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Works356 from public data
- 924
- LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement506
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.
- Video Summarization With Attention-Based Encoder–Decoder Networks392
This paper proposes a novel video summarization framework named attentive encoder–decoder networks forVideo summarization (AVS), in which the encoder uses a bidirectional long short-term memory (BiLSTM) to encode the contextual information among the input video frames.
- Efficient HOG human detection353
Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed HOG+SVM method.
- 325
- Photo2Trip314
This paper proposes to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences, and can provide a customized trip plan for a tourist.
- 281
- ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation276
A generic medical segmentation method, called Edge-aTtention guidance Network (ET-Net), which embeds edge-attention representations to guide the segmentation network, which outperforms current state-of-the-art segmentation methods.
- Learning Pooling for Convolutional Neural Network274
A learned pooling operation obtained by end-to-end training which is called LEAP (LEArning Pooling), inspired by the LEAP operation, and one simplified convolution operation to replace the traditional convolution which consumes many extra parameters are proposed.
- Mask-Guided Attention Network for Occluded Pedestrian Detection223
A novel mask-guided attention network is introduced that emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features and it is empirically demonstrated that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts.
- Convolution in Convolution for Network in Network222
The proposed method is called convolution in convolution (CiC), and one or more layers of the sparse shallow MLP are sparely connected in the channel dimension or channel–spatial domain, and the experimental results demonstrate the effectiveness of the proposed CiC method.
- Robust Tensor Analysis With L1-Norm206
Experimental results upon face and other datasets demonstrate the advantages of the proposed L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers.
- SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation200
A fast single-stage instance segmentation method that preserves instance-specific spatial information by separating mask prediction of an instance to different sub-regions of a detected bounding-box, leading to improved mask predictions and a mask alignment weighting loss and a feature alignment scheme to better correlate mask prediction with object detection.
- Consensus-Aware Visual-Semantic Embedding for Image-Text Matching196
A Consensus-aware Visual-Semantic Embedding model is proposed to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching and learns the associations and alignments between image and text based on the exploited consensus.
- Equip tourists with knowledge mined from travelogues191
This paper proposes a probabilistic topic model, named as Location-Topic model, which has the advantages of differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations.
- 176
- Fully affine invariant SURF for image matching161
The proposed algorithm makes full use of the affine invariant advantage of ASIFT and the efficient merit of SURF while avoids their drawbacks and demonstrates the robustness and efficiency of the proposed algorithm.
- From Handcrafted to Deep Features for Pedestrian Detection: A Survey160
An extensive review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches is presented and it is found that hand crafted features with large freedom degrees in shape and space have better performance.
- Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation155
A new Cascaded Hierarchical Atrous Pyramid Pooling (CHASPP) module, consisting of two cascaded components, that not only rich local detail characteristics can be comprehensively presented, but also important global contextual information can be effectively exploited to improve the prediction accuracy.
- Learning Compositional Neural Information Fusion for Human Parsing141
This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing, formulate the approach as a neural information fusion framework that is extensively evaluated on four popular datasets.
- 140
- Deep neural networks with Elastic Rectified Linear Units for object recognition139
A novel Elastic Rectified Linear Unit (EReLU) that focuses on processing the positive part of input is proposed that improves model fitting with no extra parameters and little overfitting risk and is able to further improve the performance of networks.
- Deep Contextual Attention for Human-Object Interaction Detection138
This work proposes a contextual attention framework for human-object interaction detection that leverages context by learning contextually-aware appearance features for human and object instances and adaptively selects relevant instance-centric context information to highlight image regions likely to contain human- object interactions.
- Improved prototypical networks for few-Shot learning132
This work proposes an attention-analogous strategy to explore the class distribution information by distributing different weights to samples based on their representativeness and proposes a distance scaling strategy to reduce the intra- class difference while enlarge the inter-class difference.
- 130
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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