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Works152 from public data
- EnlightenGAN: Deep Light Enhancement Without Paired Supervision2,707
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
- Generative Image Inpainting with Contextual Attention2,597
This work proposes a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
- Free-Form Image Inpainting With Gated Convolution2,102
The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers.
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- Top-Down Neural Attention by Excitation Backprop1,087
A new backpropagation scheme, called Excitation Backprop, is proposed to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process, and the concept of contrastive attention is introduced to make the top- down attention maps more discriminative.
- MAttNet: Modular Attention Network for Referring Expression Comprehension1,003
This work proposes to decompose expressions into three modular components related to subject appearance, location, and relationship to other objects, which allows for flexibly adapt to expressions containing different types of information in an end-to-end framework.
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- STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation588
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
- Photo Aesthetics Ranking Network with Attributes and Content Adaptation563
This work proposes to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
- Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing516
A new benchmark Look into Person (LIP) is introduced that makes a significant advance in terms of scalability, diversity and difficulty, and a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision.
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- Learning to predict indoor illumination from a single image409
An end-to-end deep neural network is trained that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting, which allows to automatically recover high-quality HDR illumination estimates that significantly outperform previous state- of-the-art methods.
- Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark379
A new benchmark named “Look into Person (LIP)” is introduced that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis.
- Semantic Object Parsing with Graph LSTM370
The Graph Long Short-Term Memory network is proposed, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data.
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- Recurrent Multimodal Interaction for Referring Image Segmentation315
It is argued that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and a convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information is proposed.
- Deep Image Harmonization314
This work proposes an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization and introduces an efficient way to collect large-scale and high-quality training data that can facilitate the training process.
- Deep Human Parsing with Active Template Regression308
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask is expressed as the linear combination of the learned mask templates.
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- Proposal-Free Network for Instance-Level Object Segmentation239
A Proposal-Free Network (PFN) is proposed to address the instance-level object segmentation problem, which outputs the numbers of instances of different categories and the pixel-level information on i) the coordinates of the instance bounding box each pixel belongs to, and ii) the confidences ofDifferent categories for each pixel, based on pixel-to-pixel deep convolutional neural network.
- Towards Multi-Pose Guided Virtual Try-On Network234
This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses, and significantly outperforms all state-of-the-art methods both qualitatively and quantitatively.
- Graphonomy: Universal Human Parsing via Graph Transfer Learning204
A new universal human parsing agent, named ``Graphonomy", is proposed, which incorporates hierarchical graph transfer learning upon the conventional parsing network to encode the underlying label semantic structures and propagate relevant semantic information.
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- Inner and Inter Label Propagation: Salient Object Detection in the Wild197
A co-transduction algorithm is devised to fuse both boundary and objectness labels based on an inter propagation scheme, which achieves superior performance compared with the newest state-of-the-arts in terms of different evaluation metrics.
- Semantic Object Parsing with Local-Global Long Short-Term Memory188
A novel deep Local-Global Long Short-Term Memory architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions and demonstrates the significant superiority of this LG-LSTM over other state-of-the-art methods.
- $R^{2}$ Former: Unified Retrieval and Reranking Transformer for Place Recognition171
A unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named R^{2}$ Former, which significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption.
- Matching-CNN meets KNN: Quasi-parametric human parsing169
This work aims to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model.
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- A Modulation Module for Multi-task Learning with Applications in Image Retrieval157
A general modulation module is proposed, which can be inserted into any convolutional neural network architecture, to encourage the coupling and feature sharing of relevant tasks while disentangling the learning of irrelevant tasks with minor parameters addition.
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- Eigen-PEP for Video Face Recognition135
The Eigen-PEP model is presented, built upon the recent success of the probabilistic elastic part (PEP) model, which produces an intermediate high dimensional, part-based, and pose-invariant representation of a face subject.
- Joint Object and Part Segmentation Using Deep Learned Potentials128
This paper proposes a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentsation, and more detailed part-level localization is utilized to refine object segmentation.
- SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing118
SemanticStyleGAN is presented where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way and provides a strong disentanglement between different spatial areas.
- Video Scene Parsing with Predictive Feature Learning117
It is experimentally proved that the learned predictive features in the model are able to significantly enhance the video parsing performance by combining with the standard image parsing network.
- Automatic Image Cropping using Visual Composition, Boundary Simplicity and Content Preservation Models117
This paper proposes a new, aesthetic photo cropping system which combines three models: visual composition, boundary simplicity, and content preservation, and achieves significant improvement over recent cropping methods in both quantitative and qualitative evaluation.
- Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition112
A coarse-to-fine method that decomposes the original image description into a skeleton sentence and its attributes, and generates the skeleton sentences and attribute phrases separately, which enables image description generation that better accommodates user preferences.
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- Interpretable Structure-Evolving LSTM101
The effectiveness of structure-evolving LSTM in the application of semantic object parsing is evaluated and it is demonstrated to have an advantage over state-of-the-art L STM models on standard benchmarks.
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- Towards Interpretable Face Recognition95
This work proposes a spatial activation diversity loss to learn more structured face representations and designs a feature activation diversity Loss to push the interpretable representations to be discriminative and robust to occlusions.
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- Dynamic hand gesture recognition: An exemplar-based approach from motion divergence fields92
A new visual representation for hand motions based on the motion divergence fields, which can be normalized to gray-scale images, and achieves higher recognition accuracy than other state-of-the-art motion and spatio-temporal features on this database.
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- Sky is not the limit69
An automatic background replacement algorithm that can generate realistic, artifact-free images with a diverse styles of skies by utilizing visual semantics to guide the entire process including sky segmentation, search and replacement is proposed.
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- Automatic Content-Aware Color and Tone Stylization64
A new technique is introduced that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer.
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- Scene Parsing with Global Context Embedding62
This work trains a context network based on scene similarities to generate feature representations for global contexts and designs modules to embed the feature representations and the priors into the segmentation network as additional global context cues.
- FoveaNet: Perspective-Aware Urban Scene Parsing61
This work proposes a new FoveaNet model to fully exploit the perspective geometry of scene images and address the common failures of generic parsing models, and introduces a new dense CRFs model that takes the Perspective geometry as a prior potential.
- Reversible Recursive Instance-Level Object Segmentation61
This work proposes a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework that consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub- network that generates the foreground mask of the dominant object instance in each proposal.
- Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses59
It is shown that a simple universal perturbation can fool a series of state-of-the-art defenses, and it is verified that regionally homogeneous perturbations can well transfer across different vision tasks.
- Mobile Product Image Search by Automatic Query Object Extraction58
A weighted object mask voting algorithm is proposed based on a spatially-constrained model, which allows robust localization and segmentation of the query object, and achieves significantly better retrieval accuracy than previous methods.
- DeepLens55
A novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module that can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow doF synthesis.
- Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis54
This paper proposes a simple yet effective open-domain sampling and optimization strategy to "fool" the generator into treating fake sketches as real ones, and shows impressive results in synthesizing realistic color, texture, and maintaining the geometric composition for various categories of open- domain sketches.
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- Video Salient Object Detection via Contrastive Features and Attention Modules44
A network with attention modules to learn contrastive features for video salient object detection without the high computational temporal modeling techniques is proposed, and a non-local self-attention scheme to capture the global information in the video frame is developed.
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- Video Object Detection via Object-Level Temporal Aggregation40
A detection model is applied on sparse keyframes to handle new objects, occlusions, and rapid motions and then used to exploit temporal cues and track the detected objects in the remaining frames, which enhances efficiency and temporal coherence.
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- Unsupervised Low-light Image Enhancement with Decoupled Networks32
This paper proposes a two-stage, fully unsupervised model to handle both illumination enhancement and noise suppression of real-world low-light images, and shows that the proposed method outperforms the state-of-the-art unsuper supervised methods concerning both illumination Enhancement and noise reduction.
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- Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks.25
This paper proposes to learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion and shows that this method outperforms the state-of-the-art unsuper supervised image enhancement methods in terms of both illumination enhancement and noise reduction.
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- Learning to Segment Human by Watching YouTube24
A very-weakly supervised learning framework for human segmentation task, where only an imperfect human detector is available along with massive weakly-labeled YouTube videos, and reaches a new state-of-the-art performance on the human segmentsation task.
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- Fashion Editing with Multi-scale Attention Normalization.11
A novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes and significantly outperforms the state-of-the-art methods on image manipulation.
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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 2026-10-13. 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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