Detached and Interactive Multimodal Learning (2407.19514v1)

Abstract: Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://github.com/fanyunfeng-bit/DI-MML.

Detached and Interactive Multimodal Learning: A Novel Framework

The paper presents "Detached and Interactive Multimodal Learning" (DI-MML), a framework introduced to address the challenges inherent in Multimodal Learning (MML), specifically the modality competition issue. MML aims to enhance learning models by integrating complementary information across various data modalities, such as images, audio, and text. Traditional joint training frameworks often fall short due to modality competition, where certain modalities dominate and potentially suppress others, thus inhibiting optimal learning and performance. DI-MML circumvents these challenges with an innovative detached learning approach, seeking to maintain cross-modal interactions while eliminating the constraints of competitive learning objectives.

Key Contributions

1. Detached Learning Objective: Unlike joint training models that employ a uniform learning objective for all data modalities, DI-MML utilizes isolated learning objectives tailored to each modality. This strategic separation is designed to prevent dominant modalities from stifling others, thereby allowing for a balanced development of each data type.
2. Cross-modal Interaction via Shared Classifier: To foster consistent feature representation across modalities, DI-MML employs a shared classifier that unites the features derived from separate modality-specific encoders. This shared feature space serves to facilitate cross-modal compatibility and integration, ultimately aiding in the fusion process.
3. Dimension-decoupled Unidirectional Contrastive (DUC) Loss: This novel loss function encourages knowledge transfer at the modality level, leveraging complementary attributes across different modalities without disrupting unimodal achievements. By identifying and enhancing ineffective dimensions through cross-modal guidance, DUC loss is pivotal in harmonizing modality interactions while retaining unique modality strengths.
4. Certainty-aware Logit Weighting Strategy: At the inference stage, DI-MML leverages a certainty-aware logit weighting approach to account for sample-specific modality reliability variations. This mechanism ensures that each modality’s contribution to the final prediction is appropriately weighted based on its predictive confidence, effectively utilizing complementary information at the instance level.

Experimental Validation

The paper substantiates DI-MML's efficacy through comprehensive experiments conducted on several multimodal datasets, including CREMA-D, AVE, UCF101, and ModelNet40, spanning tasks from emotion recognition to action recognition and 3D object classification. Notably, DI-MML consistently outperforms traditional joint multimodal training frameworks by significant margins. It also preserves, if not improves, individual modality performance compared to unimodal baselines, underscoring its ability to mitigate modality competition without compromising the integrity of individual modalities.

In comparison to competing methods such as Modality-Specific Learning Rate (MSLR) and Unimodal Teacher (UMT) approaches, DI-MML consistently achieves superior results both in terms of unimodal and multimodal accuracy. The framework's design, that detaches training objectives and introduces structured cross-modal interactions, distinctly sets it apart from existing solutions attempting to balance modality dynamics through modulation schemes or distillation techniques.

Theoretical and Practical Implications

From a theoretical standpoint, this research elucidates the significance of designing multimodal frameworks that prioritize isolated yet interactive learning to avert competition issues prevalent in traditional MML setups. Practically, the development of DI-MML facilitates the application of MML in complex domains requiring nuanced integration of diverse data sources without the risk of detrimental modality competition.

Future Directions

Looking forward, the trajectory of DI-MML may explore extensions into broader multimodal tasks such as detection and generation, which necessitate refined multimodal interactions beyond classification. Additionally, enhancing dimension-level semantic understanding could provide a novel avenue to optimize feature effectiveness and modality-specific insights further. Such advancements could catalyze the development of even more robust, non-competitive multimodal learning paradigms capable of extracting comprehensive insights from integrated multi-source data.