Reconstructive Visual Instruction Tuning (2410.09575v1)

Abstract: This paper introduces reconstructive visual instruction tuning (ROSS), a family of Large Multimodal Models (LMMs) that exploit vision-centric supervision signals. In contrast to conventional visual instruction tuning approaches that exclusively supervise text outputs, ROSS prompts LMMs to supervise visual outputs via reconstructing input images. By doing so, it capitalizes on the inherent richness and detail present within input images themselves, which are often lost in pure text supervision. However, producing meaningful feedback from natural images is challenging due to the heavy spatial redundancy of visual signals. To address this issue, ROSS employs a denoising objective to reconstruct latent representations of input images, avoiding directly regressing exact raw RGB values. This intrinsic activation design inherently encourages LMMs to maintain image detail, thereby enhancing their fine-grained comprehension capabilities and reducing hallucinations. Empirically, ROSS consistently brings significant improvements across different visual encoders and LLMs. In comparison with extrinsic assistance state-of-the-art alternatives that aggregate multiple visual experts, ROSS delivers competitive performance with a single SigLIP visual encoder, demonstrating the efficacy of our vision-centric supervision tailored for visual outputs.

• The paper introduces a dual-objective framework that combines text generation with latent-space reconstruction to improve visual comprehension.
• The approach uses a denoising objective on latent image representations to capture fine-grained details and reduce visual hallucinations.
• Empirical results demonstrate competitive performance across various LMM architectures using a simpler single-encoder setup.

Reconstructive Visual Instruction Tuning (ROSS) introduces a novel approach for training Large Multimodal Models (LMMs) by incorporating vision-centric supervision signals alongside traditional text-based supervision (Reconstructive Visual Instruction Tuning, 12 Oct 2024). This method aims to enhance the model's visual understanding capabilities by explicitly training it to reconstruct aspects of the input image, thereby leveraging the rich, detailed information inherent in the visual modality that is often underutilized in standard visual instruction tuning paradigms.

Methodology: Vision-Centric Supervision via Reconstruction

Conventional visual instruction tuning primarily focuses on supervising the textual outputs generated by the LMM in response to visual inputs and instructions. This involves minimizing a loss function based on the predicted text tokens compared to the ground-truth text. While effective for aligning the model's language capabilities with visual understanding, this approach neglects direct supervision on the visual processing pathway itself. Consequently, fine-grained visual details might be lost or inadequately represented internally.

ROSS addresses this limitation by introducing an auxiliary objective: reconstructing the input image. The core idea is to prompt the LMM not only to generate text but also to generate a representation that allows for the reconstruction of the visual input. This forces the model to retain and utilize more detailed visual information throughout its internal processing stages.

A key challenge in implementing this reconstruction objective lies in the high degree of spatial redundancy present in natural images. Directly regressing raw RGB pixel values can be inefficient and may lead the model to focus on low-level details or statistically common patterns rather than semantically meaningful content. Furthermore, such direct regression can be computationally intensive and may not provide robust feedback signals for high-level visual understanding.

To circumvent these issues, ROSS employs a denoising objective focused on reconstructing latent representations of the input image, rather than the raw pixels. This involves:

1. Obtaining a latent representation of the input image, typically from an intermediate layer of the visual encoder or a separate autoencoder.
2. Applying noise to this latent representation.
3. Training the LMM to denoise this representation, effectively reconstructing the original latent vector.

The loss function for ROSS combines the standard text generation loss $L_{text}$ with this new reconstructive loss $L_{recon}$:

$L_{total} = L_{text} + \lambda L_{recon}$

where $\lambda$ is a hyperparameter balancing the two objectives. The reconstructive loss, $L_{recon}$, operates on the latent space, often using a distance metric like Mean Squared Error (MSE) between the predicted latent vector $\hat{z}$ and the original latent vector $z$:

$L_{recon} = || z - \hat{z} ||^2$

This latent-space denoising objective encourages the model to capture the essential semantic and structural information of the image while being less sensitive to pixel-level variations or noise. It acts as an "intrinsic activation" mechanism, compelling the LMM to maintain fidelity to the visual input throughout its processing pipeline.

Advantages and Implications

The primary advantage of incorporating this reconstructive objective is the enhanced preservation of fine-grained visual detail within the LMM. By requiring the model to reconstruct visual aspects, ROSS encourages the internal representations to be more faithful to the input image. This translates to several practical benefits:

1. Improved Fine-Grained Comprehension: Models trained with ROSS are expected to exhibit better performance on tasks requiring detailed visual understanding, such as identifying small objects, describing subtle differences between images, or answering questions about specific image regions.
2. Reduced Hallucinations: Visual hallucinations, where the model generates text describing objects or attributes not present in the image, often stem from a disconnect between the textual generation process and the visual grounding. By strengthening the visual grounding through reconstruction, ROSS aims to mitigate such hallucinations, ensuring generated text is more consistent with the visual input.
3. Enhanced Visual Representation Learning: The reconstruction task provides an additional supervisory signal directly to the visual processing components and the vision-language interface within the LMM, potentially leading to more robust and informative multimodal representations.

Empirical Validation

The effectiveness of ROSS was demonstrated empirically across various experimental setups (Reconstructive Visual Instruction Tuning, 12 Oct 2024). Key findings include:

• Consistent Improvements: ROSS consistently yielded significant performance gains when applied to different combinations of base LMM architectures, involving various visual encoders and LLMs. This suggests the approach is broadly applicable and not overly sensitive to specific model choices.
• Competitive Performance: Notably, LMMs trained using ROSS with a single SigLIP visual encoder achieved performance competitive with state-of-the-art alternatives that rely on extrinsic assistance. These alternative methods often aggregate outputs from multiple specialized visual expert models (e.g., object detectors, OCR systems) to enhance performance. The ability of ROSS to achieve similar results with a simpler, single-encoder setup highlights the efficacy of its vision-centric supervision strategy in intrinsically boosting the model's capabilities without external modules.

These results underscore the value of leveraging the input image itself as a supervisory signal through latent reconstruction, offering a compelling alternative or complement to text-only supervision and multi-expert fusion techniques.

Conclusion

Reconstructive Visual Instruction Tuning (ROSS) presents a methodological advancement in training LMMs by integrating a vision-centric supervision signal based on reconstructing latent representations of the input image via a denoising objective. This approach encourages the retention of fine-grained visual details, leading to improved comprehension, reduced hallucinations, and competitive performance compared to more complex multi-expert systems. Its demonstrated effectiveness across different model configurations suggests its potential as a valuable technique for developing more capable and visually grounded LMMs.