thông tin chi tiết - Computer Vision - # Affordance Grounding

Interaction Relationship-aware Weakly Supervised Affordance Grounding for Intelligent Systems

Q: How can the interaction relationship-guided contrastive learning be further improved to capture more nuanced relationships between interactions?

To enhance the interaction relationship-guided contrastive learning in the INTRA framework, several strategies can be employed. First, incorporating a multi-layered relationship mapping approach could allow for a more granular understanding of interactions. Instead of a binary classification of interactions as positive or negative, a multi-class relationship map could categorize interactions into various degrees of similarity or relevance. This would enable the model to learn subtler distinctions between interactions that share common object parts but differ in context or action. Second, integrating contextual embeddings from large language models (LLMs) could provide richer semantic information about interactions. By analyzing the context in which interactions occur, the model could better differentiate between similar actions based on situational nuances. For instance, the interaction "hold" could be contextualized differently when associated with "drink" versus "carry," leading to more precise grounding. Lastly, employing a dynamic contrastive loss that adapts based on the training data could improve performance. This adaptive mechanism could weigh the importance of certain interactions more heavily based on their frequency or relevance in the dataset, allowing the model to focus on learning from the most informative examples.

Q: What other types of linguistic knowledge, beyond synonyms, could be leveraged to enhance the robustness of text conditioning for affordance grounding?

Beyond synonyms, various types of linguistic knowledge can be leveraged to enhance text conditioning in affordance grounding. One approach is to utilize hypernyms and hyponyms from lexical databases like WordNet. Hypernyms can provide broader categories for interactions, while hyponyms can specify more detailed actions, enriching the model's understanding of affordance relationships. Additionally, semantic role labeling (SRL) can be employed to identify the roles that different entities play in interactions. By understanding who is performing the action, what is being acted upon, and the context of the action, the model can better ground affordances in a more contextually relevant manner. Furthermore, leveraging co-occurrence statistics from large corpora can provide insights into how often certain interactions appear together in natural language. This statistical knowledge can help the model learn associations between actions that are frequently mentioned in similar contexts, thereby improving its ability to generalize to novel interactions. Lastly, incorporating pragmatic knowledge, such as the intended purpose or function of an object, can also enhance robustness. Understanding why an object is used in a particular way can inform the model about the likely affordances associated with it, leading to more accurate grounding.

Q: Can the proposed INTRA framework be extended to other vision-language tasks beyond affordance grounding, such as action recognition or visual reasoning?

Yes, the INTRA framework can be effectively extended to other vision-language tasks, including action recognition and visual reasoning. The core principles of interaction relationship-guided contrastive learning and text-conditioned affordance map generation are versatile and can be adapted to various applications. For action recognition, the framework can be modified to focus on identifying and localizing actions within video frames. By leveraging the interaction relationships learned during affordance grounding, the model can recognize actions based on the affordances associated with specific objects in the scene. This would allow for a more nuanced understanding of actions that involve multiple objects and interactions. In the context of visual reasoning, the INTRA framework can be utilized to enhance the model's ability to answer questions about images based on the relationships between objects and their interactions. By integrating the interaction relationship map with visual features, the model can reason about complex scenarios, such as inferring the outcome of an interaction or predicting the next likely action based on the current state of the scene. Moreover, the use of large language models for text conditioning can facilitate the incorporation of complex reasoning tasks, enabling the model to draw inferences and make predictions based on both visual and textual information. This adaptability makes the INTRA framework a promising candidate for a wide range of vision-language tasks beyond affordance grounding.

Khái niệm cốt lõi

INTRA, a novel weakly supervised affordance grounding framework, leverages interaction relationship-guided contrastive learning and text-conditioned affordance map generation to enable flexible and accurate grounding of multiple affordances on a single object, without requiring paired egocentric and exocentric images.

Tóm tắt

The paper proposes INTRA, a novel weakly supervised affordance grounding framework that addresses the challenges in prior works. INTRA recasts the problem as representation learning, eliminating the need for paired egocentric and exocentric images during training.

Key highlights:

INTRA employs interaction relationship-guided contrastive learning to capture the complex relationships between different interactions, enabling accurate grounding of multiple affordances on a single object.
INTRA leverages vision-language model (VLM) text encoders and large language models (LLMs) to perform text-conditioned affordance map generation, allowing flexible inference on novel interactions beyond the pre-defined set.
INTRA integrates text synonym augmentation to enhance the robustness of text conditioning, further improving performance.

The experimental results demonstrate that INTRA outperforms state-of-the-art weakly supervised affordance grounding methods on diverse datasets, including AGD20K, IIT-AFF, CAD, and UMD. INTRA also exhibits remarkable domain scalability, performing well on synthesized images, illustrations, and novel objects/interactions.

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Thống kê

"Our method outperformed prior arts on diverse datasets such as AGD20K, IIT-AFF, CAD and UMD."
"Experimental results demonstrate that our method has remarkable domain scalability for synthesized images / illustrations and is capable of performing affordance grounding for novel interactions and objects."

Trích dẫn

"INTRA recasts this problem as representation learning to identify unique features of interactions through contrastive learning with exocentric images only, eliminating the need for paired datasets."
"We leverage vision-language model embeddings for performing affordance grounding flexibly with any text, designing text-conditioned affordance map generation to reflect interaction relationship for contrastive learning and enhancing robustness with our text synonym augmentation."

Thông tin chi tiết chính được chắt lọc từ

INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding

by Ji Ha Jang, ... lúc arxiv.org 09-11-2024

https://arxiv.org/pdf/2409.06210.pdf

INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding

Yêu cầu sâu hơn

How can the interaction relationship-guided contrastive learning be further improved to capture more nuanced relationships between interactions?

To enhance the interaction relationship-guided contrastive learning in the INTRA framework, several strategies can be employed. First, incorporating a multi-layered relationship mapping approach could allow for a more granular understanding of interactions. Instead of a binary classification of interactions as positive or negative, a multi-class relationship map could categorize interactions into various degrees of similarity or relevance. This would enable the model to learn subtler distinctions between interactions that share common object parts but differ in context or action.
Second, integrating contextual embeddings from large language models (LLMs) could provide richer semantic information about interactions. By analyzing the context in which interactions occur, the model could better differentiate between similar actions based on situational nuances. For instance, the interaction "hold" could be contextualized differently when associated with "drink" versus "carry," leading to more precise grounding.
Lastly, employing a dynamic contrastive loss that adapts based on the training data could improve performance. This adaptive mechanism could weigh the importance of certain interactions more heavily based on their frequency or relevance in the dataset, allowing the model to focus on learning from the most informative examples.

What other types of linguistic knowledge, beyond synonyms, could be leveraged to enhance the robustness of text conditioning for affordance grounding?

Beyond synonyms, various types of linguistic knowledge can be leveraged to enhance text conditioning in affordance grounding. One approach is to utilize hypernyms and hyponyms from lexical databases like WordNet. Hypernyms can provide broader categories for interactions, while hyponyms can specify more detailed actions, enriching the model's understanding of affordance relationships.
Additionally, semantic role labeling (SRL) can be employed to identify the roles that different entities play in interactions. By understanding who is performing the action, what is being acted upon, and the context of the action, the model can better ground affordances in a more contextually relevant manner.
Furthermore, leveraging co-occurrence statistics from large corpora can provide insights into how often certain interactions appear together in natural language. This statistical knowledge can help the model learn associations between actions that are frequently mentioned in similar contexts, thereby improving its ability to generalize to novel interactions.
Lastly, incorporating pragmatic knowledge, such as the intended purpose or function of an object, can also enhance robustness. Understanding why an object is used in a particular way can inform the model about the likely affordances associated with it, leading to more accurate grounding.

Can the proposed INTRA framework be extended to other vision-language tasks beyond affordance grounding, such as action recognition or visual reasoning?

Yes, the INTRA framework can be effectively extended to other vision-language tasks, including action recognition and visual reasoning. The core principles of interaction relationship-guided contrastive learning and text-conditioned affordance map generation are versatile and can be adapted to various applications.
For action recognition, the framework can be modified to focus on identifying and localizing actions within video frames. By leveraging the interaction relationships learned during affordance grounding, the model can recognize actions based on the affordances associated with specific objects in the scene. This would allow for a more nuanced understanding of actions that involve multiple objects and interactions.
In the context of visual reasoning, the INTRA framework can be utilized to enhance the model's ability to answer questions about images based on the relationships between objects and their interactions. By integrating the interaction relationship map with visual features, the model can reason about complex scenarios, such as inferring the outcome of an interaction or predicting the next likely action based on the current state of the scene.
Moreover, the use of large language models for text conditioning can facilitate the incorporation of complex reasoning tasks, enabling the model to draw inferences and make predictions based on both visual and textual information. This adaptability makes the INTRA framework a promising candidate for a wide range of vision-language tasks beyond affordance grounding.