Search Results for author: Lingqiao Liu

Found 63 papers, 9 papers with code

Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation

no code implementations5 Aug 2021 Duo Peng, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Jun Liu

In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.

Domain Generalization Semantic Segmentation

Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

1 code implementation22 May 2021 Yingjie Zhou, Xucheng Song, Yanru Zhang, Fanxing Liu, Ce Zhu, Lingqiao Liu

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.

Anomaly Detection

Center Prediction Loss for Re-identification

no code implementations30 Apr 2021 Lu Yang, Yunlong Wang, Lingqiao Liu, Peng Wang, Lu Chi, Zehuan Yuan, Changhu Wang, Yanning Zhang

In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.

CAT: Cross-Attention Transformer for One-Shot Object Detection

no code implementations30 Apr 2021 Weidong Lin, Yuyan Deng, Yang Gao, Ning Wang, Jinghao Zhou, Lingqiao Liu, Lei Zhang, Peng Wang

Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison.

One-Shot Object Detection Semantic Similarity +1

Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification

no code implementations9 Mar 2021 Lu Yang, Hongbang Liu, Jinghao Zhou, Lingqiao Liu, Lei Zhang, Peng Wang, Yanning Zhang

Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under different viewpoints.

Vehicle Re-Identification

Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems

no code implementations EMNLP 2021 Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, Chunxu Shen

Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context.

Task-Oriented Dialogue Systems

Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks

no code implementations ECCV 2020 Yan Liu, Lingqiao Liu, Peng Wang, Pingping Zhang, Yinjie Lei

Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain.

Crowd Counting

Towards Using Count-level Weak Supervision for Crowd Counting

no code implementations29 Feb 2020 Yinjie Lei, Yan Liu, Pingping Zhang, Lingqiao Liu

Most existing crowd counting methods require object location-level annotation, i. e., placing a dot at the center of an object.

Crowd Counting

Semi-supervised Learning via Conditional Rotation Angle Estimation

no code implementations9 Jan 2020 Hai-Ming Xu, Lingqiao Liu, Dong Gong

Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target.

Self-Supervised Learning

To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions

no code implementations10 Dec 2019 Jun-Jie Zhang, Lingqiao Liu, Peng Wang, Chunhua Shen

Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training.

Auxiliary Learning Self-Supervised Learning

Improving Distant Supervised Relation Extraction by Dynamic Neural Network

no code implementations15 Nov 2019 Yanjie Gou, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Xi Peng

To account for this style shift, the model should adjust its parameters in accordance with entity types.

Relation Extraction

Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction

no code implementations12 Nov 2019 Liangyi Kang, Jie Liu, Lingqiao Liu, Qinfeng Shi, Dan Ye

Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation.

Structured Binary Neural Networks for Image Recognition

no code implementations22 Sep 2019 Bohan Zhuang, Chunhua Shen, Mingkui Tan, Peng Chen, Lingqiao Liu, Ian Reid

Experiments on both classification, semantic segmentation and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature.

Object Detection Quantization +1

In defense of OSVOS

no code implementations19 Aug 2019 Yu Liu, Yutong Dai, Anh-Dzung Doan, Lingqiao Liu, Ian Reid

Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i. e. online fine-tuning on the target instance.

Depth Estimation Fine-tuning +5

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations

no code implementations10 Aug 2019 Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, Chunhua Shen

Furthermore, we propose a second progressive quantization scheme which gradually decreases the bit-width from high-precision to low-precision during training.

Knowledge Distillation Quantization

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

no code implementations29 Jul 2019 Damien Teney, Peng Wang, Jiewei Cao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.

Visual Reasoning

Training Quantized Neural Networks with a Full-precision Auxiliary Module

no code implementations CVPR 2020 Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid

In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function.

Image Classification Object Detection +1

RPC: A Large-Scale Retail Product Checkout Dataset

no code implementations22 Jan 2019 Xiu-Shen Wei, Quan Cui, Lei Yang, Peng Wang, Lingqiao Liu

The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products.

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

no code implementations12 Jan 2019 Yu Liu, Lingqiao Liu, Hamid Rezatofighi, Thanh-Toan Do, Qinfeng Shi, Ian Reid

As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.

Object Detection

Mask-aware networks for crowd counting

no code implementations18 Dec 2018 Shengqin Jiang, Xiaobo Lu, Yinjie Lei, Lingqiao Liu

Our rationale is that the mask prediction could be better modeled as a binary segmentation problem and the difficulty of estimating the density could be reduced if the mask is known.

Crowd Counting

Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification

no code implementations11 Dec 2018 Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu, Chunhua Shen, Jianxin Wu

Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.

Vehicle Re-Identification

Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

no code implementations CVPR 2019 Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid

In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources.

Classification General Classification +3

Towards Effective Deep Embedding for Zero-Shot Learning

no code implementations30 Aug 2018 Lei Zhang, Peng Wang, Lingqiao Liu, Chunhua Shen, Wei Wei, Yannning Zhang, Anton Van Den Hengel

Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier.

Zero-Shot Learning

Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

no code implementations5 Jun 2018 Lei Zhang, Peng Wang, Chunhua Shen, Lingqiao Liu, Wei Wei, Yanning Zhang, Anton Van Den Hengel

In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.

Image Super-Resolution

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

no code implementations11 May 2018 Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu

To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.

Few-Shot Learning Fine-Grained Image Recognition

Towards Effective Low-bitwidth Convolutional Neural Networks

no code implementations CVPR 2018 Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid

This paper tackles the problem of training a deep convolutional neural network with both low-precision weights and low-bitwidth activations.


Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization

no code implementations1 Nov 2017 Yu Chen, Chunhua Shen, Hao Chen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang

In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity.

Pose Estimation

Towards Context-Aware Interaction Recognition for Visual Relationship Detection

1 code implementation ICCV 2017 Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Ian Reid

The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent.

Visual Relationship Detection

Visually Aligned Word Embeddings for Improving Zero-shot Learning

no code implementations18 Jul 2017 Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE).

Semantic Similarity Semantic Textual Similarity +2

Multi-Attention Network for One Shot Learning

no code implementations CVPR 2017 Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton Van Den Hengel, Heng Tao Shen

One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image.

One-Shot Learning Word Embeddings

Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation

no code implementations25 May 2017 Tong Shen, Guosheng Lin, Lingqiao Liu, Chunhua Shen, Ian Reid

Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation.

Weakly-Supervised Semantic Segmentation

Towards Context-aware Interaction Recognition

no code implementations18 Mar 2017 Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Ian Reid

Recognizing how objects interact with each other is a crucial task in visual recognition.

Deep Learning Features at Scale for Visual Place Recognition

no code implementations18 Jan 2017 Zetao Chen, Adam Jacobson, Niko Sunderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian Reid, Michael Milford

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks.

Visual Place Recognition

From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur

no code implementations CVPR 2017 Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi

The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.

Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

no code implementations1 Aug 2016 ZongYuan Ge, Chris McCool, Conrad Sanderson, Peng Wang, Lingqiao Liu, Ian Reid, Peter Corke

Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification.

Classification General Classification +1

Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps

no code implementations22 Jun 2016 Jiewei Cao, Lingqiao Liu, Peng Wang, Zi Huang, Chunhua Shen, Heng Tao Shen

Instance retrieval requires one to search for images that contain a particular object within a large corpus.

What's Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution

no code implementations CVPR 2016 Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton Van Den Hengel, Heng Tao Shen

The key observation motivating our approach is that "regular object" images, "unusual object" images and "other objects" images exhibit different region-level scores in terms of both the score values and the spatial distributions.

Gaussian Processes Object Detection

Less is more: zero-shot learning from online textual documents with noise suppression

no code implementations CVPR 2016 Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes.

Zero-Shot Learning

Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution

no code implementations14 Feb 2016 Peng Wang, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel, Heng Tao Shen

To address this problem, we propose a novel approach by inspecting the distribution of the detection scores at multiple image regions based on the detector trained from the "regular object" and "other objects".

Gaussian Processes

Order-aware Convolutional Pooling for Video Based Action Recognition

no code implementations31 Jan 2016 Peng Wang, Lingqiao Liu, Chunhua Shen, Heng Tao Shen

Most video based action recognition approaches create the video-level representation by temporally pooling the features extracted at each frame.

Action Recognition

Compositional Model based Fisher Vector Coding for Image Classification

no code implementations16 Jan 2016 Lingqiao Liu, Peng Wang, Chunhua Shen, Lei Wang, Anton Van Den Hengel, Chao Wang, Heng Tao Shen

To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions.

Classification General Classification +1

Cross-convolutional-layer Pooling for Image Recognition

no code implementations4 Oct 2015 Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation.

General Classification Image Classification

Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data

no code implementations23 Jun 2015 Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN).

Mining Mid-level Visual Patterns with Deep CNN Activations

1 code implementation21 Jun 2015 Yao Li, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

The purpose of mid-level visual element discovery is to find clusters of image patches that are both representative and discriminative.

Object Classification

What value do explicit high level concepts have in vision to language problems?

1 code implementation CVPR 2016 Qi Wu, Chunhua Shen, Lingqiao Liu, Anthony Dick, Anton Van Den Hengel

Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Image Captioning Question Answering +1

Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition

no code implementations4 Mar 2015 Peng Wang, Yuanzhouhan Cao, Chunhua Shen, Lingqiao Liu, Heng Tao Shen

One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs.

Action Recognition Image Classification

The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification

1 code implementation CVPR 2015 Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

This paper, however, advocates that if used appropriately convolutional layer activations can be turned into a powerful image representation which enjoys many advantages over fully-connected layer activations.

General Classification Image Classification

Mid-level Deep Pattern Mining

no code implementations CVPR 2015 Yao Li, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used.

Object Classification

Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors

no code implementations NeurIPS 2014 Lingqiao Liu, Chunhua Shen, Lei Wang, Anton Van Den Hengel, Chao Wang

By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC).

Fine-Grained Image Classification General Classification +1

A Generalized Probabilistic Framework for Compact Codebook Creation

no code implementations30 Jan 2014 Lingqiao Liu, Lei Wang, Chunhua Shen

In the third criterion, which shows the best merging performance, we propose a max-margin-based parameter estimation method and apply it with multinomial distribution.

Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

no code implementations CVPR 2013 Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivitybased biomarkers for the Alzheimer's disease (AD).

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