RRepoGEO

REPOGEO REPORT · LITE

JIA-Lab-research/Seg-Zero

Default branch main · commit 55077202 · scanned 6/12/2026, 3:28:23 AM

GitHub: 629 stars · 30 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface JIA-Lab-research/Seg-Zero, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify Seg-Zero's role as a research project/model in the README opening

    Why:

    CURRENT
    # Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

The repo is the official implement of "Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement".
    COPY-PASTE FIX
    # Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

Seg-Zero is a novel research project and model that performs zero-shot image segmentation by generating reasoning chains and leveraging cognitive reinforcement learning. This repository provides the official implementation for "Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement".
  • hightopics#2
    Add more specific topics to differentiate from generic ML frameworks

    Why:

    CURRENT
    multimodal, multimodel-large-language-model, reasoning-language-models, reinforcement-learning, segmentation
    COPY-PASTE FIX
    multimodal, multimodel-large-language-model, reasoning-language-models, reinforcement-learning, segmentation, zero-shot-segmentation, open-vocabulary, vision-language-model, cognitive-reinforcement
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://jia-lab-research.github.io/Seg-Zero/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface JIA-Lab-research/Seg-Zero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. pytorch_geometric/pytorch_geometric · recommended 1×
  3. deepmind/graph_nets · recommended 1×
  4. deepmind/sonnet · recommended 1×
  5. google/jax · recommended 1×
  • CATEGORY QUERY
    How to perform image segmentation using reasoning chains and reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Geometric (pytorch_geometric/pytorch_geometric)
    2. DeepMind's Graph Nets library (deepmind/graph_nets)
    3. Sonnet (deepmind/sonnet)
    4. JAX (google/jax)
    5. OpenNARS (opennars/opennars)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. Gymnasium (Farama-Foundation/Gymnasium)
    8. RLlib (ray-project/ray)
    9. Ray (ray-project/ray)
    10. DeepMind's Acme (deepmind/acme)
    11. Mask R-CNN
    12. Detectron2 (facebookresearch/detectron2)
    13. YOLOv8 (ultralytics/ultralytics)
    14. SAM (Segment Anything Model) (facebookresearch/segment-anything)
    15. Grounding DINO (IDEA-Research/GroundingDINO)

    AI recommended 15 alternatives but never named JIA-Lab-research/Seg-Zero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable segmentation models trained without supervised reasoning data, using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Stable-Baselines3
    3. Ray RLlib
    4. TensorFlow
    5. TF-Agents
    6. Keras-RL
    7. OpenAI Gym
    8. Farama Gymnasium
    9. MONAI
    10. Unity ML-Agents

    AI recommended 10 alternatives but never named JIA-Lab-research/Seg-Zero. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of JIA-Lab-research/Seg-Zero?
    pass
    AI named JIA-Lab-research/Seg-Zero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts JIA-Lab-research/Seg-Zero in production, what risks or prerequisites should they evaluate first?
    pass
    AI named JIA-Lab-research/Seg-Zero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo JIA-Lab-research/Seg-Zero solve, and who is the primary audience?
    pass
    AI named JIA-Lab-research/Seg-Zero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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JIA-Lab-research/Seg-Zero — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite