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ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Liang Zhao, En Yu, Zheng Ge, Jinrong Yang, Haoran Wei, Hongyu Zhou, Jianjian Sun, Yuang Peng, Runpei Dong, Chunrui Han, Xiangyu Zhang
TL;DR
Existing end-to-end MLLMs rely mainly on language instructions, limiting precise interaction with regions in complex scenes. ChatSpot introduces precise referring instructions, a unified multimodal model, and multi-grained training data for region-aware interaction. The paper reports outstanding performance in interactive chatting and downstream tasks, while noting limitations in output boxes, special-symbol recognition, catastrophic forgetting, and evaluation.
Problem
Existing end-to-end MLLMs limit interaction to language instructions, making precise region reference difficult in complex scenes.
Method
ChatSpot combines precise referring instructions using points and boxes with a unified end-to-end MLLM and the MGVLID multi-grained instruction-following dataset.
Results
ChatSpot demonstrates outstanding performance in interactive chatting and downstream tasks, including 64.5% accuracy with COCO-1000 ground-truth boxes and 39.6% AP with DINO-generated boxes.
Takeaways & Limitations
Combining precise referring instructions with MLLMs stimulates special-region understanding and reasoning.
Takeaways & Limitations
ChatSpot lacks referential output boxes and recognition of some special symbols, and fine-tuning on new datasets can cause catastrophic forgetting.
Abstract
from arXiv · showhide
Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. This enables MLLMs to focus on the region of interest and achieve finer-grained interaction. Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience. We also construct a multi-grained vision-language instruction-following dataset based on existing datasets and GPT-4 generating. Furthermore, we design a series of evaluation tasks to assess the effectiveness of region recognition and interaction. Experimental results showcase ChatSpot's promising performance.
1 Introduction
Existing end-to-end MLLMs primarily support language-only interaction, which makes precise communication difficult in complex scenes. ChatSpot addresses this gap with region-referencing interaction, a multi-grained instruction dataset, and evaluation tasks for region understanding.
- Region prompts such as points and boxes let MLLMs focus on a region of interest when language alone cannot precisely describe a complex-scene requirement.
- ChatSpot is a unified end-to-end MLLM that supports language, clicking, and box-drawing interactions for whole-image or region-specific tasks.
- Precise referring supports fine-grained applications such as identifying jersey numbers and analyzing facial expressions in selected regions.
- The system can provide region-specific analysis and refined instructions after an intelligent agent locates a target or region of interest.
- The work combines precise referring instruction tuning, MGVLID with around 1.2M images and 3M query-answer pairs, and evaluation tasks for model effectiveness.
2 Related Works
Large language models evolved from foundational architectures toward large-scale zero-shot learning, instruction following, and human-AI interaction. Their capabilities also motivate interactive systems that use either plugins or end-to-end multimodal integration.
- 2.1 Large Language Models: GPT-3 introduced 175-billion-parameter language modeling with strong zero-shot performance, while later models extended open-source and application-specific LLM development.
- 2.1 Large Language Models: RLHF-based instruction alignment produced InstructGPT and ChatGPT, which demonstrated powerful human-AI interaction capabilities.
- 2.2 Interactive Systems: LLMs can serve as central controllers that schedule modality-specific tools for tasks such as VQA, image editing, and image captioning.
- 2.2 Interactive Systems: Interactive systems are organized around plugin-based methods that invoke external tools and end-to-end interaction approaches.
3 Methods
ChatSpot combines an image encoder, language decoder, and modality-alignment projector with precise referring instructions that encode selected points or regions alongside language. Its MGVLID dataset supplies both image-level and region-level instruction data for multi-grained interaction.
- 3.1 Overall Architecture: ChatSpot uses an image encoder, decoder-only LLM, and modality-alignment block, with an MLP aligning visual tokens to the language space.
- 3.1 Overall Architecture: Users select points or boxes with mouse gestures, combine the region coordinates with language instructions, and receive an autoregressive response from the LLM decoder.
- 3.2 Precise Referring Instruction: Precise referring instructions unify image-level and region-level task descriptions by encoding selected region coordinates as normalized text tokens.
- 3.2 Precise Referring Instruction: The region representation uses freely chosen numbers of points, enabling multi-grained references including points, boxes, and polygons.
- 3.3 Dataset Construction: The dataset pipeline uses captions and bounding boxes with GPT-4 to generate diverse region-level conversations, supplemented by PointQA data for point-wise tuning.
- 3.3 Dataset Construction: MGVLID combines image-text and region-text instruction data from 11 source datasets, holding out 4 datasets for evaluation.
4 Experiments
ChatSpot is evaluated as a shared-parameter generalist on region recognition, OCR, VQA, and qualitative interaction tasks. Results show zero-shot region understanding and region-grounded perception, generation, OCR, and spatial reasoning.
- Task Evaluation: The shared-parameter generalist supports regional classification, OCR, and VQA by changing language instructions across tasks.The experiments evaluate region recognition and zero-shot ability without separate task-specific model parameters.
- Regional Classification: 64.5% accuracy on COCO-1000 with GT boxes and 39.6% AP with DINO-generated boxes demonstrate zero-shot regional classification.The evaluation uses 1,000 COCO validation images and no COCO annotations.
- Regional Optical Character Recognition: 31.8% accuracy on the COCO Text validation set demonstrates ChatSpot’s regional OCR ability.The model receives dataset-provided text-region boxes and answers which text appears in each region.
- Region Perception Ability: Qualitative cases show that point or box prompts let ChatSpot recognize regions at different granularities and perceive features such as a bear’s wet hair.The examples connect fine-grained region perception with information useful for refined robotic operations.
- Content Generation Capability: ChatSpot generates region-related explanations and suggestions, including precautions for cutting a recognized lemon, beyond directly visible content.The example first identifies the object in the selected region and then produces additional guidance.
- Optical Character Recognition (OCR) Ability: Qualitative examples show OCR and spatial reasoning: ChatSpot reads numbers in context, infers that a refrigerator is powered, and gives instructions for retrieving a Coke.The reasoning example combines region recognition with contextual status inference and downstream action guidance.
5 Discussion
ChatSpot’s discussion evaluates robustness in region selection and identifies limitations affecting recognition, output capabilities, continual adaptation, and benchmarking.
- 5.1 Robustness of Region Referring: ChatSpot’s robustness analysis tests randomly added box noise at scales 0.1, 0.2, and 0.3 because users may annotate regions inaccurately.
- 5.3 Limitations: ChatSpot lacks referential output boxes and recognition for some special symbols, including license plate numbers, partly because of insufficient training data.
- 5.3 Limitations: Fine-tuning ChatSpot on a new dataset can cause catastrophic forgetting, creating an overall performance bottleneck.
- 5.3 Limitations: Region-recognition evaluation remains an open problem because predefined-box evaluation differs from traditional vision-language evaluation and lacks a comprehensive automatic benchmark.
6 Conclusion
The paper proposes precise referring instruction tuning and ChatSpot, an end-to-end MLLM supporting point- and box-based region prompts. Trained on MGVLID, ChatSpot shows strong interactive-chatting and downstream-task performance, suggesting that precise referring improves special-region understanding and reasoning.
- 6 Conclusion: ChatSpot combines precise referring instructions with point and box prompts in a fully end-to-end MLLM for region interaction.
- 6 Conclusion: MGVLID is a large-scale multi-grained vision-language instruction-following dataset used to train ChatSpot.
- 6 Conclusion: ChatSpot demonstrates outstanding performance in interactive chatting and downstream tasks after training on MGVLID.
- 6 Conclusion: The results suggest that combining precise referring instructions with MLLMs stimulates special-region understanding and reasoning.
A.1 More Interactive Cases
Additional dialogue cases show ChatSpot supporting interaction at the full-image, region-box, and region-point levels.
- A.1 More Interactive Cases: ChatSpot supports multiple interaction levels, including the full image, region boxes, and region points.
- A.1 More Interactive Cases: The paper states that future versions will support more diverse interaction forms.
A.2 Failure Cases Analysis
The failure-case analysis attributes difficulties to limited data and instructions, especially for license-plate recognition and precise color identification in complex backgrounds.
- A.2 Failure Cases Analysis: Limited license-plate recognition data in MGVLID leads to unsatisfactory performance on license-plate recognition.
- A.2 Failure Cases Analysis: Complex backgrounds can make ChatSpot inaccurately identify the precise colors of regions or objects.
- A.2 Failure Cases Analysis: The authors propose adding specific instructions and developing a broader, balanced dataset to address these failure cases.
- A.2 Failure Cases Analysis: The appendix includes additional interaction examples and failure cases for license-plate and color recognition.