Leveraging Scene Context to Improve Resilience of Visual Referring Expression Generation Models

Core Concepts

Visual scene context can make referring expression generation models more resilient to perturbations in target object representations, enabling them to identify referent types even when target information is completely missing.

Abstract

This paper investigates the role of visual scene context in improving the resilience of referring expression generation (REG) models to imperfect target representations. The authors train and test Transformer-based REG models with varying degrees of noise added to the target object representations, and evaluate their performance using automatic metrics as well as a focused human evaluation on the validity of assigned referent types.
The results show that even simple scene context information can make REG models surprisingly resilient to target perturbations. When target representations are entirely occluded by noise, models with access to visual context can still identify referent types with high accuracy, outperforming models that only have access to the target information. This effect is more pronounced for non-human object classes, where context seems to provide stronger associations.
Further analysis reveals that the REG models learn to exploit the co-occurrence of similar objects in the visual context to compensate for missing target information. The models allocate more attention to context objects of the same class as the referent, especially in the decoder. This suggests that REG models can leverage regular patterns of object co-occurrence in scenes to generate adequate descriptions, even when the target itself is not clearly visible.
The authors also conduct experiments on a more diverse and less standardized dataset (PACO-EGO4D), which show that the effectiveness of visual context is not universally applicable, but depends on the characteristics of the data. Overall, the findings offer new perspectives on the role of scene context in visual REG, going beyond the traditional view of context as a source of distraction.

Stats

The target object is a couch on the right side of the image.
The visual context contains a brown chair on the right side of the image.

Quotes

"Scene context is well known to facilitate hu-mans' perception of visible objects."
"We take a new perspective on scene context in REG and hypothesize that contextual information can be conceived of as a resource that makes REG models more resilient and facilitates the generation of object descriptions, and object types in particular."

Key Insights Distilled From

Resilience through Scene Context in Visual Referring Expression Generation

by Sime... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12289.pdf

Deeper Inquiries

How can the insights from this study be leveraged to improve the robustness of REG models in real-world applications with diverse and noisy visual inputs?

The study highlights the importance of leveraging contextual information to enhance the resilience of Referring Expression Generation (REG) models in handling noisy visual inputs. One key takeaway is the effectiveness of visual scene context in aiding models to generate accurate descriptions even when target information is obscured. To improve the robustness of REG models in real-world applications, these insights can be applied in the following ways:

Contextual Augmentation: Incorporating diverse contextual information beyond visual scene context, such as temporal context or user-specific context, can help models adapt to a wider range of scenarios and noisy inputs.
Transfer Learning: Pre-training models on a diverse set of data with varying levels of noise and context can help them generalize better to real-world applications with noisy visual inputs.
Adaptive Attention Mechanisms: Implementing adaptive attention mechanisms that dynamically adjust the focus on different parts of the input based on the available context can improve the model's ability to generate accurate expressions in noisy environments.
Ensemble Learning: Combining multiple REG models that specialize in different types of contextual information can enhance the overall robustness and accuracy of the system in diverse and noisy visual settings.

How do the findings relate to human language production and the role of context in referring expression generation in natural conversations?

The findings of the study shed light on the parallels between how REG models leverage contextual information and how humans utilize context in language production and referring expression generation in natural conversations. Just as REG models benefit from visual scene context to generate accurate descriptions, humans rely on contextual cues to facilitate communication. Some key connections include:

Semantic Understanding: Similar to how humans rapidly grasp the gist of a scene and understand objects in context, REG models use contextual information to identify referents and generate appropriate descriptions.
Resilience to Ambiguity: Both humans and REG models leverage context to disambiguate references and ensure the accuracy of expressions, especially in the presence of noise or incomplete information.
Adaptation to Varying Scenarios: Just as humans adjust their language based on the context of a conversation, REG models can adapt their output based on the available visual context, demonstrating flexibility and robustness in generating referring expressions.

What other types of contextual information, beyond visual scene context, could be exploited by REG models to enhance their resilience?

In addition to visual scene context, REG models can leverage various other types of contextual information to enhance their resilience and improve the accuracy of referring expression generation. Some alternative forms of context that could be beneficial include:

Temporal Context: Incorporating information about the temporal sequence of events or the order of appearance of objects can help models generate more coherent and contextually relevant descriptions.
Spatial Context: Utilizing spatial relationships between objects, such as proximity or relative positioning, can aid in generating more precise and informative referring expressions.
User Context: Considering user-specific information, such as preferences, history, or interactions, can personalize the generation of referring expressions and make them more tailored to individual users.
Task-Specific Context: Adapting the model's output based on the specific task or goal at hand can enhance the relevance and accuracy of the generated expressions in different contexts or scenarios.
By incorporating a diverse range of contextual information beyond visual scene context, REG models can become more adaptable, robust, and effective in generating accurate referring expressions across a wide array of real-world applications.