💻 computer science

A Dual-Balance Framework for Long-Tailed Multimodal Relation Extraction

This paper proposes a unified dual-balance framework that addresses both label and modality imbalances in long-tailed multimodal relation extraction through a modality-branch fusion strategy for cross-modal consistency and a prior-aware class calibrator for improved tail relation recognition, demonstrating competitive performance on MNRE and MORE benchmarks.

Original authors: Zhihao Lin, Hailin Wang, Ao Ma, Hangyi Ren, He Ma, Yanbing Xie, Dan Zhang

Published 2026-08-24

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CC BY 4.0

Original authors: Zhihao Lin, Hailin Wang, Ao Ma, Hangyi Ren, He Ma, Yanbing Xie, Dan Zhang

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the vast, noisy landscape of social media, where billions of posts mix words with pictures every day, computers face a difficult task: understanding the true meaning behind the combination. This field, known as multimodal relation extraction, asks machines to identify how two people, places, or things are connected within a single post. It is a fundamental skill for building digital maps of human knowledge, allowing search engines to find specific facts and helping artificial intelligence make sense of our world. However, for a computer to learn this, it must be trained on massive amounts of examples. The problem is that real-world data is rarely fair. Just as a library might have thousands of copies of a bestseller but only a single copy of a rare, obscure book, social media data is dominated by common relationships while rare, specific connections are left with very few examples. Furthermore, the two types of information—text and images—do not always agree. A picture might be blurry, misleading, or simply irrelevant, while the text is clear, or vice versa. When a computer tries to learn from this uneven and noisy mix, it tends to ignore the rare connections and the confusing images, failing to understand the full story.

Researchers at Southwestern University of Finance and Economics have developed a new system designed to fix these specific problems. They recognized that standard computer models often get stuck because they are overwhelmed by the most common types of relationships and confused by unreliable pictures. To solve this, they created a unified framework that acts like a dual-balance system, addressing the issue of rare data and the issue of conflicting information at the same time. Instead of treating text and images as equal partners from the start, their system learns to weigh them carefully. It understands that sometimes a picture is noisy and should be trusted less, while the text holds the truth, and at other times, the image provides a crucial clue that the words missed. This adaptive approach allows the computer to focus on the right signal for each specific post, rather than blindly following the most frequent patterns.

The second part of their solution tackles the problem of the "long tail," where rare relationships are ignored because they appear so infrequently in the training data. Standard models naturally become biased toward the common relationships, much like a person who only reads the headlines and misses the deep stories. The researchers introduced a mechanism that adjusts the computer's final decision-making process. By looking at how often each type of relationship appears in the training set, the system can consciously correct its own bias. It essentially tells the model, "Do not be so confident in the common answers; pay closer attention to the rare ones." This adjustment happens without needing to artificially create more data or throw away the common examples, allowing the model to learn a more complete picture of reality.

To test their ideas, the team applied this framework to two major datasets of social media posts, which contained thousands of text-image pairs with known relationships. They compared their method against a wide range of existing models, including those that rely heavily on text alone and those that try to combine text and images in different ways. The results showed that their balanced approach consistently outperformed the others. Most importantly, it significantly improved the computer's ability to recognize the rare, tail-end relationships that previous models often missed. In one specific test, the system showed a dramatic improvement in identifying obscure connections, proving that the dual-balance strategy successfully prevented the model from being overwhelmed by the noise and the common patterns.

The researchers also looked closely at how their system worked internally to ensure it was doing what they intended. They found that the part of the system responsible for balancing text and images successfully learned to ignore misleading visual cues when the text was clear, and to use visual clues when the text was ambiguous. Similarly, the part responsible for correcting the bias toward common relationships was shown to shift the computer's confidence away from the frequent answers and toward the rare ones. When they tested the system with very little training data, it still performed better than the standard models, suggesting that this approach is robust and effective even when information is scarce. The study concludes that by addressing both the imbalance of information sources and the imbalance of data frequency simultaneously, computers can become much better at understanding the complex, messy reality of human communication on social media.

Technical Summary: A Dual-Balance Framework for Long-Tailed Multimodal Relation Extraction

1. Problem Statement

Multimodal Relation Extraction (MRE) aims to identify semantic relationships between entities in social media posts containing both text and images. While traditional text-based methods perform well on balanced datasets, real-world MRE faces two critical, often overlooked challenges that degrade model performance, particularly for rare relations:

Label Imbalance (Long-Tailed Distribution): Real-world datasets exhibit highly skewed label distributions where a few "head" relations dominate, while "tail" relations have insufficient training data. This biases models toward frequent classes, impairing their ability to generalize to rare but semantically valid relations.
Modality Imbalance: Textual and visual modalities contribute unequally. Text is typically semantically rich, whereas visual signals in social media are often noisy, incomplete, or misleading. This leads to cross-modal misalignment, where models over-rely on text and underutilize or misinterpret visual cues, weakening the effectiveness of multimodal fusion.

Existing approaches typically address these issues independently or assume balanced contributions, failing to provide a unified solution for the dual imbalance problem.

2. Methodology

The authors propose a Unified Dual-Balance Framework that jointly addresses modality imbalance at the representation level and label imbalance at the decision level. The framework consists of three primary components:

A. Cross-Modal Representation (CMR)

The framework first encodes inputs using pre-trained backbones (BERT for text, CLIP-ViT or ResNet-50 for images).

Textual Encoding: Special markers are inserted to highlight entity pairs.
Visual Encoding: A unified object-aware representation is constructed by concatenating features from the original image, a generated image (providing auxiliary semantic cues), and grounded object regions (providing entity-local evidence).
Alignment: Text-guided cross-modal attention aligns visual tokens to textual tokens, projecting both modalities into a shared hidden space.

B. Modality-Balancer: Modality-Branch Fusion Strategy (MFS)

To address modality imbalance, the MFS module dynamically balances the contributions of text and vision.

Dual-Branch Architecture: It employs two groups of parallel branches (K text branches and K visual branches) that process representations separately.
Balancing Gating: A gating network analyzes global summaries of both modalities and their interaction to generate a normalized routing vector. This vector assigns weights to the text branches, visual branches, and a residual text shortcut.
Adaptive Fusion: The final representation is a weighted sum of the branch outputs. This allows the model to rely more on visual evidence when it is informative and shift reliance to text when visual signals are noisy, while preserving reliable semantic cues via the residual shortcut.

C. Label-Balancer: Prior-aware Class Calibrator (PCC)

To address label imbalance, the PCC module operates at the decision level to correct prediction bias without heavy resampling.

Logit Calibration: The module estimates empirical class priors from the training set. It adjusts the classifier logits by subtracting a term proportional to the log of the class prior ( $\log \hat{p}_y$ ).
Adaptive Strength: The calibration strength ( $\alpha$ ) is dynamically computed based on the variance of the class prior distribution. A higher variance (indicating severe imbalance) triggers stronger calibration, effectively shifting decision boundaries to favor tail relations.
Loss Function: The model is optimized using a PCC-calibrated cross-entropy loss, which penalizes over-confident predictions on head classes and encourages recognition of tail classes.

3. Key Contributions

The paper claims the following contributions:

Unified Framework: Proposal of a single framework that simultaneously tackles modality imbalance (representation-level) and label imbalance (decision-level) for long-tailed MRE.
Specialized Modules:
- MFS: A strategy using gating mechanisms to adaptively balance and integrate visual and textual signals, enhancing cross-modal consistency.
- PCC: A lightweight calibrator that reshapes decision boundaries based on label distribution priors to improve tail relation recognition.
Empirical Validation: Extensive experiments demonstrating that the framework achieves competitive performance and consistently improves tail relation recognition under dual imbalance conditions.

4. Experimental Results

The framework was evaluated on two benchmark datasets, MNRE and MORE, both characterized by severe label imbalance.

Overall Performance: The proposed framework, when integrated with strong baselines (e.g., HVFormer, TMR), consistently improved Micro-F1 scores. For instance, on the MORE dataset, integrating with HVFormer yielded a +1.82 Micro-F1 gain, and with TMR, a +2.50 gain.
Tail Relation Recognition: Analysis across frequency groups (High, Medium, Low) showed that the most significant improvements occurred in low-frequency (tail) groups. On MNRE, the low-frequency group saw a +10.15 F1 point increase; on MORE, a +13.92 increase.
Ablation Studies: Removing either MFS or PCC resulted in substantial performance drops, confirming that both representation-level balancing and decision-level calibration are necessary and complementary.
Robustness: The model demonstrated superior robustness in low-resource settings (5%-50% of training data) compared to baselines.
Case Studies: Visualizations of logits showed that the model successfully increased confidence in ground-truth tail relations while suppressing spurious activations for "None" or frequent incorrect relations.

5. Significance and Claims

The paper positions this work as a necessary step toward robust MRE in real-world scenarios. The authors claim that existing methods largely overlook the interaction between modality imbalance and label imbalance. By proposing a dual-balance approach, the framework:

Mitigates the tendency of models to over-rely on text when visual cues are noisy.
Alleviates the bias toward dominant relation classes without relying on data resampling.
Provides a unified pipeline that enhances the recognition of semantically valid but rare relations, which are critical for comprehensive knowledge graph construction and information extraction from social media.

The authors conclude that their method offers a stable and effective solution for long-tailed MRE, with future work planned to extend the framework to Multimodal Large Language Models (MLLMs) and more challenging low-resource scenarios.