So, when performing multi-scale inference, can we predict regions containing small objects from coarser scales? 如果深度卷积神经网络是生物视觉的近似,那么应该可以以较低的分辨率定位类似物体的区域,并通过以更高的分辨率放大它们来识别它们,类似于我们的周边视觉与中央凹视觉的耦合方式。为此,我们提出了一个名...
2.2 FocusChip Generation 在推断时,根据阈值t得到FcousPixels(二值图), 并膨胀该二值图以获得更多的上下文区域,然后获得其连通成分,生成包围每个连通成分的矩形chips并保证大小大于k,最后将重叠的矩形区域合并成一个,得到最终的Chips,在多尺度图像金字塔上迭代地截取区域做推断。算法如下。 2.3 Focus Stacking for Obj...
Motivate by this, in this paper, a novel Dense Multi-scale Inference Network (DMINet) is proposed for the accurate SOD task, which mainly consists of a dual-stream multi-receptive field module and a residual multi-mode interaction strategy. The former uses the well-designed different receptive...
A. Multiscale inference for high- frequency data. arXiv:0803.0392A. Sykulski, S. C. Olhede, G. A. Pavliotis, Multiscale Inference for High-frequency Data, Submitted.S. Olhede, G.A. Pavliotis, and A. Sykulski. Multiscale inference for high fre- quency data. Preprint, 2008....
AutoFocus, on the other hand, is an efficient multi-scaleinferencealgorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions that are likely to contain small objects at finer scales. This ...
AutoFocus, on the other hand, is an efficient multi-scaleinferencealgorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions that are likely to contain small objects at finer scales. This ...
Inference Model for Internet-of-Things DPFEE: A High Performance Scalable Pre-Processor for Network Security Systems A New Fluid-Chip Co-Design for Digital Microfluidic Biochips Considering Cost Drivers and Design Convergence A Monolithic 3D Hybrid Architecture for Energy-Efficient Computation Explore ...
Usually in most studies, the superposition of 2i−1×1 and 1×2i−1 asymmetric convolution replace standard convolution in order to improve the inference speed without reducing the representational power, as in Inception-V3 [42]. On the other hand, the use of 3 × 1, 1 × 3, and ...
Variational autoencoders (VAEs)40 are generative models that employ variational inference and deep neural networks to learn the underlying distribution of the data they are trained on. These models consist of an encoder network that parameterizes the latent variational distribution of the data and a...
inference时,考虑如下概率,如果下式成立,则通过查阅外部知识片段以生成回复(输出1),否则,则依赖API去生成(输出0)。在实际过程中,其实输出的不是01值,而是概率值。 \max_{k_\in K} p_{\text{decision}}(l_{k_i} = 1|C_t, k_i) \geq \max_{s_\in S} p_{\text{decision}}(l_{s_i} = ...