REVIEW 6 cited by
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present an interactive visual framework named InternGPT, or iGPT for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, with non-verbal instructions like pointing movements that enable users to directly manipulate images or videos on the screen. Pointing (including gestures, cursors, etc.) movements can provide more flexibility and precision in performing vision-centric tasks that require fine-grained control, editing, and generation of visual content. The name InternGPT stands for \textbf{inter}action, \textbf{n}onverbal, and \textbf{chat}bots. Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2. Additionally, in iGPT, an auxiliary control mechanism is used to improve the control capability of LLM, and a large vision-language model termed Husky is fine-tuned for high-quality multi-modal dialogue (impressing ChatGPT-3.5-turbo with 93.89\% GPT-4 Quality). We hope this work can spark new ideas and directions for future interactive visual systems. Welcome to watch the code at https://github.com/OpenGVLab/InternGPT.
Forward citations
Cited by 6 Pith papers
-
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
The work introduces the UAV Reasoning Segmentation task, the DRSeg benchmark dataset, and PixDLM as a baseline dual-path multimodal language model for reasoning-based segmentation in aerial imagery.
-
GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning
A generative multimodal process reward model that produces step-level critiques and corrections improves average math accuracy for six multimodal LLMs by 2.9 to 5.9 points under a refinement-based Best-of-N strategy.
-
LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance
LIRA improves referring segmentation and reduces hallucination in multimodal LLMs by fusing semantic and pixel features and interleaving local image regions with text descriptions.
-
Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces
VeBrain unifies perception, spatial reasoning, and robot control in one MLLM by representing control as keypoint detection plus skill selection, with a robotic adapter for deployment.
-
Docopilot: Improving Multimodal Models for Document-Level Understanding
A new academic-paper dataset and a retrieval-free fine-tuned InternVL2 model improve multi-page document QA accuracy and latency on several benchmarks.
-
Vision Generalist Model: A Survey
A structured review of vision generalist models, classifying them into encoding-based and sequence-to-sequence frameworks and summarizing datasets, benchmarks, techniques, and open problems.
Discussion (0). Sign in to comment.