Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Alok ChoudharySuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
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.
- 1,363
- 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.
- 718
- 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.
- 460
- 435
- 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.
- 345
- 321
- 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.
- 260
- 250
- 240
- 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.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.