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T-Rex: Counting by Visual Prompting

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arxiv 2311.13596 v1 pith:FKMOISAD submitted 2023-11-22 cs.CV

Qing Jiang , Feng Li , Tianhe Ren , Shilong Liu , Zhaoyang Zeng , Kent Yu , Lei Zhang This is my paper · ORCID
classification cs.CV
keywords countingt-rexobjectsvisualobjectpromptingpotentialresults
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We introduce T-Rex, an interactive object counting model designed to first detect and then count any objects. We formulate object counting as an open-set object detection task with the integration of visual prompts. Users can specify the objects of interest by marking points or boxes on a reference image, and T-Rex then detects all objects with a similar pattern. Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects. T-Rex has achieved state-of-the-art performance on several class-agnostic counting benchmarks. To further exploit its potential, we established a new counting benchmark encompassing diverse scenarios and challenges. Both quantitative and qualitative results show that T-Rex possesses exceptional zero-shot counting capabilities. We also present various practical application scenarios for T-Rex, illustrating its potential in the realm of visual prompting.

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Cited by 5 Pith papers

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

  1. DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    Global prompt integration, visual-textual relation distillation and selective fusion make visual prompts discriminative enough for DETR-ViP to beat prior visual-prompt detectors by several mAP points.

  2. DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models

    cs.CV 2025-05 conditional novelty 7.0 of 10

    DINO-R1 trains visual-prompt detectors with group-relative query rewards and KL regularization, improving zero-shot and fine-tuned detection over supervised fine-tuning.

  3. Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Training a multimodal LLM on GPT-4o-generated chain-of-thought referring traces, then optimizing with GRPO, improves referring accuracy and abstention on HumanRef.

  4. Expanding Zero-Shot Object Counting with Rich Prompts

    cs.CV 2025-05 conditional novelty 5.0 of 10

    RichCount improves zero-shot object counting by enriching text prompts with MLLM-generated descriptions and aligning them to CLIP visual features, achieving state-of-the-art mean absolute error on three counting benchmarks.

  5. Cotton-SF YOLO: Learning Structural and Frequency Cues for Early Cotton Square Detection in Complex Field Environments

    cs.CV 2026-07 conditional novelty 4.0 of 10

    Adding dynamic-snake-convolution and FFT-modulation modules to YOLO26m raises cotton-square detection mAP50 from 0.810 to 0.820, mAP50:95 from 0.478 to 0.494, and recall from 0.771 to 0.794 on a new field dataset.

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