{"product_id":"computer-vision-eccv-2024-workshops-9783031919060","title":"Computer Vision  ECCV 2024 Workshops","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- TONO: a synthetic dataset for face image compliance to ISO\/ICAO standard.\u003cbr\u003e.- mproving Post-Earthquake Crack Detection using Semi-Synthetic Gener ated Images.\u003cbr\u003e.- DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition.\u003cbr\u003e.- Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis.\u003cbr\u003e.- RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models.\u003cbr\u003e.- NeRFmentation: NeRF-based Augmentation for Monocular Depth Estima tion.\u003cbr\u003e.- Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition.\u003cbr\u003e.- Time-Resolved MNIST Dataset for Single-Photon Recognition.\u003cbr\u003e.- NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Hu man Pose Estimation in Top-View Fisheye Images.\u003cbr\u003e.- Training and Benchmarking Leukocyte Sub-types Classification Methods with Synthetic Images.\u003cbr\u003e.- DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling.\u003cbr\u003e.- Contextual Knowledge Pursuit for Faithful Visual Synthesis.\u003cbr\u003e.- SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models.\u003cbr\u003e.- Diffusion-based Synthetic Dataset Generation for Egocentric 3D Human Pose Estimation.\u003cbr\u003e.- BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabil ities in Pretrained Diffusion Models.\u003cbr\u003e.- A CycleGAN Model to Synthesize Missing and Unpaired MRI Sequences for Under-Represented Multiple Sclerosis Lesions.\u003cbr\u003e.- The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition.\u003cbr\u003e.- DreamTexture: High-Fidelity Synthetic 3D Data Generation through De coupled Geometry and Texture Synthesis.\u003cbr\u003e.- Control+Shift: Generating Controllable Distribution Shifts.\u003cbr\u003e.- Comparative Analysis of Synthetic and Real Melanoma Images in AI-Driven Diagnosis.\u003cbr\u003e.- How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recog nition.\u003cbr\u003e.- Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization.\u003cbr\u003e.- FABRIC: Personalizing Diffusion Models with Iterative Feedback.\u003cbr\u003e.- TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection.\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195480138071,"sku":"9783031919060","price":66.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/computer-vision-eccv-2024-workshops-9783031919060","provider":"Book Curl","version":"1.0","type":"link"}