Source-linked AI summary
Visual In-Context Learning for Large Vision-Language Models
Yucheng Zhou, Xiang Li, Qianning Wang, Jianbing Shen
TL;DR
LVLM in-context learning is constrained by cross-modal interaction difficulties and separated visual and textual representation spaces. The paper proposes VICL, which retrieves demonstrations, creates intent-oriented image summaries, and composes language-based demonstrations; experiments across five visual reasoning datasets report improved performance and further examine information flow, demonstration design, and in-context unlearning.
Problem
LVLM in-context learning is limited by difficult cross-modal interactions and disparities between visual features and language embeddings.
Method
VICL retrieves and reranks visual demonstrations, generates intent-oriented image summaries, and composes language-based demonstrations using those summaries.
Results
VICL consistently outperforms ICL and Zero-Shot across five datasets and different LVLMs, while information-flow analyses examine its operation across layers and attention heads.
Takeaways & Limitations
Visual summaries reduce demonstration token counts and support more demonstrations, while in-context unlearning shows feasibility for resetting specific knowledge without additional training.
Takeaways & Limitations
VICL depends heavily on LVLM parameter size and training-data scale, and broader or more difficult tasks may require improved strategies and further validation.
Abstract
from arXiv · showhide
In Large Visual Language Models (LVLMs), the efficacy of In-Context Learning (ICL) remains limited by challenges in cross-modal interactions and representation disparities. To overcome these challenges, we introduce a novel Visual In-Context Learning (VICL) method comprising Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented Demonstration Composition. Our approach retrieves images via ''Retrieval & Rerank'' paradigm, summarises images with task intent and task-specific visual parsing, and composes language-based demonstrations that reduce token count and alleviate cross-modal interaction problem. Experimental evaluations on five visual reasoning datasets demonstrate the effectiveness of our method. Moreover, our extensive experiments leverage information flow analysis to elucidate the effectiveness of our method, and investigate the impact of length and position of demonstrations for LVLM. The use of in-context unlearning further shows promise in resetting specific model knowledge without retraining.
1 Introduction
LVLM in-context learning is limited by difficult cross-modal interactions and disparities between visual and textual representations. VICL addresses these challenges through retrieved, intent-oriented visual demonstrations and evaluates their effects across visual reasoning tasks.
- LVLMs extend language-model reasoning to tasks requiring joint understanding of images and text.
- Visual-language interactions occur in deeper LVLM layers, making cross-modal interaction a central obstacle for in-context learning.
- Visual features and language embeddings occupy distinct representation spaces, creating an additional cross-modal gap that limits LVLM in-context learning.
- VICL combines visual demonstration retrieval, intent-oriented image summarization, and language-based demonstration composition.The method retrieves and reranks candidate images, summarizes image-label pairs using task intent and task-specific visual parsing, and replaces demonstration images with summaries.
- Across five image reasoning datasets, the experiments compare VICL with baseline methods and analyze information flow, demonstration order, and demonstration sequence length.The study also examines intent-oriented summaries and the feasibility of in-context unlearning without additional training.
2 Related Work
Related work establishes LVLMs as multimodal systems and presents in-context learning as adaptation through added context rather than parameter optimization. It also motivates in-context unlearning as a parameter-free approach to selectively reset model knowledge.
- Large Vision-Language Models: LVLMs comprehend and generate content across vision and language modalities, supporting tasks involving textual and visual information.
- Large Vision-Language Models: Visual-understanding LVLMs generate textual responses from visual inputs for tasks including image captioning, image question answering, and video understanding.
- Large Vision-Language Models: LVLMs may memorize unnecessary knowledge, creating a need to erase selected information without completely retraining the model.
- In-Context Learning: In-context learning adapts model behavior by adding demonstrations and a new query to the input without optimizing model weights.
- In-Context Learning: One proposed explanation links in-context learning to demonstrations sharing potential concepts within pre-training text with long-range coherence.
- In-Context Unlearning: In-context unlearning applies the in-context learning paradigm without parameter updates, using incorrectly and correctly labeled examples to construct instance-specific prompts.
3 Visual In-Context Learning
VICL extends visual in-context learning with retrieval, intent-oriented summarization, and demonstration composition. It replaces direct image-heavy demonstrations with task-aligned linguistic summaries to address cross-modal interaction challenges and support deeper analysis of information flow.
- Method overview: VICL comprises Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented Demonstration Composition.The pipeline is presented as the paper’s three core components.
- Visual Demonstration Retrieval: Visual Demonstration Retrieval selects candidate images by visual similarity, then reranks them using textual descriptions and cross-modal semantic similarity.A visual encoder retrieves candidates, while an LVLM-generated description and a pretrained image-text model support reranking.
- Intent-Oriented Image Summarization: Intent-Oriented Image Summarization extracts task intent and task-specific visual parsing from reference image, question, and answer triplets.The resulting summaries are intended to replace direct image analysis with pre-aligned interpretations.
- Intent-Oriented Demonstration Composition: Demonstration composition replaces original images with intent-oriented summaries, reducing token count and enabling more demonstrations within the LVLM token limit.This shifts processing toward linguistic representations of visual content grounded in task intent.
- Information Flow Analysis: Information-flow analysis finds that image summaries are most important for label words in shallow layers, while label-word influence on the target position increases in deeper layers.The analysis also reports that label-word influence on the input image diminishes with depth, while word-to-word flow remains relatively stable.
4 Experiments
Experiments evaluate VICL across five image-reasoning datasets, multiple LVLMs, retrieval strategies, demonstration settings, visual summarization methods, and in-context unlearning. VICL consistently outperforms ICL and zero-shot baselines, while intent-oriented summaries, retrieval-and-reranking, demonstration quantity and order, and unlearning behavior are analyzed.
- Experimental Settings: Experiments use EmoSet, Emotion6, UnBiasedEmo, CIFAR10, and MNIST, with accuracy as the metric for all test sets.The study samples 100 demonstration candidates and 1000 test samples for each dataset.
- In-Context Learning: VICL consistently outperforms both ICL and zero-shot across all evaluated models and datasets.The reported gains are especially pronounced for LLaVA-13B, which the authors associate with stronger multimodal reasoning capacity.
- Visual Demonstration Retrieval: “V-Ret + VL-Rank” outperforms other retrieval and reranking strategies on Emotion6, while two-step methods generally exceed random sampling.The comparison contrasts ViT-based retrieval, vision-language reranking, individual strategies, and random selection.
- Impact of Image Demonstration Number: VICL improves more substantially as image demonstrations increase, whereas ICL plateaus or slightly declines beyond three demonstrations.The result indicates diminishing returns for additional ICL demonstrations but stronger use of added demonstrations by VICL.
- Impact of Context Length: Context expansion initially raises VICL accuracy, including an increase from 0.3 to 0.69 on EmoSet, before gains diminish or accuracy slightly declines.Similar trends occur on Emotion6 and UnBiasedEmo, showing a balance between sufficient and excessive context.
- Order of Demonstrations: Positive-label demonstrations perform best at the head of the sequence, followed by the tail, while the middle position performs worst.The authors divide demonstration positions into head, middle, and tail sections.
- Impact of Visual Summarization Method: Intent-oriented image summarization achieves the best performance among standard captioning, task-intent summarization, image-parsing summarization, and IOIS.The authors attribute the improvement to combining task intent with detailed visual parsing.
5 Conclusion
The paper introduces VICL for LVLMs to address cross-modal interaction and representation-space challenges. It combines retrieval, intent-oriented summarization, and demonstration composition, while also extending ICL toward in-context unlearning.
- VICL integrates Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Demonstration Composition for LVLMs.
- VICL streamlines in-context learning while enabling LVLMs to adjust their knowledge base dynamically without retraining.
- Extensive evaluations verify VICL’s effectiveness for processing multimodal tasks and highlight its potential to bridge visual and linguistic modalities.
Limitations
VICL’s effectiveness depends on the underlying LVLM’s parameter size and training-data scale, while broader applications remain open for exploration.
- VICL efficacy depends heavily on LVLM performance, which relies on original parameter size and training-data scale.Validating VICL with larger LVLMs requires additional computational resources.
- Although VICL shows promise for visual reasoning tasks, its broader applications and performance on difficult tasks require further investigation.
A Prompts
The appendix specifies separate Zero-Shot, ICL, and VICL prompts for emotion and image-classification datasets. VICL replaces retrieved demonstration images with their summaries while retaining labels and the target image.
- The three prompting methods are Zero-Shot, ICL, and VICL.
- Zero-Shot prompts ask for an emotion or category label from a single image and a provided label list.Emotion datasets use an emotion question, while CIFAR10 and MNIST use an image-category question.
- ICL prompts provide multiple labeled demonstration images before asking for the target answer.The demonstrations use image-label pairs for both emotion datasets and CIFAR10/MNIST.
- VICL prompts replace demonstration images with summaries while preserving their labels and retain the target image for answering.This structure is specified separately for emotion datasets and CIFAR10/MNIST.
B Visual Summarization Prompt
The visual summarization study compares standard captioning, task-intent descriptions, image parsing, and IOIS. These methods progressively incorporate task purpose and reasoning, with IOIS combining task intent and image parsing.
- The study investigates different visual summarization methods for VICL and their prompts.
- Standard captioning: Standard captioning uses conventional captioning techniques to generate a detailed description of image content.
- Task Intent: Task Intent enriches image descriptions with task-specific intent by using the image and its corresponding label.
- Image Parsing: Image Parsing describes image observations and the reasoning process behind those observations.
- IOIS: IOIS combines Task Intent and Image Parsing to integrate visual content, semantic meaning, and task-oriented purpose.