Improving Scene Graph Generation with Relation Words' Debiasing in Vision-Language Models

Core Concepts

Enhancing scene graph generation by debiasing relation words in vision-language models.

Abstract

• Scene Graph Generation (SGG) complexity and underrepresentation issues.
• Proposal to use pre-trained vision-language models for representation enhancement.
• Introduction of LM Estimation to address relation word biases.
• Dynamic ensemble strategy to improve SGG representation.
• Experimental results showcasing performance enhancements.

Stats

"Woman carrying towel" score only 0.05 due to the umbrella being closed, while its counterpart with an opened umbrella scores markedly higher.
The comprehensive knowledge of VLMs can help compensate for underrepresented samples.
The LM Estimation method seeks to undermine the proxy relation words’ distribution for SGG within VLMs.

Quotes

"Our method effectively addresses the words biases, enhances SGG’s representation, and achieves remarkable performance enhancements."

Key Insights Distilled From

Improving Scene Graph Generation with Relation Words' Debiasing in Vision-Language Models

by Yuxuan Wang,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16184.pdf

Deeper Inquiries

How can LM Estimation be applied in other vision-language tasks

LM Estimation can be applied in other vision-language tasks by adapting the method to estimate the label distribution specific to the task at hand. This involves gathering pairs with labels from a validation set and formulating an optimization objective that minimizes cross-entropy loss between the logits adjusted by the estimated label distribution and the ground-truth labels. By solving this constrained optimization problem, LM Estimation can effectively estimate and mitigate bias in pre-trained VLMs for various vision-language tasks.

What are the potential limitations of using pre-trained VLMs in addressing underrepresentation

The potential limitations of using pre-trained VLMs in addressing underrepresentation include:

Limited Training Data: Pre-trained models may not have been exposed to all possible scenarios or classes present in a specific dataset, leading to gaps in knowledge.
Domain Mismatch: The data distribution and objectives of pre-training may differ significantly from those of the target task, resulting in biases when directly applying zero-shot models.
Complexity of Tasks: Vision-language tasks often involve intricate relationships and diverse semantics, which may challenge pre-trained models' ability to generalize effectively across all instances.
Scalability Issues: As datasets grow larger or more complex, pre-trained models may struggle to adapt efficiently without fine-tuning or additional strategies.

How can dynamic ensemble strategies be further optimized for different datasets

Dynamic ensemble strategies can be further optimized for different datasets by:

Adaptive Weighting Schemes: Implementing adaptive weighting schemes based on sample characteristics such as difficulty level, uncertainty scores, or model confidence levels can enhance ensemble performance.
Task-Specific Tuning: Tailoring ensemble weights based on specific dataset properties or task requirements can optimize performance for different scenarios.
Ensemble Diversity: Incorporating diverse model architectures or training methodologies within ensembles can improve robustness and generalization across varied datasets.
Continuous Learning Mechanisms: Implementing mechanisms for continuous learning where ensemble weights are updated dynamically based on incoming data streams can ensure adaptability over time.

By incorporating these optimizations, dynamic ensemble strategies can be tailored effectively to suit the unique characteristics of different datasets and maximize performance gains in vision-language tasks.