Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

负担

钝剑

,Bayesian Self-training for Semi-supervised 3D Segmentation,rtite matching algorithm, we extend the method to semi-supervised 3D instance segmentation, and finally, with the same building blocks, to dense 3D visual grounding. We demonstrate state-of-the-art results for our semi-supervised method on SemanticKITTI and ScribbleKITTI for 3D semantic segmentation

美学

,Motion and Structure from Event-Based Normal Flow,ometric error term, as an alternative to the full (optical) flow in solving a family of geometric problems that involve instantaneous first-order kinematics and scene geometry. Furthermore, we develop a fast linear solver and a continuous-time nonlinear solver on top of the proposed geometric error

slipped-disk

dura-mater

,Learning to Complement and to Defer to Multiple Users,Comprehensive evaluations across real-world and synthesized datasets demonstrate LECODU’s superior performance compared to state-of-the-art HAI-CC methods. Remarkably, even when relying on unreliable users with high rates of label noise, LECODU exhibits significant improvement over both human decisi

Admonish

Ornament

万灵丹

,Multi-sentence Grounding for Long-Term Instructional Video,ltaneously, as a result, the model shows superior performance on a series of multi-sentence grounding tasks, surpassing existing state-of-the-art methods by a significant margin on three public benchmarks, namely, 9.0% on HT-Step, 5.1% on HTM-Align and 1.9% on CrossTask. All codes, models, and the r

拥护

,Do Generalised Classifiers , on Human Drawn Sketches?,straction levels. This is achieved by learning a codebook of abstraction-specific prompt biases, a weighted combination of which facilitates the representation of sketches across abstraction levels – low abstract edge-maps, medium abstract sketches in TU-Berlin, and highly abstract doodles in QuickD

KEGEL