Search Results for author: Rongrong Ji

Found 147 papers, 84 papers with code

API-Net: Robust Generative Classifier via a Single Discriminator

1 code implementation ECCV 2020 Xinshuai Dong, Hong Liu, Rongrong Ji, Liujuan Cao, Qixiang Ye, Jianzhuang Liu, Qi Tian

On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure.

Robust classification

SSCGAN: Facial Attribute Editing via Style Skip Connections

no code implementations ECCV 2020 Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Rongrong Ji

Each connection extracts the style feature of the latent feature maps in the encoder and then performs a residual learning based mapping function in the global information space guided by the target attributes.

Enabling Deep Residual Networks for Weakly Supervised Object Detection

no code implementations ECCV 2020 Yunhang Shen, Rongrong Ji, Yan Wang, Zhiwei Chen, Feng Zheng, Feiyue Huang, Yunsheng Wu

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale image-level annotation for detector training.

Weakly Supervised Object Detection

Shadow-Aware Dynamic Convolution for Shadow Removal

1 code implementation10 May 2022 Yimin Xu, Mingbao Lin, Hong Yang, Ke Li, Yunhang Shen, Fei Chao, Rongrong Ji

Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction.

Image Reconstruction Shadow Removal

A Closer Look at Branch Classifiers of Multi-exit Architectures

no code implementations28 Apr 2022 Shaohui Lin, Bo Ji, Rongrong Ji, Angela Yao

Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks.

What Goes beyond Multi-modal Fusion in One-stage Referring Expression Comprehension: An Empirical Study

1 code implementation17 Apr 2022 Gen Luo, Yiyi Zhou, Jiamu Sun, Shubin Huang, Xiaoshuai Sun, Qixiang Ye, Yongjian Wu, Rongrong Ji

But the most encouraging finding is that with much less training overhead and parameters, SimREC can still achieve better performance than a set of large-scale pre-trained models, e. g., UNITER and VILLA, portraying the special role of REC in existing V&L research.

Data Augmentation Referring Expression +1

PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation

1 code implementation2 Apr 2022 Jing He, Yiyi Zhou, Qi Zhang, Yunhang Shen, Xiaoshuai Sun, Chao Chen, Rongrong Ji

Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation.

Image Generation

SeqTR: A Simple yet Universal Network for Visual Grounding

2 code implementations30 Mar 2022 Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, Rongrong Ji

In this paper, we propose a simple yet universal network termed SeqTR for visual grounding tasks, e. g., phrase localization, referring expression comprehension (REC) and segmentation (RES).

Referring Expression Referring Expression Comprehension +1

Training-free Transformer Architecture Search

no code implementations23 Mar 2022 Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, Rongrong Ji

Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks.

ARM: Any-Time Super-Resolution Method

1 code implementation21 Mar 2022 Bohong Chen, Mingbao Lin, Kekai Sheng, Mengdan Zhang, Peixian Chen, Ke Li, Liujuan Cao, Rongrong Ji

To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets.

Image Super-Resolution

Global2Local: A Joint-Hierarchical Attention for Video Captioning

no code implementations13 Mar 2022 Chengpeng Dai, Fuhai Chen, Xiaoshuai Sun, Rongrong Ji, Qixiang Ye, Yongjian Wu

Recently, automatic video captioning has attracted increasing attention, where the core challenge lies in capturing the key semantic items, like objects and actions as well as their spatial-temporal correlations from the redundant frames and semantic content.

Frame Video Captioning

Differentiated Relevances Embedding for Group-based Referring Expression Comprehension

no code implementations12 Mar 2022 Fuhai Chen, Xiaoshuai Sun, Xuri Ge, Jianzhuang Liu, Yongjian Wu, Feiyue Huang, Rongrong Ji

In particular, based on the visual and textual semantic features, RMSL conducts an adaptive learning cycle upon triplet ranking, where (1) the target-negative region-expression pairs with low within-group relevances are used preferentially in model training to distinguish the primary semantics of the target objects, and (2) an across-group relevance regularization is integrated into model training to balance the bias of group priority.

Referring Expression Referring Expression Comprehension

Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks

1 code implementation8 Mar 2022 Yunshan Zhong, Mingbao Lin, Xunchao Li, Ke Li, Yunhang Shen, Fei Chao, Yongjian Wu, Rongrong Ji

However, these methods suffer from severe performance degradation when quantizing the SR models to ultra-low precision (e. g., 2-bit and 3-bit) with the low-cost layer-wise quantizer.

Quantization Super-Resolution

Coarse-to-Fine Vision Transformer

1 code implementation8 Mar 2022 Mengzhao Chen, Mingbao Lin, Ke Li, Yunhang Shen, Yongjian Wu, Fei Chao, Rongrong Ji

Our proposed CF-ViT is motivated by two important observations in modern ViT models: (1) The coarse-grained patch splitting can locate informative regions of an input image.

Boosting Crowd Counting via Multifaceted Attention

1 code implementation5 Mar 2022 Hui Lin, Zhiheng Ma, Rongrong Ji, YaoWei Wang, Xiaopeng Hong

Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations.

Crowd Counting

Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters

1 code implementation15 Feb 2022 Mingbao Lin, Liujuan Cao, Yuxin Zhang, Ling Shao, Chia-Wen Lin, Rongrong Ji

Then, we introduce a recommendation-based filter selection scheme where each filter recommends a group of its closest filters.

Image Classification Network Pruning

Optimizing Gradient-driven Criteria in Network Sparsity: Gradient is All You Need

1 code implementation30 Jan 2022 Yuxin Zhang, Mingbao Lin, Mengzhao Chen, Zihan Xu, Fei Chao, Yunhan Shen, Ke Li, Yongjian Wu, Rongrong Ji

We prove that supermask training is to accumulate the weight gradients and can partly solve the independence paradox.

What Hinders Perceptual Quality of PSNR-oriented Methods?

no code implementations4 Jan 2022 Tianshuo Xu, Peng Mi, Xiawu Zheng, Lijiang Li, Fei Chao, Guannan Jiang, Wei zhang, Yiyi Zhou, Rongrong Ji

E. g, in EDSR, our proposed method achieves 3. 60$\times$ faster learning speed compared to a GAN-based method with a subtle degradation in visual quality.

Contrastive Learning

Dual Contrastive Learning for General Face Forgery Detection

no code implementations27 Dec 2021 Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin L, Rongrong Ji

With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns.

Contrastive Learning

Learning to Learn Transferable Attack

no code implementations10 Dec 2021 Shuman Fang, Jie Li, Xianming Lin, Rongrong Ji

By treating the attack of both specific data and a modified model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization.

Adversarial Attack Data Augmentation +1

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization

1 code implementation17 Nov 2021 Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji

In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase.

Quantization

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

1 code implementation NeurIPS 2021 Shaojie Li, Jie Wu, Xuefeng Xiao, Fei Chao, Xudong Mao, Rongrong Ji

In this work, we revisit the role of discriminator in GAN compression and design a novel generator-discriminator cooperative compression scheme for GAN compression, termed GCC.

Towards Language-guided Visual Recognition via Dynamic Convolutions

no code implementations17 Oct 2021 Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Xinghao Ding, Yongjian Wu, Feiyue Huang, Yue Gao, Rongrong Ji

Based on the LaConv module, we further build the first fully language-driven convolution network, termed as LaConvNet, which can unify the visual recognition and multi-modal reasoning in one forward structure.

Question Answering Referring Expression +3

Long-Range Feature Propagating for Natural Image Matting

1 code implementation25 Sep 2021 Qinglin Liu, Haozhe Xie, Shengping Zhang, Bineng Zhong, Rongrong Ji

Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte.

Ranked #2 on Image Matting on Composition-1K (using extra training data)

Image Matting

OMPQ: Orthogonal Mixed Precision Quantization

1 code implementation16 Sep 2021 Yuexiao Ma, Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Yongjian Wu, Yunsheng Wu, Guannan Jiang, Wei zhang, Rongrong Ji

Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.

AutoML Quantization

Fine-grained Data Distribution Alignment for Post-Training Quantization

1 code implementation9 Sep 2021 Yunshan Zhong, Mingbao Lin, Mengzhao Chen, Ke Li, Yunhang Shen, Fei Chao, Yongjian Wu, Feiyue Huang, Rongrong Ji

To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and we propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization.

Quantization

An Information Theory-inspired Strategy for Automatic Network Pruning

1 code implementation19 Aug 2021 Xiawu Zheng, Yuexiao Ma, Teng Xi, Gang Zhang, Errui Ding, Yuchao Li, Jie Chen, Yonghong Tian, Rongrong Ji

This practically limits the application of model compression when the model needs to be deployed on a wide range of devices.

AutoML Model Compression +1

Towards Robustness Against Natural Language Word Substitutions

1 code implementation ICLR 2021 Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu

Robustness against word substitutions has a well-defined and widely acceptable form, i. e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing.

Natural Language Inference Sentiment Analysis

Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion

1 code implementation14 Jul 2021 Mingbao Lin, Rongrong Ji, Bohong Chen, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Qi Tian

Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance.

Image Classification

GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference

1 code implementation29 Jun 2021 Peng Tu, Yawen Huang, Rongrong Ji, Feng Zheng, Ling Shao

To take advantage of the labeled examples and guide unlabeled data learning, we further propose a mask generation module to generate high-quality pseudo masks for the unlabeled data.

Semi-Supervised Semantic Segmentation

RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words

1 code implementation CVPR 2021 Xuying Zhang, Xiaoshuai Sun, Yunpeng Luo, Jiayi Ji, Yiyi Zhou, Yongjian Wu, Feiyue Huang, Rongrong Ji

Then, we build a BERTbased language model to extract language context and propose Adaptive-Attention (AA) module on top of a transformer decoder to adaptively measure the contribution of visual and language cues before making decisions for word prediction.

Image Captioning Language Modelling +2

Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification

no code implementations CVPR 2021 Qiong Wu, Pingyang Dai, Jie Chen, Chia-Wen Lin, Yongjian Wu, Feiyue Huang, Bineng Zhong, Rongrong Ji

In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.

Person Re-Identification

HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

1 code implementation18 Jun 2021 YuHan Wang, Xu Chen, Junwei Zhu, Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Yongjian Wu, Feiyue Huang, Rongrong Ji

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results.

3D Face Reconstruction Face Recognition +1

You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient

1 code implementation4 Jun 2021 Shaokun Zhang, Xiawu Zheng, Chenyi Yang, Yuchao Li, Yan Wang, Fei Chao, Mengdi Wang, Shen Li, Jun Yang, Rongrong Ji

Motivated by the necessity of efficient inference across various constraints on BERT, we propose a novel approach, YOCO-BERT, to achieve compress once and deploy everywhere.

AutoML Model Compression

1xN Pattern for Pruning Convolutional Neural Networks

1 code implementation31 May 2021 Mingbao Lin, Yuxin Zhang, Yuchao Li, Bohong Chen, Fei Chao, Mengdi Wang, Shen Li, Yonghong Tian, Rongrong Ji

We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations.

Network Pruning

Local Relation Learning for Face Forgery Detection

no code implementations6 May 2021 Shen Chen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, Rongrong Ji

Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern.

ISTR: End-to-End Instance Segmentation with Transformers

1 code implementation3 May 2021 Jie Hu, Liujuan Cao, Yao Lu, Shengchuan Zhang, Yan Wang, Ke Li, Feiyue Huang, Ling Shao, Rongrong Ji

However, such an upgrade is not applicable to instance segmentation, due to its significantly higher output dimensions compared to object detection.

Instance Segmentation Object Detection +1

Carrying out CNN Channel Pruning in a White Box

1 code implementation24 Apr 2021 Yuxin Zhang, Mingbao Lin, Chia-Wen Lin, Jie Chen, Feiyue Huang, Yongjian Wu, Yonghong Tian, Rongrong Ji

Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w. r. t.

Image Classification

Lottery Jackpots Exist in Pre-trained Models

2 code implementations18 Apr 2021 Yuxin Zhang, Mingbao Lin, Fei Chao, Yan Wang, Ke Li, Yunhang Shen, Yongjian Wu, Rongrong Ji

In this paper, we show that high-performing and sparse sub-networks without the involvement of weight tuning, termed "lottery jackpots", exist in pre-trained models with unexpanded width.

Network Pruning

Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning

no code implementations6 Apr 2021 Boyu Yang, Mingbao Lin, Binghao Liu, Mengying Fu, Chang Liu, Rongrong Ji, Qixiang Ye

By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization.

class-incremental learning Incremental Learning

Distilling a Powerful Student Model via Online Knowledge Distillation

1 code implementation26 Mar 2021 Shaojie Li, Mingbao Lin, Yan Wang, Yongjian Wu, Yonghong Tian, Ling Shao, Rongrong Ji

Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one.

Knowledge Distillation

On Evolving Attention Towards Domain Adaptation

no code implementations25 Mar 2021 Kekai Sheng, Ke Li, Xiawu Zheng, Jian Liang, WeiMing Dong, Feiyue Huang, Rongrong Ji, Xing Sun

However, considering that the configuration of attention, i. e., the type and the position of attention module, affects the performance significantly, it is more generalized to optimize the attention configuration automatically to be specialized for arbitrary UDA scenario.

Partial Domain Adaptation Unsupervised Domain Adaptation

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

2 code implementations CVPR 2021 Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye

Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.

Few-Shot Object Detection

Image-to-image Translation via Hierarchical Style Disentanglement

1 code implementation CVPR 2021 Xinyang Li, Shengchuan Zhang, Jie Hu, Liujuan Cao, Xiaopeng Hong, Xudong Mao, Feiyue Huang, Yongjian Wu, Rongrong Ji

Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks.

Disentanglement Multimodal Unsupervised Image-To-Image Translation +1

SiMaN: Sign-to-Magnitude Network Binarization

1 code implementation16 Feb 2021 Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Fei Chao, Mingliang Xu, Chia-Wen Lin, Ling Shao

In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.

Binarization

Network Pruning using Adaptive Exemplar Filters

1 code implementation20 Jan 2021 Mingbao Lin, Rongrong Ji, Shaojie Li, Yan Wang, Yongjian Wu, Feiyue Huang, Qixiang Ye

Inspired by the face recognition community, we use a message passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters.

Face Recognition Network Pruning

Dual-Level Collaborative Transformer for Image Captioning

1 code implementation16 Jan 2021 Yunpeng Luo, Jiayi Ji, Xiaoshuai Sun, Liujuan Cao, Yongjian Wu, Feiyue Huang, Chia-Wen Lin, Rongrong Ji

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning.

Image Captioning Object Detection

Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models

no code implementations ICCV 2021 Jie Li, Rongrong Ji, Peixian Chen, Baochang Zhang, Xiaopeng Hong, Ruixin Zhang, Shaoxin Li, Jilin Li, Feiyue Huang, Yongjian Wu

A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial.

Dimensionality Reduction

TRAR: Routing the Attention Spans in Transformer for Visual Question Answering

1 code implementation ICCV 2021 Yiyi Zhou, Tianhe Ren, Chaoyang Zhu, Xiaoshuai Sun, Jianzhuang Liu, Xinghao Ding, Mingliang Xu, Rongrong Ji

Due to the superior ability of global dependency modeling, Transformer and its variants have become the primary choice of many vision-and-language tasks.

Question Answering Referring Expression +3

EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS

no code implementations ICCV 2021 Qinqin Zhou, Xiawu Zheng, Liujuan Cao, Bineng Zhong, Teng Xi, Gang Zhang, Errui Ding, Mingliang Xu, Rongrong Ji

EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked.

Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network

no code implementations13 Dec 2020 Jiayi Ji, Yunpeng Luo, Xiaoshuai Sun, Fuhai Chen, Gen Luo, Yongjian Wu, Yue Gao, Rongrong Ji

The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation.

Image Captioning

Fast Class-wise Updating for Online Hashing

no code implementations1 Dec 2020 Mingbao Lin, Rongrong Ji, Xiaoshuai Sun, Baochang Zhang, Feiyue Huang, Yonghong Tian, DaCheng Tao

To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches.

online learning

UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection

1 code implementation NeurIPS 2020 Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang

In this paper, we propose a unified WSOD framework, termed UWSOD, to develop a high-capacity general detection model with only image-level labels, which is self-contained and does not require external modules or additional supervision.

Object Proposal Generation Weakly Supervised Object Detection

Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation

1 code implementation17 Nov 2020 Shaojie Li, Mingbao Lin, Yan Wang, Fei Chao, Ling Shao, Rongrong Ji

The latter simultaneously distills informative attention maps from both the generator and discriminator of a pre-trained model to the searched generator, effectively stabilizing the adversarial training of our light-weight model.

Translation

PAMS: Quantized Super-Resolution via Parameterized Max Scale

1 code implementation ECCV 2020 Huixia Li, Chenqian Yan, Shaohui Lin, Xiawu Zheng, Yuchao Li, Baochang Zhang, Fan Yang, Rongrong Ji

Specifically, most state-of-the-art SR models without batch normalization have a large dynamic quantization range, which also serves as another cause of performance drop.

Quantization Super-Resolution +1

Rotated Binary Neural Network

2 code implementations NeurIPS 2020 Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin

In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.

Binarization Quantization

Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

2 code implementations CVPR 2021 Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J. Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, Xing Sun

Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes.

Frame Representation Learning +1

Binarized Neural Architecture Search for Efficient Object Recognition

no code implementations8 Sep 2020 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, Rongrong Ji, David Doermann, Guodong Guo

In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.

Edge-computing Face Recognition +2

Dual Channel Hypergraph Collaborative Filtering

no code implementations SIGKDD 2020 Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, Yue Gao.

Second, the hypergraph structure is employed for modeling users and items with explicit hybrid high-order correlations.

Collaborative Filtering Recommendation Systems

Anti-Bandit Neural Architecture Search for Model Defense

1 code implementation ECCV 2020 Hanlin Chen, Baochang Zhang, Song Xue, Xuan Gong, Hong Liu, Rongrong Ji, David Doermann

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks.

Denoising Neural Architecture Search

Dual Distribution Alignment Network for Generalizable Person Re-Identification

1 code implementation27 Jul 2020 Peixian Chen, Pingyang Dai, Jianzhuang Liu, Feng Zheng, Qi Tian, Rongrong Ji

Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.

Domain Generalization Generalizable Person Re-identification

Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

2 code implementations ECCV 2020 Yunpeng Zhai, Qixiang Ye, Shijian Lu, Mengxi Jia, Rongrong Ji, Yonghong Tian

Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.

Domain Adaptive Person Re-Identification Ensemble Learning +1

Cogradient Descent for Bilinear Optimization

no code implementations CVPR 2020 Li'an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Guodong Guo, Rongrong Ji

Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.

Image Reconstruction Network Pruning

Rethinking Performance Estimation in Neural Architecture Search

1 code implementation CVPR 2020 Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian

In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space.

Neural Architecture Search

Projection & Probability-Driven Black-Box Attack

1 code implementation CVPR 2020 Jie Li, Rongrong Ji, Hong Liu, Jianzhuang Liu, Bineng Zhong, Cheng Deng, Qi Tian

For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.

Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation

1 code implementation26 Apr 2020 Hao Cheng, Fanxu Meng, Ke Li, Yuting Gao, Guangming Lu, Xing Sun, Rongrong Ji

To gain a universal improvement on both valid and invalid filters, we compensate grafting with distillation (\textbf{Cultivation}) to overcome the drawback of grafting .

Architecture Disentanglement for Deep Neural Networks

1 code implementation ICCV 2021 Jie Hu, Liujuan Cao, Qixiang Ye, Tong Tong, Shengchuan Zhang, Ke Li, Feiyue Huang, Rongrong Ji, Ling Shao

Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs.

AutoML Disentanglement

ASFD: Automatic and Scalable Face Detector

no code implementations25 Mar 2020 Bin Zhang, Jian Li, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yili Xia, Wenjiang Pei, Rongrong Ji

In this paper, we propose a novel Automatic and Scalable Face Detector (ASFD), which is based on a combination of neural architecture search techniques as well as a new loss design.

Neural Architecture Search

Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation

1 code implementation CVPR 2020 Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, Rongrong Ji

In addition, we address a key challenge in this multi-task setup, i. e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS).

Referring Expression Referring Expression Comprehension +1

Siamese Box Adaptive Network for Visual Tracking

2 code implementations CVPR 2020 Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji

Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target.

Visual Tracking

HRank: Filter Pruning using High-Rank Feature Map

2 code implementations CVPR 2020 Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, Ling Shao

The principle behind our pruning is that low-rank feature maps contain less information, and thus pruned results can be easily reproduced.

Network Pruning

Improving Face Recognition from Hard Samples via Distribution Distillation Loss

2 code implementations ECCV 2020 Yuge Huang, Pengcheng Shen, Ying Tai, Shaoxin Li, Xiaoming Liu, Jilin Li, Feiyue Huang, Rongrong Ji

To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.

Face Recognition

Channel Pruning via Automatic Structure Search

1 code implementation23 Jan 2020 Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, Yonghong Tian

In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i. e., channel number in each layer, rather than selecting "important" channels as previous works did.

Filter Sketch for Network Pruning

1 code implementation23 Jan 2020 Mingbao Lin, Liujuan Cao, Shaojie Li, Qixiang Ye, Yonghong Tian, Jianzhuang Liu, Qi Tian, Rongrong Ji

Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure.

Network Pruning

Filter Grafting for Deep Neural Networks

2 code implementations CVPR 2020 Fanxu Meng, Hao Cheng, Ke Li, Zhixin Xu, Rongrong Ji, Xing Sun, Gaungming Lu

To better perform the grafting process, we develop an entropy-based criterion to measure the information of filters and an adaptive weighting strategy for balancing the grafted information among networks.

A Real-time Global Inference Network for One-stage Referring Expression Comprehension

1 code implementation7 Dec 2019 Yiyi Zhou, Rongrong Ji, Gen Luo, Xiaoshuai Sun, Jinsong Su, Xinghao Ding, Chia-Wen Lin, Qi Tian

Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description.

Referring Expression Referring Expression Comprehension

Variational Structured Semantic Inference for Diverse Image Captioning

no code implementations NeurIPS 2019 Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang

To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema.

Image Captioning

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

no code implementations26 Nov 2019 Kekai Sheng, Wei-Ming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma

In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.

Binarized Neural Architecture Search

1 code implementation25 Nov 2019 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, David Doermann, Rongrong Ji

A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.

Neural Architecture Search

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

3 code implementations11 Nov 2019 Chuming Lin, Jian Li, Yabiao Wang, Ying Tai, Donghao Luo, Zhipeng Cui, Chengjie Wang, Jilin Li, Feiyue Huang, Rongrong Ji

In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.

General Classification Optical Flow Estimation

Beyond Universal Person Re-ID Attack

no code implementations30 Oct 2019 Wenjie Ding, Xing Wei, Rongrong Ji, Xiaopeng Hong, Qi Tian, Yihong Gong

We propose a \emph{more universal} adversarial perturbation (MUAP) method for both image-agnostic and model-insensitive person Re-ID attack.

General Classification Person Re-Identification

Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation

no code implementations CVPR 2019 Chunlei Liu, Wenrui Ding, Xin Xia, Baochang Zhang, Jiaxin Gu, Jianzhuang Liu, Rongrong Ji, David Doermann

The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).

Hadamard Codebook Based Deep Hashing

no code implementations21 Oct 2019 Shen Chen, Liujuan Cao, Mingbao Lin, Yan Wang, Xiaoshuai Sun, Chenglin Wu, Jingfei Qiu, Rongrong Ji

Specifically, we utilize an off-the-shelf algorithm to generate a binary Hadamard codebook to satisfy the requirement of bit independence and bit balance, which subsequently serves as the desired outputs of the hash functions learning.

Image Retrieval

Semantic-aware Image Deblurring

no code implementations9 Oct 2019 Fuhai Chen, Rongrong Ji, Chengpeng Dai, Xiaoshuai Sun, Chia-Wen Lin, Jiayi Ji, Baochang Zhang, Feiyue Huang, Liujuan Cao

Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC).

Deblurring Image Captioning +1

Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

1 code implementation ICCV 2019 Yingyue Xu, Dan Xu, Xiaopeng Hong, Wanli Ouyang, Rongrong Ji, Min Xu, Guoying Zhao

We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions.

RGB Salient Object Detection Salient Object Detection

FreeAnchor: Learning to Match Anchors for Visual Object Detection

3 code implementations NeurIPS 2019 Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye

In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner.

Object Detection

Scoot: A Perceptual Metric for Facial Sketches

1 code implementation ICCV 2019 Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Yun Liu, Ming-Ming Cheng, Bo Ren, Paul L. Rosin, Rongrong Ji

In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.

Face Sketch Synthesis SSIM

Bayesian Optimized 1-Bit CNNs

no code implementations ICCV 2019 Jiaxin Gu, Junhe Zhao, Xiao-Long Jiang, Baochang Zhang, Jianzhuang Liu, Guodong Guo, Rongrong Ji

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models.

Scene-based Factored Attention for Image Captioning

no code implementations7 Aug 2019 Chen Shen, Rongrong Ji, Fuhai Chen, Xiaoshuai Sun, Xiangming Li

Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image.

Image Captioning

Semi-Supervised Adversarial Monocular Depth Estimation

no code implementations6 Aug 2019 Rongrong Ji, Ke Li, Yan Wang, Xiaoshuai Sun, Feng Guo, Xiaowei Guo, Yongjian Wu, Feiyue Huang, Jiebo Luo

In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.

Monocular Depth Estimation

Interpretable Neural Network Decoupling

no code implementations ECCV 2020 Yuchao Li, Rongrong Ji, Shaohui Lin, Baochang Zhang, Chenqian Yan, Yongjian Wu, Feiyue Huang, Ling Shao

More specifically, we introduce a novel architecture controlling module in each layer to encode the network architecture by a vector.

Supervised Online Hashing via Similarity Distribution Learning

no code implementations31 May 2019 Mingbao Lin, Rongrong Ji, Shen Chen, Feng Zheng, Xiaoshuai Sun, Baochang Zhang, Liujuan Cao, Guodong Guo, Feiyue Huang

In this paper, we propose to model the similarity distributions between the input data and the hashing codes, upon which a novel supervised online hashing method, dubbed as Similarity Distribution based Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship in the produced Hamming space.

Dynamic Distribution Pruning for Efficient Network Architecture Search

1 code implementation28 May 2019 Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

The search space is dynamically pruned every a few epochs to update this distribution, and the optimal neural architecture is obtained when there is only one structure remained.

Neural Architecture Search

Multinomial Distribution Learning for Effective Neural Architecture Search

1 code implementation ICCV 2019 Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, Qi Tian

Therefore, NAS can be transformed to a multinomial distribution learning problem, i. e., the distribution is optimized to have a high expectation of the performance.

Neural Architecture Search

Hadamard Matrix Guided Online Hashing

1 code implementation11 May 2019 Mingbao Lin, Rongrong Ji, Hong Liu, Xiaoshuai Sun, Shen Chen, Qi Tian

We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code.

Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning

1 code implementation29 Apr 2019 Xinyang Li, Jie Hu, Shengchuan Zhang, Xiaopeng Hong, Qixiang Ye, Chenglin Wu, Rongrong Ji

Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised learning process by translating attributes of labeled data to unlabeled data, and then reconstructing the unlabeled data by a cycle consistency operation.

Disentanglement Image-to-Image Translation +1

Supervised Online Hashing via Hadamard Codebook Learning

1 code implementation28 Apr 2019 Mingbao Lin, Rongrong Ji, Hong Liu, Yongjian Liu

Notably, the proposed HCOH can be embedded with supervised labels and it not limited to a predefined category number.

Semantic Similarity Semantic Textual Similarity

Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

1 code implementation CVPR 2019 Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, David Doermann

In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner.

Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection

no code implementations27 Feb 2019 Yao Liu, Ying Tai, Jilin Li, Shouhong Ding, Chengjie Wang, Feiyue Huang, Dongyang Li, Wenshuai Qi, Rongrong Ji

In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users.

Face Anti-Spoofing General Classification

Towards Optimal Discrete Online Hashing with Balanced Similarity

1 code implementation29 Jan 2019 Mingbao Lin, Rongrong Ji, Hong Liu, Xiaoshuai Sun, Yongjian Wu, Yunsheng Wu

In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework.

Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

1 code implementation23 Jan 2019 Shaohui Lin, Rongrong Ji, Yuchao Li, Cheng Deng, Xuelong. Li

In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries.

Domain Adaptation Object Detection +1

Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression

1 code implementation CVPR 2019 Yuchao Li, Shaohui Lin, Baochang Zhang, Jianzhuang Liu, David Doermann, Yongjian Wu, Feiyue Huang, Rongrong Ji

The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression.

Model Compression

Towards Visual Feature Translation

1 code implementation CVPR 2019 Jie Hu, Rongrong Ji, Hong Liu, Shengchuan Zhang, Cheng Deng, Qi Tian

In this paper, we make the first attempt towards visual feature translation to break through the barrier of using features across different visual search systems.

Translation

PVRNet: Point-View Relation Neural Network for 3D Shape Recognition

no code implementations2 Dec 2018 Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao

More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the point-multi-view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation.

3D Shape Classification 3D Shape Recognition

Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training

1 code implementation CVPR 2019 Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, Rongrong Ji

Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.

Person Re-Identification

Hypergraph Neural Networks

1 code implementation25 Sep 2018 Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.

Object Recognition Representation Learning

PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition

no code implementations23 Aug 2018 Haoxuan You, Yifan Feng, Rongrong Ji, Yue Gao

With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.

3D Object Recognition 3D Shape Classification +2

GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints

no code implementations CVPR 2018 Fuhai Chen, Rongrong Ji, Xiaoshuai Sun, Yongjian Wu, Jinsong Su

In offline optimization, we adopt an end-to-end formulation, which jointly trains the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model.

Image Captioning

Modulated Convolutional Networks

no code implementations CVPR 2018 Xiaodi Wang, Baochang Zhang, Ce Li, Rongrong Ji, Jungong Han, Xian-Bin Cao, Jianzhuang Liu

In this paper, we propose new Modulated Convolutional Networks (MCNs) to improve the portability of CNNs via binarized filters.

GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition

no code implementations CVPR 2018 Yifan Feng, Zizhao Zhang, Xibin Zhao, Rongrong Ji, Yue Gao

The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i. e., from the view level, the group level and the shape level, which are organized using a grouping strategy.

3D Shape Classification 3D Shape Recognition +1

Generative Adversarial Learning Towards Fast Weakly Supervised Detection

no code implementations CVPR 2018 Yunhan Shen, Rongrong Ji, Shengchuan Zhang, WangMeng Zuo, Yan Wang

Without the need of annotating bounding boxes, the existing methods usually follow a two/multi-stage pipeline with an online compulsive stage to extract object proposals, which is an order of magnitude slower than fast fully supervised object detectors such as SSD [31] and YOLO [34].

Weakly Supervised Object Detection

CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation

no code implementations28 May 2018 Xiao Liu, Shengchuan Zhang, Hong Liu, Xin Liu, Cheng Deng, Rongrong Ji

In principle, CerfGAN contains a novel component, i. e., a multi-class discriminator (MCD), which gives the model an extremely powerful ability to match multiple translation mappings.

Face Hallucination Image-to-Image Translation +2

Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure

1 code implementation9 Apr 2018 Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Ming-Ming Cheng, Bo Ren, Rongrong Ji, Paul L. Rosin

However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.

Face Sketch Synthesis

Asynchronous Bidirectional Decoding for Neural Machine Translation

2 code implementations16 Jan 2018 Xiangwen Zhang, Jinsong Su, Yue Qin, Yang Liu, Rongrong Ji, Hongji Wang

The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation.

Machine Translation Translation

Action-Attending Graphic Neural Network

no code implementations17 Nov 2017 Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Rongrong Ji, Jian Yang

The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision.

Action Analysis Action Recognition +1

Cross-Modality Binary Code Learning via Fusion Similarity Hashing

no code implementations CVPR 2017 Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang, Baochang Zhang

In this paper, we propose a hashing scheme, termed Fusion Similarity Hashing (FSH), which explicitly embeds the graph-based fusion similarity across modalities into a common Hamming space.

Ordinal Constrained Binary Code Learning for Nearest Neighbor Search

no code implementations19 Nov 2016 Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang

By given a large-scale training data set, it is very expensive to embed such ranking tuples in binary code learning.

Small Data Image Classification

Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation

no code implementations25 Sep 2016 Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu

Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.

Machine Translation Translation

Variational Neural Discourse Relation Recognizer

1 code implementation EMNLP 2016 Biao Zhang, Deyi Xiong, Jinsong Su, Qun Liu, Rongrong Ji, Hong Duan, Min Zhang

In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.

Top Rank Supervised Binary Coding for Visual Search

no code implementations ICCV 2015 Dongjin Song, Wei Liu, Rongrong Ji, David A. Meyer, John R. Smith

In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information.

Image Retrieval online learning

Video (GIF) Sentiment Analysis using Large-Scale Mid-Level Ontology

no code implementations2 Jun 2015 Zheng Cai, Donglin Cao, Rongrong Ji

However, GIF sentiment analysis is quite challenging, not only because it hinges on spatio-temporal visual contentabstraction, but also for the relationship between such abstraction and final sentiment remains unknown. In this paper, we dedicated to find out such relationship. We proposed a SentiPairSequence basedspatiotemporal visual sentiment ontology, which forms the midlevel representations for GIFsentiment.

Sentiment Analysis

Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery

no code implementations CVPR 2015 Wei Liu, Rongrong Ji, Shaozi Li

In particular, we slide a 3D detection window in the 3D point cloud to match the exemplar shape, which the lack of training data in 3D domain is conquered via (1) We collect 3D CAD models and 2D positive samples from Internet.

3D Object Detection

Understanding Image Structure via Hierarchical Shape Parsing

no code implementations CVPR 2015 Xian-Ming Liu, Rongrong Ji, Changhu Wang, Wei Liu, Bineng Zhong, Thomas S. Huang

A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.

Label Propagation from ImageNet to 3D Point Clouds

no code implementations CVPR 2013 Yan Wang, Rongrong Ji, Shih-Fu Chang

Our approach shows further major gains in accuracy when the training data from the target scenes is used, outperforming state-ofthe-art approaches with far better efficiency.

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