Sequential Compositional Generalization in Multimodal Models (2404.12013v1)

Abstract: The rise of large-scale multimodal models has paved the pathway for groundbreaking advances in generative modeling and reasoning, unlocking transformative applications in a variety of complex tasks. However, a pressing question that remains is their genuine capability for stronger forms of generalization, which has been largely underexplored in the multimodal setting. Our study aims to address this by examining sequential compositional generalization using \textsc{CompAct} (\underline{Comp}ositional \underline{Act}ivities)\footnote{Project Page: \url{http://cyberiada.github.io/CompAct}}, a carefully constructed, perceptually grounded dataset set within a rich backdrop of egocentric kitchen activity videos. Each instance in our dataset is represented with a combination of raw video footage, naturally occurring sound, and crowd-sourced step-by-step descriptions. More importantly, our setup ensures that the individual concepts are consistently distributed across training and evaluation sets, while their compositions are novel in the evaluation set. We conduct a comprehensive assessment of several unimodal and multimodal models. Our findings reveal that bi-modal and tri-modal models exhibit a clear edge over their text-only counterparts. This highlights the importance of multimodality while charting a trajectory for future research in this domain.

Evaluation of Multimodal Models on Sequential Compositional Generalization in Egocentric Videos

Introduction to the Study

In this paper, researchers explore the effectiveness of multimodal models in handling sequential compositional generalization, a task based on the CompAct dataset which comprises egocentric kitchen activity videos paired with audio cues and textual descriptions. The focus lies on how well unimodal and multimodal approaches manage to generate and understand novel combinations of previously learned elements.

Description and Significance of the CompAct Dataset

The CompAct dataset was developed specifically for this paper and utilizes sequences from EPIC KITCHENS-100 (EK-100). Each video in this dataset captures unscripted kitchen activities from a first-person perspective and features accompanying audio and textual data. Importantly, the train and test sets exhibit similar distributions of verbs and objects (atoms), but different combinations of these atoms, setting the stage for rigorous evaluation of compositional generalization.

Key Features

• Multimodal Composition: Each instance combines video, audio, and text.
• Compositional Splits: Training on known atoms (verbs/objects) but novel combinations in test settings.
• Rich Annotations: Linguistic descriptions connect closely with the visual and auditory data, offering a trio of synchronized modalities for analysis.

Methodology and Model Evaluation

Researchers undertook a comprehensive assessment of several models ranging from unimodal text-only models to sophisticated multimodal systems integrating video, audio, and text.

Tasks Designed for Assessment

• Next Utterance Prediction: Models predict a textual description for the next unseen video segment based on past sequences.
• Atom Classification: Direct classification of verbs and objects, focusing on recognizing elements in isolation.

Models Tested

Several types of models were considered:

• Baseline Models: These include unimodal and various forms of multimodal configurations (e.g., text-only, video-text, audio-text).
• Pretrained Models: Large-scale models like LLaMA2 and ImageBind, which have been pretrained with extensive multimodal data.

Findings and Implications

The results suggest that multimodal models generally outperform their unimodal counterparts, especially when combining video, audio, and textual modalities. Pretrained models like ImageBind showed significant prowess, likely benefiting from their extensive multimodal pretraining regimes. Notably, the paper found that all models struggled to some degree with true compositional generalization — interpreting entirely novel combinations of familiar elements.

Models' Generalization Capabilities

• Although there were improvements with multimodal inputs, genuine compositional tasks remained challenging.
• Pretrained models did not always perform consistently, indicating possible limitations in their training or adaptation phases.

Future Research and Theoretical Contributions

This paper raises several questions for future research:

• Role of Grounding: How does grounding in real-world audio-visual data affect the learning dynamics and capabilities of generative models?
• Model Architectures: What architectural innovations are necessary to better support compositional generalization?

Final Thoughts

The results underscore the complexity of compositional generalization and highlight the need for further investigation into how multimodal models can be better engineered and trained to handle such tasks effectively. As AI systems increasingly move towards real-world applications, the ability to generalize over novel yet logically related combinations of learned components will be crucial.