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Works114 from public data
- Video Object Segmentation Using Space-Time Memory Networks903
This work proposes a novel solution for semi-supervised video object segmentation by leveraging memory networks and learning to read relevant information from all available sources to better handle the challenges such as appearance changes and occlussions.
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- EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images319
This paper introduces a fast and accurate light field depth estimation method based on a fully-convolutional neural network that achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and demonstrates the effectiveness of the proposed method on real-world light-field images.
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- Copy-and-Paste Networks for Deep Video Inpainting146
A novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video to significantly improve the lane detection accuracy on road videos.
- Visual enhancement of old documents with hyperspectral imaging129
This work demonstrates how to use the invisible bands to improve the visual quality of text-based documents corrupted with undesired artifacts such as ink-bleed, ink-corrosion, and foxing.
- Onion-Peel Networks for Deep Video Completion116
An asymmetric attention block is proposed that computes similarities between the hole boundary pixels in the target and the non-hole pixel in the references in a non-local manner and can have an unlimited spatial-temporal window size and fill the holes with globally coherent contents.
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- Approaching the computational color constancy as a classification problem through deep learning109
A deep learning framework for the illumination estimation problem is adopted and the convolutional neural network is trained to solve the problem by casting the color constancy problem as an illumination classification problem.
- Building Emotional Machines: Recognizing Image Emotions Through Deep Neural Networks97
This paper focuses on two high-level features, the object and the background, and assumes that the semantic information in images is a good cue for predicting emotions, and builds an emotion-based feedforward deep neural network that produces the emotion values of a given image.
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- A Generalized Framework for Video Instance Segmentation83
A Generalized framework for VIS, namely GenVIS, that achieves state-of-the-art performance on challenging benchmarks without designing complicated architectures or requiring extra post-processing, and introduces a memory that effectively acquires information from previous states.
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- Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks70
This work proposes a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training.
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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 2026-10-14. 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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