Search Results for author: Juncheng Li

Found 36 papers, 19 papers with code

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

no code implementations20 Apr 2022 Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.

Stereo Image Super-Resolution

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

no code implementations19 Apr 2022 Guangwei Gao, Zixiang Xu, Juncheng Li, Jian Yang, Tieyong Zeng, Guo-Jun Qi

Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously.

Image Super-Resolution

Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning

1 code implementation24 Mar 2022 Juncheng Li, Junlin Xie, Long Qian, Linchao Zhu, Siliang Tang, Fei Wu, Yi Yang, Yueting Zhuang, Xin Eric Wang

To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i. e., Charades-CG and ActivityNet-CG.

Semantic correspondence

Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution

1 code implementation16 Dec 2021 Guangwei Gao, Wenjie Li, Juncheng Li, Fei Wu, Huimin Lu, Yi Yu

Convolutional neural networks based single-image super-resolution (SISR) has made great progress in recent years.

Image Super-Resolution

From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution

1 code implementation29 Sep 2021 Juncheng Li, Zehua Pei, Tieyong Zeng

In this survey, we give an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy.

Image Quality Assessment Image Super-Resolution

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

1 code implementation2 Sep 2021 Guangwei Gao, Guoan Xu, Juncheng Li, Yi Yu, Huimin Lu, Jian Yang

Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch.

Autonomous Driving Drone navigation +1

Transformer for Single Image Super-Resolution

1 code implementation25 Aug 2021 Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, Tieyong Zeng

LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA).

Image Super-Resolution

Structure-Preserving Deraining with Residue Channel Prior Guidance

1 code implementation ICCV 2021 Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, Tieyong Zeng

Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.

Single Image Deraining

Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference

no code implementations ICCV 2021 Juncheng Li, Siliang Tang, Linchao Zhu, Haochen Shi, Xuanwen Huang, Fei Wu, Yi Yang, Yueting Zhuang

Secondly, we introduce semantic coherence learning to explicitly encourage the semantic coherence of the adaptive hierarchical graph network from three hierarchies.

Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation

1 code implementation2 Jun 2021 Qinyan Dai, Juncheng Li, Qiaosi Yi, Faming Fang, Guixu Zhang

Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.

Disparity Estimation Image Reconstruction +1

Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network

no code implementations24 Mar 2021 Zhengxue Wang, Guangwei Gao, Juncheng Li, Yi Yu, Huimin Lu

Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance.

Image Super-Resolution

Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing

no code implementations24 Feb 2021 Qiaosi Yi, Juncheng Li, Faming Fang, Aiwen Jiang, Guixu Zhang

To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.

Image Dehazing Single Image Dehazing

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

no code implementations5 Jan 2021 Qiaosi Yi, Yunxing Liu, Aiwen Jiang, Juncheng Li, Kangfu Mei, Mingwen Wang

Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.

Crowd Counting Density Estimation

Robust Meta-learning with Noise via Eigen-Reptile

no code implementations1 Jan 2021 Dong Chen, Lingfei Wu, Siliang Tang, Fangli Xu, Juncheng Li, Chang Zong, Chilie Tan, Yueting Zhuang

In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.

Few-Shot Learning

Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations

no code implementations12 Sep 2020 Ze Cheng, Juncheng Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze

In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation.

MDCN: Multi-scale Dense Cross Network for Image Super-Resolution

1 code implementation30 Aug 2020 Juncheng Li, Faming Fang, Jiaqian Li, Kangfu Mei, Guixu Zhang

Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.

Image Super-Resolution

Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling

no code implementations11 Aug 2020 Jiacheng Li, Siliang Tang, Juncheng Li, Jun Xiao, Fei Wu, ShiLiang Pu, Yueting Zhuang

In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting.

Meta-Learning Visual Storytelling

Disentangle Perceptual Learning through Online Contrastive Learning

no code implementations24 Jun 2020 Kangfu Mei, Yao Lu, Qiaosi Yi, Hao-Yu Wu, Juncheng Li, Rui Huang

Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.

Contrastive Learning

Towards Zero-shot Learning for Automatic Phonemic Transcription

no code implementations26 Feb 2020 Xinjian Li, Siddharth Dalmia, David R. Mortensen, Juncheng Li, Alan W. black, Florian Metze

The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes.

Zero-Shot Learning

Fast Loop Closure Detection via Binary Content

no code implementations25 Feb 2020 Han Wang, Juncheng Li, Maopeng Ran, Lihua Xie

Our method is compared with the state-of-the-art loop closure detection methods and the results show that it outperforms the traditional methods at both recall rate and speed.

Image Retrieval Loop Closure Detection +1

HighEr-Resolution Network for Image Demosaicing and Enhancing

1 code implementation19 Nov 2019 Kangfu Mei, Juncheng Li, Jiajie Zhang, Hao-Yu Wu, Jie Li, Rui Huang

However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.

Demosaicking

Walking with MIND: Mental Imagery eNhanceD Embodied QA

no code implementations5 Aug 2019 Juncheng Li, Siliang Tang, Fei Wu, Yueting Zhuang

The experimental results and further analysis prove that the agent with the MIND module is superior to its counterparts not only in EQA performance but in many other aspects such as route planning, behavioral interpretation, and the ability to generalize from a few examples.

Adversarial camera stickers: A physical camera-based attack on deep learning systems

1 code implementation21 Mar 2019 Juncheng Li, Frank R. Schmidt, J. Zico Kolter

In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself?

A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling

3 code implementations22 Oct 2018 Yun Wang, Juncheng Li, Florian Metze

This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization.

Sound Audio and Speech Processing

Progressive Feature Fusion Network for Realistic Image Dehazing

1 code implementation4 Oct 2018 Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang

Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.

Image Dehazing Single Image Dehazing

An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks

1 code implementation3 Oct 2018 Kangfu Mei, Aiwen Jiang, Juncheng Li, Jihua Ye, Mingwen Wang

Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.

Image Super-Resolution

Multi-scale Residual Network for Image Super-Resolution

1 code implementation ECCV 2018 Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang

Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).

Image Super-Resolution

Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval

1 code implementation ICMR 2018 Niluthpol Chowdhury Mithun, Juncheng Li, Florian Metze, Amit K. Roy-Chowdhury

Constructing a joint representation invariant across different modalities (e. g., video, language) is of significant importance in many multimedia applications.

Video-Text Retrieval

A Comparison of deep learning methods for environmental sound

1 code implementation20 Mar 2017 Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das

On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.

Learning Filter Banks Using Deep Learning For Acoustic Signals

no code implementations29 Nov 2016 Shuhui Qu, Juncheng Li, Wei Dai, Samarjit Das

Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.

Very Deep Convolutional Neural Networks for Raw Waveforms

8 code implementations1 Oct 2016 Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, Samarjit Das

Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.

Representation Learning

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