Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

增強(qiáng)

騙子

,Few-Shot End-to-End Object Detection via?Constantly Concentrated Encoding Across Heads,oposals to facilitate the adaptation at lower heads. Extensive experimental results show that our model brought clear gain on benchmarks. Detailed ablation studies are provided to justify the selection of each component.

人類

,Implicit Neural Representations for?Image Compression,rtion performance. Our contributions to source compression with INRs vastly outperform prior work. We show that our INR-based compression algorithm, meta-learning combined with SIREN and positional encodings, outperforms JPEG2000 and Rate-Distortion Autoencoders on Kodak with 2x reduced dimensionali

信條

蜈蚣

laceration

,Learning Ego 3D Representation as?Ray Tracing,presentation from 2D images without any depth supervision, and with the built-in geometry structure consistent .?BEV. Despite its simplicity and versatility, extensive experiments on standard BEV visual tasks (., camera-based 3D object detection and BEV segmentation) show that our model outperforms

蚊子

GREG

,Hierarchically Self-supervised Transformer for?Human Skeleton Representation Learning, covering three skeleton-based downstream tasks including action recognition, action detection, and motion prediction. Under both supervised and semi-supervised evaluation protocols, our method achieves the state-of-the-art performance. Additionally, we demonstrate that the prior knowledge learned b

linear

cuticle

,Balancing Stability and?Plasticity Through Advanced Null Space in?Continual Learning,ove the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.