#iccv2025 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #iccv2025, aggregated by home.social.
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Lidar waveforms are worth 40x128x33 words
#HackerNews #Lidar #Waveforms #LidarTechnology #DataScience #ICCV2025 #ResearchInnovation
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Calling all #ICCV2025 attendees! We're hosting a Reve social where you can meet our team and unwind on Tuesday evening. RSVP here, limited spots remaining: luma.com/9dy5x3j4
Reve ICCV Social · Luma -
Apple a ICCV 2025: 19-23 ottobre ad Honolulu. 8 paper accettati e Dr. C. Thomas ospite del workshop VISION.
🌟 8 lavori in mostra
🚀 Keynote d’apertura 19 ottobre#ICCV2025 #ComputerVision #AppleAI
https://machinelearning.apple.com/updates/apple-at-iccv-2025#papers
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MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
In manufacturing, quality control remains a critical yet complex task, especially when multiple defect types are involved. MultiADS introduces a system capable of detecting and segmenting a wide range of anomalies (e.g., scratches, bends, holes), even in zero-shot settings.
By combining visual analysis with descriptive textual input and using a curated Knowledge Base for Anomalies, MultiADS generalizes to unseen defect types without requiring prior visual examples and consistently outperforms state-of-the-art models across several benchmarks, offering a robust and scalable solution for industrial inspection tasks.
Sadikaj, Y., Zhou, H., Halilaj, L., Schmid, S., Staab, S., & Plant, C. MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning. International Conference on Computer Vision, ICCV 2025, Hawai, Oct 19-23, 2025, #ICCV2025. https://arxiv.org/abs/2504.06740.
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🎉 Researchers from our AI institute at the University of Stuttgart @Uni_Stuttgart will present two papers tackling real-world challenges with AI:
- "Making the Web More Inclusive: Enter AccessGuru" (#ASSETS2025).
- "MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning" (#ICCV2025).