Pith. sign in

REVIEW 11 cited by

DetGPT: Detect What You Need via Reasoning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2305.14167 v2 pith:WUCOJB7T submitted 2023-05-23 cs.CV cs.AI

Renjie Pi , Jiahui Gao , Shizhe Diao , Rui Pan , Hanze Dong , Jipeng Zhang , Lewei Yao , Jianhua Han
show 3 more authors
Hang Xu Lingpeng Kong Tong Zhang
This is my paper
classification cs.CVcs.AI
keywords objectdetgptdetectionmodelsparadigmproposeduserapproach
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In recent years, the field of computer vision has seen significant advancements thanks to the development of large language models (LLMs). These models have enabled more effective and sophisticated interactions between humans and machines, paving the way for novel techniques that blur the lines between human and machine intelligence. In this paper, we introduce a new paradigm for object detection that we call reasoning-based object detection. Unlike conventional object detection methods that rely on specific object names, our approach enables users to interact with the system using natural language instructions, allowing for a higher level of interactivity. Our proposed method, called DetGPT, leverages state-of-the-art multi-modal models and open-vocabulary object detectors to perform reasoning within the context of the user's instructions and the visual scene. This enables DetGPT to automatically locate the object of interest based on the user's expressed desires, even if the object is not explicitly mentioned. For instance, if a user expresses a desire for a cold beverage, DetGPT can analyze the image, identify a fridge, and use its knowledge of typical fridge contents to locate the beverage. This flexibility makes our system applicable across a wide range of fields, from robotics and automation to autonomous driving. Overall, our proposed paradigm and DetGPT demonstrate the potential for more sophisticated and intuitive interactions between humans and machines. We hope that our proposed paradigm and approach will provide inspiration to the community and open the door to more interative and versatile object detection systems. Our project page is launched at detgpt.github.io.

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs

    cs.CV 2025-02 unverdicted novelty 7.0 of 10

    WorldSense provides the first benchmark requiring synergistic audio-video-text understanding on 1,662 real-world videos and 3,172 QA pairs, where the best current multimodal LLM reaches only 65.1% accuracy.

  2. DriveQA: Passing the Driving Knowledge Test

    cs.CV 2025-08 conditional novelty 6.0 of 10

    DriveQA is a new multimodal driving-knowledge benchmark showing that LLMs and MLLMs struggle with right-of-way, numerical traffic rules, and sign variations, with modest transfer gains to nuScenes and BDD.

  3. Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence

    cs.CV 2025-05 unverdicted novelty 6.0 of 10

    Spatial-MLLM adds a 3D spatial encoder initialized from a visual geometry model and space-aware frame sampling to MLLMs to improve spatial understanding and reasoning from purely 2D visual inputs.

  4. Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence

    cs.CV 2025-05 unverdicted novelty 6.0 of 10

    Spatial-MLLM boosts MLLM spatial intelligence from 2D inputs via dual encoders initialized from geometry models plus space-aware sampling, claiming state-of-the-art results.

  5. MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

    cs.CV 2024-01 conditional novelty 6.0 of 10

    MoE-LLaVA applies mixture-of-experts sparsity to LVLMs via MoE-Tuning, delivering LLaVA-1.5-7B level visual understanding and better hallucination resistance with only ~3B active parameters.

  6. Towards Sparse Video Understanding and Reasoning

    cs.CV 2026-02 conditional novelty 5.0 of 10

    A video-QA agent that carries only a structured text summary between rounds beats dense-frame baselines on accuracy while using a handful of frames per video.

  7. Efficient Reasoning with Hidden Thinking

    cs.CL 2025-01 unverdicted novelty 5.0 of 10

    Heima compresses verbose CoT into hidden thinking tokens via information-theoretic analysis and an adaptive interpreter, claiming maintained or improved zero-shot accuracy on reasoning benchmarks.

  8. InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    InternVideo3 introduces Multimodal Contextual Reasoning and M^2LA attention to enable closed-loop evidence accumulation in long-video understanding and agentic tool use, reporting strong benchmark results.

  9. MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

    cs.CV 2024-02 unverdicted novelty 4.0 of 10

    MobileVLM V2 shows that 1.7B and 3B parameter vision-language models can reach or exceed the performance of 3B and 7B+ models on common VLM benchmarks via targeted design and data improvements.

  10. A Survey on Multimodal Large Language Models

    cs.CV 2023-06 accept novelty 3.0 of 10

    This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.

  11. A Comprehensive Overview of Large Language Models

    cs.CL 2023-07 unverdicted novelty 2.0 of 10

    A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.

Pith tools