157 citations · 1.3k across the 97 of their papers we have counts for
20 papers · 1 filter
Deep Self-representative Concept Factorization Network for Representation Learning
Yan Zhang, Zhao Zhang, Zheng Zhang +4
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Ne…
Identity Preserve Transform: Understand What Activity Classification Models Have Learnt
Jialing Lyu, Weichao Qiu, Xinyue Wei +3
Activity classification has observed great success recently. The performance on small dataset is almost saturated and people are moving towards larger datasets. What leads to the p…
Abstract Reasoning with Distracting Features
Kecheng Zheng, Zheng-jun Zha, Wei Wei
Abstraction reasoning is a long-standing challenge in artificial intelligence. Recent studies suggest that many of the deep architectures that have triumphed over other domains fai…
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Yang Wang, Yang Cao, Zheng-Jun Zha +4
Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration…
Learning Deep Bilinear Transformation for Fine-grained Image Representation
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha +1
Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations. However, the computational cost to learn pairwise interac…
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
Yuhang Li, Xuejin Chen, Feng Wu +1
In this paper, we explore the task of generating photo-realistic face images from lines. Previous methods based on conditional generative adversarial networks (cGANs) have shown th…
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +3
Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image regi…
Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +3
Weakly supervised referring expression grounding aims at localizing the referential object in an image according to the linguistic query, where the mapping between the referential…
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Zhao Zhang, Lei Wang, Sheng Li +4
In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover th…
Domain-Specific Embedding Network for Zero-Shot Recognition
Shaobo Min, Hantao Yao, Hongtao Xie +2
Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most…
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Zhao Zhang, Jiahuan Ren, Sheng Li +3
We propose a novel and unsupervised representation learning model, i.e., Robust Block-Diagonal Adaptive Locality-constrained Latent Representation (rBDLR). rBDLR is able to recover…
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
Xiaotian Chen, Xuejin Chen, Zheng-Jun Zha
Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and vis…
Context-Aware Visual Policy Network for Fine-Grained Image Captioning
Zheng-Jun Zha, Daqing Liu, Hanwang Zhang +2
With the maturity of visual detection techniques, we are more ambitious in describing visual content with open-vocabulary, fine-grained and free-form language, i.e., the task of im…
Posterior-Guided Neural Architecture Search
Yizhou Zhou, Xiaoyan Sun, Chong Luo +2
The emergence of neural architecture search (NAS) has greatly advanced the research on network design. Recent proposals such as gradient-based methods or one-shot approaches signif…
Joint Visual Grounding with Language Scene Graphs
Daqing Liu, Hanwang Zhang, Zheng-Jun Zha +2
Visual grounding is a task to ground referring expressions in images, e.g., localize "the white truck in front of the yellow one". To resolve this task fundamentally, the model sho…
Multimodal Semantic Attention Network for Video Captioning
Liang Sun, Bing Li, Chunfeng Yuan +2
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder f…
One-Shot Texture Retrieval with Global Context Metric
Kai Zhu, Wei Zhai, Zheng-Jun Zha +1
In this paper, we tackle one-shot texture retrieval: given an example of a new reference texture, detect and segment all the pixels of the same texture category within an arbitrary…
Camera Lens Super-Resolution
Chang Chen, Zhiwei Xiong, Xinmei Tian +2
Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we invest…
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha +1
Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localiz…
Making History Matter: History-Advantage Sequence Training for Visual Dialog
Tianhao Yang, Zheng-Jun Zha, Hanwang Zhang
We study the multi-round response generation in visual dialog, where a response is generated according to a visually grounded conversational history. Given a triplet: an image, Q&A…