Computer Vision - ECCV 2024

Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off.- SkyScenes: A Synthetic Dataset for Aerial Scene Understanding.- Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps.- GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction.- AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation.- PFedEdit: Personalized Federated Learning via Automated Model Editing.- De-Confusing Pseudo-Labels in Source-Free Domain Adaptation.- GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes.- EraseDraw : Learning to Insert Objects by Erasing Them from Images.- SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference.- Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models.- Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training.- Keypoint Promptable Re-Identification.- Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas.- DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting.- Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos.- Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores.- MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception.- Training A Secure Model against Data-Free Model Extraction.- EpipolarGAN: Omnidirectional Image Synthesis with Explicit Camera Control.- TriNeRFLet: A Wavelet Based Triplane NeRF Representation.- EgoBody3M: Egocentric Body Tracking on a VR Headset using a Diverse Dataset.- Photorealistic Video Generation with Diffusion Models.- RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement.- TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models.- Object-Aware Query Perturbation for Cross-Modal Image-Text Retrieval.- DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation.