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- Chao XuSuggested from co-authorship
- Katsushi IkeuchiSuggested from co-authorship
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Works316 from public data
- DAFL: Data-Free Learning of Student Networks453
A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
- Data-Free Learning of Student Networks453
A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
- Real-Time Intermediate Flow Estimation for Video Frame Interpolation392
RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed and can support arbitrary-timestep frame interpolation with the temporal encoding input.
- Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery344
This work presents a new approach to robustly solve photometric stereo problems by using advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries.
- DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing315
This work proposes a differentiable sphere tracing algorithm that can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization and shows excellent generalization capability and robustness against various noises.
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- CARS: Continuous Evolution for Efficient Neural Architecture Search277
This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
- AdderNet: Do We Really Need Multiplications in Deep Learning?240
This paper develops a special back-propagation approach for AdderNets by investigating the full-precision gradient, and proposes an adaptive learning rate strategy to enhance the training procedure of Ad DerNets according to the magnitude of each neuron's gradient.
- Polarized 3D: High-Quality Depth Sensing with Polarization Cues220
A framework to combine surface normals from polarization (hereafter polarization normals) with an aligned depth map to overcome physics-based artifacts, allowing the benefits of polarization to be used to enhance depth maps.
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- Deep Shape from Polarization157
This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning, and achieves results that exceed the previous state-of-the-art on a challenging dataset.
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- Self-Calibrating Deep Photometric Stereo Networks149
An uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning that can effectively take advantage of intermediate supervision, resulting in reduced learning difficulty compared to a single-stage model is proposed.
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- RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation.140
A real-time intermediate flow estimation algorithm (RIFE) for video frame interpolation (VFI) that can be trained end-to-end and achieve excellent performance and achieves state-of-the-art index on several benchmarks is proposed.
- CRRN: Multi-scale Guided Concurrent Reflection Removal Network137
This paper proposes the Concurrent Reflection Removal Network (CRRN), a network that integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes.
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- Occluded Imaging with Time-of-Flight Sensors126
It is concluded that off-the-shelf TOF cameras can look around corners and that performance depends on two primary factors: camera modulation frequency and the width of the specular lobe (“shininess”) of the wall.
- SSF-CNN: Spatial and Spectral Fusion with CNN for Hyperspectral Image Super-Resolution121
Experimental results validate that the proposed spatial and spectral fusion CNNs outperforms the state-of-the-art methods and baseline CNN architectures in both quantitative values and visual qualities.
- 1000× Faster Camera and Machine Vision with Ordinary Devices116
A spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1,000x faster than human vision is developed.
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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 2026-10-12. 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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