RRepoGEO

REPOGEO REPORT · LITE

zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation

Default branch master · commit da0357ed · scanned 6/3/2026, 10:12:24 PM

GitHub: 588 stars · 42 forks

AI VISIBILITY SCORE
10 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
0 / 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 zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["awesome-list", "temporal-action-detection", "action-proposal-generation", "computer-vision", "deep-learning", "research-papers", "survey", "weakly-supervised"]
  • highabout#2
    Clarify the repository's nature in the description

    Why:

    CURRENT
    Temporal Action Detection & Weakly Supervised Temporal Action Detection & Temporal Action Proposal Generation
    COPY-PASTE FIX
    A comprehensive awesome list of research papers and resources for Temporal Action Detection, Weakly Supervised Temporal Action Detection, and Temporal Action Proposal Generation.
  • highlicense#3
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository.

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 zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
open-mmlab/mmaction2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. open-mmlab/mmaction2 · recommended 1×
  2. facebookresearch/detectron2 · recommended 1×
  3. CMU-Perceptual-Computing-Lab/openpose · recommended 1×
  4. tensorflow/models · recommended 1×
  5. facebookresearch/pytorchvideo · recommended 1×
  • CATEGORY QUERY
    How can I detect and localize specific human actions within long video recordings?
    you: not recommended
    AI recommended (in order):
    1. MMAction2 (open-mmlab/mmaction2)
    2. Detectron2 (facebookresearch/detectron2)
    3. OpenPose (CMU-Perceptual-Computing-Lab/openpose)
    4. TensorFlow Models (tensorflow/models)
    5. PyTorchVideo (facebookresearch/pytorchvideo)
    6. OpenCV (opencv/opencv)
    7. YOLO (ultralytics/ultralytics)

    AI recommended 7 alternatives but never named zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective algorithms for generating temporal proposals of actions in video analysis?
    you: not recommended
    AI recommended (in order):
    1. Temporal Action Proposals with Stacked Long Short-Term Memory (TAL-SCNN)
    2. Boundary-Matching Network (BMN)
    3. Temporal Segment Networks (TSN)
    4. Structured Segment Networks (SSN)
    5. ActivityNet Captions (ANC)
    6. Context-Aware R-CNN (CAR-CNN)

    AI recommended 6 alternatives but never named zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation?
    pass
    AI did not name zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation — likely talking about a different project

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

  • If a team adopts zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation — likely talking about a different project

    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 zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation solve, and who is the primary audience?
    pass
    AI did not name zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation — likely talking about a different project

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

Embed your GEO score

Drop this badge into the README of zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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zhenyingfang/Awesome-Temporal-Action-Detection-Temporal-Action-Proposal-Generation — 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