in other vision-language tasks

The robust OVAR capability enabled by the DENOISER framework can have several potential applications beyond action recognition in various vision-language tasks, including: Visual Question Answering (VQA): The robust denoising capabilities of DENOISER can be applied to improve the accuracy of text-based queries in VQA tasks, enhancing the model's ability to understand and respond to questions about visual content. Image Captioning: By denoising textual descriptions associated with images, DENOISER can enhance the quality and accuracy of image captions generated by vision-language models, improving the overall performance of image captioning systems. Visual Dialog: In visual dialog tasks, DENOISER can help improve the understanding of noisy text inputs during dialog interactions between humans and machines, leading to more coherent and contextually relevant responses. Cross-Modal Retrieval: The robust denoising capabilities of DENOISER can benefit cross-modal retrieval tasks by improving the matching accuracy between visual and textual representations, enhancing the retrieval performance in multimedia search applications. By applying the DENOISER framework to these vision-language tasks, the enhanced robustness and accuracy can lead to significant improvements in various real-world applications requiring effective integration of visual and textual information.

Conceitos Básicos

Existing open-vocabulary action recognition methods are highly sensitive to noisy class descriptions, which are common in real-world scenarios. To address this issue, the paper proposes the DENOISER framework that jointly optimizes text denoising and action recognition in an iterative manner, leveraging both textual and visual information.

Resumo

The paper explores the problem of open-vocabulary action recognition (OVAR) where the model needs to classify videos into a broad range of action categories, including novel unseen classes during inference. Existing OVAR methods rely on vision-language alignment, treating class names as textual descriptions and measuring similarity between visual and textual embeddings. However, these methods assume the class descriptions provided by users are always clean, which is unrealistic in practice as misspellings and typos are common.

To address this issue, the paper first evaluates the robustness of existing OVAR methods under simulated noise in class descriptions, revealing their poor performance. It then proposes the DENOISER framework, which consists of two main components:

Generative Step: This step treats denoising of class texts as a generative task. It proposes multiple candidate texts based on spelling similarity, and then uses both inter-modal (visual-textual) and intra-modal (textual-only) information to determine the best denoised text.
Discriminative Step: This step uses the existing OVAR models to assign class labels to visual samples. The assigned visual samples are then used to help denoise the corresponding class texts in the generative step.

The generative and discriminative steps are optimized in an alternating manner, where the denoised text classes help improve the OVAR model, and the classified visual samples in turn help better denoise the text classes.

Extensive experiments on multiple datasets and OVAR models show that the proposed DENOISER framework significantly improves the robustness against noisy class descriptions, outperforming various baselines. Detailed ablation studies further validate the effectiveness of the key components.

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Estatísticas

Just a small amount of noise (5%) can lower the classification accuracy of existing OVAR methods by a large margin.
As the noise level increases, the performance of existing OVAR methods degrades significantly.

Citações

"One crucial issue is completely ignored: the class descriptions given by users may be noisy, e.g., misspellings and typos, limiting the real-world practicality of vanilla OVAR."
"We are hence motivated to fill the research gap of noisy class names in OVAR."

Principais Insights Extraídos De

DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition

by Haozhe Cheng... às arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14890.pdf

DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition

Perguntas Mais Profundas

How can the DENOISER framework be extended to handle other types of noises beyond misspellings and typos, such as semantic shifts or ambiguities in class descriptions

To extend the DENOISER framework to handle other types of noises beyond misspellings and typos in class descriptions, such as semantic shifts or ambiguities, several approaches can be considered:

Semantic Embeddings: Incorporating semantic embeddings of class descriptions can help capture the underlying meaning of the text, allowing the model to identify and correct semantic shifts or ambiguities in the descriptions.

Contextual Information: Utilizing contextual information from surrounding words or phrases in the class descriptions can provide additional context for the denoising process, helping the model disambiguate between similar words or phrases.

Language Models: Leveraging advanced language models like BERT or GPT can aid in understanding the context and semantics of the noisy class descriptions, enabling the model to make more informed corrections.

Knowledge Graphs: Integrating knowledge graphs or ontologies can assist in resolving semantic ambiguities by providing structured information about the relationships between different concepts or entities mentioned in the class descriptions.

By incorporating these strategies, the DENOISER framework can be enhanced to handle a broader range of noise types in class descriptions, ensuring more accurate and robust denoising capabilities.

How can the proposed iterative optimization between text denoising and action recognition be further improved in terms of convergence and efficiency

To improve the iterative optimization between text denoising and action recognition in terms of convergence and efficiency, the following enhancements can be implemented:

Dynamic Learning Rates: Implementing adaptive learning rate schedules can help optimize the convergence of the iterative process by adjusting the learning rates based on the progress of the optimization.

Early Stopping: Introducing early stopping criteria based on validation performance can prevent overfitting and improve efficiency by stopping the iterations when the model starts to overfit on the training data.

Batch Processing: Batch processing of text denoising and action recognition tasks can improve efficiency by processing multiple samples simultaneously, reducing computational overhead and speeding up the optimization process.

Parallel Processing: Utilizing parallel processing techniques can further enhance efficiency by distributing the workload across multiple processors or GPUs, enabling faster convergence and optimization.

By incorporating these strategies, the iterative optimization process in the DENOISER framework can be enhanced for improved convergence and efficiency.

What are the potential applications of the robust OVAR capability enabled by the DENOISER framework beyond action recognition, such as in other vision-language tasks

The robust OVAR capability enabled by the DENOISER framework can have several potential applications beyond action recognition in various vision-language tasks, including:

Visual Question Answering (VQA): The robust denoising capabilities of DENOISER can be applied to improve the accuracy of text-based queries in VQA tasks, enhancing the model's ability to understand and respond to questions about visual content.

Image Captioning: By denoising textual descriptions associated with images, DENOISER can enhance the quality and accuracy of image captions generated by vision-language models, improving the overall performance of image captioning systems.

Visual Dialog: In visual dialog tasks, DENOISER can help improve the understanding of noisy text inputs during dialog interactions between humans and machines, leading to more coherent and contextually relevant responses.

Cross-Modal Retrieval: The robust denoising capabilities of DENOISER can benefit cross-modal retrieval tasks by improving the matching accuracy between visual and textual representations, enhancing the retrieval performance in multimedia search applications.

By applying the DENOISER framework to these vision-language tasks, the enhanced robustness and accuracy can lead to significant improvements in various real-world applications requiring effective integration of visual and textual information.