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- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition12,821
This work equips the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
- 5,457
- 3,149
- Channel Pruning for Accelerating Very Deep Neural Networks2,839
This paper proposes an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction, and generalizes this algorithm to multi-layer and multi-branch cases.
- 2,617
- 1,450
- 1,330
- Deep ADMM-Net for compressive sensing MRI1,287
Experiments on MRI image reconstruction under different sampling ratios in k-space demonstrate that the proposed novel ADMM-Net algorithm significantly improves the baseline ADMM algorithm and achieves high reconstruction accuracies with fast computational speed.
- Lazy snapping1,276
Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.
- 1,255
- Learning a convolutional neural network for non-uniform motion blur removal930
A deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN) is proposed and the candidate set of motion kernels predicted by the CNN are extended using carefully designed image rotations.
- 908
- Geodesic Saliency Using Background Priors885
Evaluation on two databases validates that geodesic saliency achieves superior results and outperforms previous approaches by a large margin, in both accuracy and speed (2 ms per image), illustrating that appropriate prior exploitation is helpful for the ill-posed saliency detection problem.
- 883
- Accelerating Very Deep Convolutional Networks for Classification and Detection860
This paper aims to accelerate the test-time computation of convolutional neural networks, especially very deep CNNs, and develops an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD).
- Image deblurring with blurred/noisy image pairs764
This paper shows in this paper how to produce a high quality image that cannot be obtained by simply denoising the noisy image, or deblurring the blurred image alone, by combining information extracted from both blurred and noisy images.
- 699
- Image completion with structure propagation671
This paper introduces a novel approach to image completion in which the user manually specifies important missing structure information by extending a few curves or line segments from the known to the unknown regions by adopting the Belief Propagation algorithm to find the optimal patches.
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- Object Detection Networks on Convolutional Feature Maps454
It is shown by experiments that despite the effective ResNets and Faster R-CNN systems, the design of NoCs is an essential element for the 1st-place winning entries in ImageNet and MS COCO challenges 2015.
- 437
- Bayesian Face Revisited: A Joint Formulation436
This paper revisits the classical Bayesian face recognition method by Baback Moghaddam et al. and proposes a new joint formulation that leads to an EM-like model learning at the training time and an efficient, closed-formed computation at the test time.
- 415
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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