Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded (1902.03751v2)

Abstract: Many vision and LLMs suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image. In this work, we propose a generic approach called Human Importance-aware Network Tuning (HINT) that effectively leverages human demonstrations to improve visual grounding. HINT encourages deep networks to be sensitive to the same input regions as humans. Our approach optimizes the alignment between human attention maps and gradient-based network importances - ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We apply HINT to Visual Question Answering and Image Captioning tasks, outperforming top approaches on splits that penalize over-reliance on language priors (VQA-CP and robust captioning) using human attention demonstrations for just 6% of the training data.

• The paper introduces HINT, a method aligning model gradients with human attention, yielding an 8-point boost on the VQA-CP dataset.
• The paper applies a gradient-of-gradient step to enforce attention alignment across all layers for enhanced visual grounding.
• The paper demonstrates significant improvements in visual question answering and image captioning using only 6% human attention data for training.

Human Importance-aware Network Tuning (HINT) for Improving Visual Grounding in Vision and LLMs

The paper "Taking a HINT: Leveraging Explanations to Make Vision and LLMs More Grounded" presents a novel methodological approach to enhance the visual grounding capability of vision-and-LLMs. Visual grounding refers to the ability of these models to correctly associate linguistic input with corresponding visual regions in an image, rather than rely on superficial correlations or priors inherent in the data.

Core Proposal and Methodology

The authors introduce a framework called Human Importance-aware Network Tuning (HINT). This framework leverages human annotations as a means to direct model attention towards semantically relevant visual regions, thereby enhancing visual grounding. The proposed approach involves aligning gradient-based importance scores—the derivatives indicating the sensitivity of model outputs with respect to its inputs—with human attention maps, which depict regions of an image deemed important by human annotators. Unlike typical attention mechanisms that only influence intermediate layers, HINT enforces attention alignment with a gradient-of-gradient step. This ensures that the model sensitivity is adjusted throughout all layers, integrating a human-centric grounding into the model's decision process.

Numerical Results

The paper reports significant performance gains when applying HINT to tasks such as Visual Question Answering (VQA) and Image Captioning. In particular, the approach demonstrates a substantial improvement of 8 percentage points on the VQA-CP dataset—a variant of VQA specifically designed to challenge models on visual grounding by altering answer distributions between training and testing phases. Moreover, HINT achieves these results using human attention data for only 6% of the training set, underscoring the efficiency and impact of the proposed method.

Comparison with Existing Methods

HINT was compared against several leading methods: Grounded VQA (GVQA), which disentangles vision components from language priors; Adversarial Regularization (AdvReg), which uses adversarial techniques to reduce reliance on language priors; and a baseline human attention alignment approach that directly supervises the model's attention maps. HINT outperformed these methods, particularly in scenarios that penalize over-reliance on language biases. This suggests HINT's potential in not just focusing model attention but ensuring its integration into the prediction-making process.

Implications and Future Directions

The implications of this research are multifaceted:

• Practical Implications: The method enhances model robustness and interpretability, which are critical for applications where explainability is paramount, such as autonomous driving or medical diagnosis.
• Theoretical Contributions: The exploration and validation of gradient-based adjustments underscore the importance of integrating human-centric supervision directly into the learning process, which could inspire further innovations in multimodal learning frameworks.
• Future Developments: Looking forward, this approach could be applied to a broader array of AI systems requiring robust visual-linguistic reasoning, possibly extending into the domain of video understanding or more complex human-robot interaction scenarios.

In conclusion, the introduction of HINT represents a noteworthy advancement in leveraging human explanations to refine the visual grounding of models dealing with vision and language tasks. This paper opens avenues for crafting AI models that align more closely with human cognitive processes, thereby bridging gaps between human intuition and machine computation.