optimization is derived based on the principle of maximizing the marginal likelihood and conducted usi...
Compute cost to decode JPEGcan be analyzed by counting the number of operations (OPs) for the inverse of the above process. Consider decoding a single 8×8 patch. Our proposed scheme decodes step (h) - (f) with a computation cost of 3Ns+ 128 OPs whereNs ∈[1..64]: number of RLE...
Jersey Number 2023 (new,奖金为500美元) 8.4th Embodied AI Workshop 网址:https://embodied-ai.org/ 方向:rearrangement, visual navigation, vision-and-language, and audio-visual navigation 9.The 4th CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics 网址:https://scene-under...
This year, wereceived a record number of9155submissions (a 12% increase over CVPR 2022), and accepted2360papers, for a 25.78% acceptance rate. 注1:欢迎各位大佬提交issue,分享CVPR 2023论文和开源项目! 注2:关于往年CV顶会论文以及其他优质CV论文和大盘点,详见:https://github.com/amusi/daily-paper-...
To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does not necessarily lead to a similar level of reduction in latency. This mainly stems from inefficiently low floating-po...
leading to linear scale-up in training costs and the number of sub-NeRFs as the scene expands. An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions. However, the feature grid ...
we propose logo, a fal sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. logo consistently outperforms six active learning strategies in ...
Original file line numberDiff line numberDiff line change @@ -115,6 +115,16 @@ CVPR2023 decisions are now available on OpenReview! This year, wereceived a reco - Paper: https://arxiv.org/abs/2212.06785 - Code: https://github.com/ZrrSkywalker/I2P-MAE # NeRF **NoPe-NeRF: Optimising...
Training with a growing number of datasets 表 1 展示了在增加数据集数量时对上游数据集的影响:1) 增加训练数据集的数量始终会带来更好的模型性能。2)多数据集使用ScaleDet进行训练通常优于单数据集训练。这表明ScaleDet在异构标签空间、不同数据集的不同领域中学习得很好,并且不会过度拟合任何特定数据集。
the number of clients or batch size. This is done through increasing the size of an injected fully-connected (FC) layer. We show that this results in a resource overhead which grows larger with an increasing number of clients. We show that this resource overhead is caused by an incorrect...