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Works184 from public data
- Learning Deconvolution Network for Semantic Segmentation4,393
A novel semantic segmentation algorithm by learning a deep deconvolution network on top of the convolutional layers adopted from VGG 16-layer net, which demonstrates outstanding performance in PASCAL VOC 2012 dataset.
- Learning Multi-domain Convolutional Neural Networks for Visual Tracking2,726
A novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network using a large set of videos with tracking ground-truths to obtain a generic target representation.
- Large-Scale Image Retrieval with Attentive Deep Local Features867
An attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature), based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset.
- 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.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
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- Domain-Specific Batch Normalization for Unsupervised Domain Adaptation445
A novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks that achieves the state-of-the-art accuracy in the standard setting and the multi-source domain adaption scenario.
- Weakly Supervised Action Localization by Sparse Temporal Pooling Network401
This work proposes a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks that attains state-of-the-art results on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with its weak supervision.
- Channel Attention Is All You Need for Video Frame Interpolation381
A simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component, and achieves outstanding performance compared to the existing models with a component for optical flow computation.
- Local-Global Video-Text Interactions for Temporal Grounding366
This paper addresses the problem of text-to-video temporal grounding using a novel regression-based model that learns to extract a collection of mid-level features for semantic phrases in a text query, which corresponds to important semantic entities described in the query.
- Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction345
The proposed network-joint network with the CNN for ImageQA and the parameter prediction network-is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU.
- URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark328
The URVOS algorithm addresses the challenging problem by performing language-based object segmentation and mask propagation jointly using a single deep neural network with a proper combination of two attention models.
- Real-Time MDNet299
This work presents a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet) that accelerates feature extraction procedure and learns more discriminative models for instance classification; it enhances representation quality of target and background by maintaining a high resolution feature map with a large receptive field per activation.
- InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering279
An information-theoretic regularization technique for few-shot novel view synthesis based on neural im-plicit representation that minimizes potential reconstruction inconsistency that happens due to in-sufficient viewpoints by imposing the entropy constraint of the density in each ray.
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- Learning to Optimize Domain Specific Normalization for Domain Generalization270
The state-of-the-art accuracy of the algorithm in the standard domain generalization benchmarks is demonstrated, as well as viability to further tasks such as multi-source domain adaptation anddomain generalization in the presence of label noise.
- Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation267
This algorithm is based on knowledge distillation and provides a principled way to maintain the representations of old models while adjusting to new tasks effectively, which effectively alleviates the notorious catastrophic forgetting problem despite the limited accessibility of data in the previous tasks.
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- Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network177
A novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network based on DCNN that exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels.
- Streamlined Dense Video Captioning171
A novel dense video captioning framework is proposed, which models temporal dependency across events in a video explicitly and leverages visual and linguistic context from prior events for coherent storytelling.
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- Weakly Supervised Semantic Segmentation Using Web-Crawled Videos161
A novel algorithm for weakly supervised semantic segmentation based on image-level class labels only that substantially outperforms existing methods based on the same level of supervision and is even as competitive as the approaches relying on extra annotations.
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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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