Feedback-Driven Vision-Language Alignment with Minimal Human Supervision (2501.04568v2)

Abstract: Vision-LLMs (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for extensive, high-quality image-text training data. Curation of these image-text pairs is both time-consuming and computationally expensive. To address this challenge, we introduce SVP (Sampling-based Visual Projection), a novel framework that enhances vision-language alignment without relying on manually curated text-image pairs or preference annotation. SVP leverages a small set of manually selected images, self-captioning and a pre-trained grounding model as a feedback mechanism to elicit latent information in VLMs. We evaluate our approach across six key areas: captioning, referring, visual question answering, multitasking, hallucination control, and object recall. Results demonstrate significant improvements, including a 14 % average improvement in captioning tasks, up to 12 % increase in object recall, and significantly reduced hallucinations, while maintaining question-answering capabilities. Using SVP, a small VLM achieves hallucination reductions similar to a model five times larger, while a VLM with initially poor referring capabilities more than doubles its performance, approaching parity with a model twice its size.

• The paper presents SVP, a model-agnostic framework that uses self-captioning and grounding feedback to align visual and linguistic modalities without curated datasets.
• Empirical results show up to a 14% improvement in captioning tasks and a 12% increase in object recall, along with reduced hallucination rates.
• The approach enables smaller models to perform comparably to larger ones, promoting sustainable AI development through efficient, self-generated data for training.

Supervision-free Vision-Language Alignment: Advancing Multimodal Understanding through SVP

The paper "Supervision-free Vision-Language Alignment" by Giannone et al. proposes an innovative approach to enhancing vision-LLMs (VLMs) through a novel framework called Supervision-free Visual Projection (SVP). VLMs traditionally rely heavily on meticulously curated image-text pairs, which can be resource-intensive to prepare. The development of SVP marks a significant step towards streamlining this process by reducing the dependency on curated datasets.

Core Contributions and Methodology

SVP is a model-agnostic framework that addresses the challenge of aligning visual and linguistic modalities in VLMs without relying on supervision. The framework integrates self-captioning with grounding feedback to improve model outputs. Grounding, in this context, refers to a mechanism for connecting low-level visual elements with high-level linguistic representations, akin to how humans use tangible examples to connect sensory inputs with language.

The paper delineates a three-step process for SVP:

1. Inner-Loop Sampling: The authors generate numerous image descriptions using a pre-trained VLM and enhance these descriptions with feedback from a grounding model, such as GroundingDINO. The grounding model provides spatially enriched outputs that serve as additional context for improved description generation.
2. Scoring and Filtering: The generated captions are scored based on their alignment with the grounding feedback, using techniques inspired by variational inference. High-score samples are deemed more aligned with the visual input and are thus selected for model adaptation.
3. Outer-Loop Model Adaptation: The selected samples serve as a self-generated dataset for adapting the base VLM, employing parameter-efficient fine-tuning methods like LoRa.

Numerical Results

The empirical evaluations demonstrate significant improvements across a suite of vision-language tasks. SVP-enhanced models showed an average of 14\% improvement in captioning tasks and up to 12\% increase in object recall. Notably, SVP achieved a marked reduction in hallucination rates, with a model using SVP performing comparably to models five times larger in size. These gains underscore the framework's capacity to enhance small-scale models' performance to the level of larger models.

Theoretical and Practical Implications

Theoretically, this work emphasizes the efficacy of integrating grounding feedback into language generation, presenting a model-agnostic method that refines the visual-text duality in VLMs. Grounding, as a feedback system, elicits latent information by leveraging models' spatial and positional reasoning capabilities. This insight challenges the conventional reliance on large-scale annotated datasets and preference-based methods, proposing a paradigm where models self-align using intrinsic feedback.

Practically, SVP's reduction in data dependency heralds a shift towards more sustainable AI development. The potential for VLMs to learn from self-generated, yet grounded, datasets could democratize access to advanced AI, as resource constraints are alleviated. Furthermore, the diminished hallucinatory behavior of VLMs post-SVP adaptation enhances their applicability in critical domains, including healthcare and autonomous navigation.

Future Prospects

While SVP demonstrates promising results, future work could explore its scalability across more diverse datasets and its application in real-time computational scenarios. Investigating the integration of SVP in end-to-end pipelines might further reveal its impact on enhancing prediction accuracy and interpretability.

In summary, the introduction of Supervision-free Visual Projection provides a fresh perspective on advancing vision-LLMs without substantial manual intervention, thereby contributing significantly to the field of multimodal AI.