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- Image Segmentation Using Deep Learning: A Survey3,949
A comprehensive review of recent pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings are provided.
- Region Covariance: A Fast Descriptor for Detection and Classification1,481
A fast method for computation of covariances based on integral images, and the performance of the covariance features is superior to other methods, as it is shown, and large rotations and illumination changes are also absorbed by the covariances matrix.
- Underwater scene prior inspired deep underwater image and video enhancement1,262
An underwater image enhancement convolutional neural network model based on underwater scene prior based on underwater scene prior is proposed, called UWCNN, which directly reconstructs the clear latent underwater image, which benefits from the underwater scene prior which can be used to synthesize underwater image training data.
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- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- A Novel Performance Evaluation Methodology for Single-Target Trackers667
The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison and a fully-annotated dataset with per-frame annotations with several visual attributes, which is the largest benchmark to date.
- Going deeper into action recognition: A survey651
This survey provides a comprehensive review of the notable steps taken towards recognizing human actions, starting with the pioneering methods that use handcrafted representations, and then, navigating into the realm of deep learning based approaches.
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- See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks537
This work introduces a novel network, called as CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view and proposes a unified and end-to-end trainable framework where different co-att attention variants can be derived for mining the rich context within videos.
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- LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement515
A trainable Convolutional Neural Network is proposed for weakly illuminated image enhancement, namely LightenNet, which takes a weakly illumination image as input and outputs its illumination map that is subsequently used to obtain the enhanced image based on Retinex model.
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- A Self-supervised Approach for Adversarial Robustness385
This paper proposes a self-supervised adversarial training mechanism in the input space that provides significant robustness against the unseen adversarial attacks and can be deployed as a plug-and-play solution to protect a variety of vision systems, as it demonstrates for the case of classification, segmentation and detection.
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- Less Is More: Towards Compact CNNs343
This work shows that, by incorporating sparse constraints into the objective function, it is possible to decimate the number of neurons during the training stage, thus theNumber of parameters and the memory footprint of the neural network are reduced, which is desirable at the test time.
- Ultra-Resolving Face Images by Discriminative Generative Networks333
This work presents a discriminative generative network that can ultra-resolve a very low resolution face image of size \(16 \times 16\) pixels to its \(8\times \) larger version by reconstructing 64 pixels from a single pixel.
- A Survey on Deep Learning Technique for Video Segmentation316
This survey comprehensively review two basic lines of research — generic object segmentation in videos, and video semantic segmentation — by introducing their respective task settings, background concepts, perceived need, development history, and main challenges.
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- DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking275
This paper presents an efficient and very robust tracking algorithm using a single convolutional neural network for learning effective feature representations of the target object in a purely online manner and introduces a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation.
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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