Search Results for author: Zhiqiang Shen

Found 48 papers, 21 papers with code

Stereo Neural Vernier Caliper

1 code implementation21 Mar 2022 Shichao Li, Zechun Liu, Zhiqiang Shen, Kwang-Ting Cheng

We propose a new object-centric framework for learning-based stereo 3D object detection.

3D Object Detection

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

1 code implementation3 Jan 2022 Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework.

A Fast Knowledge Distillation Framework for Visual Recognition

2 code implementations2 Dec 2021 Zhiqiang Shen, Eric Xing

In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, while training faster than ReLabel since no post-processes such as RoI align and softmax operations are used.

Knowledge Distillation Representation Learning +1

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

1 code implementation29 Nov 2021 Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric Xing, Zhiqiang Shen

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i. e., uniform strategy, due to its superior representational capacity.


Sliced Recursive Transformer

1 code implementation9 Nov 2021 Zhiqiang Shen, Zechun Liu, Eric Xing

The proposed sliced recursive operation allows us to build a transformer with more than 100 or even 1000 layers effortlessly under a still small size (13~15M), to avoid difficulties in optimization when the model size is too large.

Image Classification

Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types

no code implementations11 Oct 2021 Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Zhiqiang Shen, Eric P Xing, Yanyan Lan

The core problem is to model how regulatory elements interact with each other and its variability across different cell types.

Multi-modal Self-supervised Pre-training for Large-scale Genome Data

no code implementations NeurIPS Workshop AI4Scien 2021 Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Yanyan Lan, Zhiqiang Shen, Eric Xing

In this work, we propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT.

How Do Adam and Training Strategies Help BNNs Optimization?

no code implementations21 Jun 2021 Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng

We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs.

COTR: Convolution in Transformer Network for End to End Polyp Detection

no code implementations23 May 2021 Zhiqiang Shen, Chaonan Lin, Shaohua Zheng

Motivated by the detection transformer (DETR), COTR is constituted by a CNN for feature extraction, transformer encoder layers interleaved with convolutional layers for feature encoding and recalibration, transformer decoder layers for object querying, and a feed-forward network for detection prediction.

Object Detection

"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

1 code implementation16 Apr 2021 Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

However, the BN layer is costly to calculate and is typically implemented with non-binary parameters, leaving a hurdle for the efficient implementation of BNN training.

Image Classification

Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

no code implementations CVPR 2021 Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, Marios Savvides

In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection.

Few-Shot Object Detection

Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation

no code implementations22 Feb 2021 Shaohua Zheng, Zhiqiang Shen, Chenhao Peia, Wangbin Ding, Haojin Lin, Jiepeng Zheng, Lin Pan, Bin Zheng, Liqin Huang

In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists.

Lung Cancer Diagnosis

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

1 code implementation CVPR 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Lei Huang, Kwang-Ting Cheng, Marios Savvides

In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels.

Contrastive Learning Self-Supervised Learning

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning

no code implementations8 Feb 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Marios Savvides, Kwang-Ting Cheng

A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i. e. learning to transfer in few-shot scenario.)

Few-Shot Learning

Contrast and Order Representations for Video Self-Supervised Learning

no code implementations ICCV 2021 Kai Hu, Jie Shao, YuAn Liu, Bhiksha Raj, Marios Savvides, Zhiqiang Shen

To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.

Action Recognition Self-Supervised Learning

DR 21 South Filament: a Parsec-sized Dense Gas Accretion Flow onto the DR 21 Massive Young Cluster

no code implementations4 Dec 2020 Bo Hu, Keping Qiu, Yue Cao, Junhao Liu, Yuwei Wang, Guangxing Li, Zhiqiang Shen, Juan Li, Junzhi Wang, Bin Li, Jian Dong

DR21 south filament (DR21SF) is a unique component of the giant network of filamentary molecular clouds in the north region of Cygnus X complex.

Astrophysics of Galaxies

Conditional Link Prediction of Category-Implicit Keypoint Detection

no code implementations29 Nov 2020 Ellen Yi-Ge, Rui Fan, Zechun Liu, Zhiqiang Shen

Keypoints of objects reflect their concise abstractions, while the corresponding connection links (CL) build the skeleton by detecting the intrinsic relations between keypoints.

Keypoint Detection Link Prediction

Online Ensemble Model Compression using Knowledge Distillation

1 code implementation ECCV 2020 Devesh Walawalkar, Zhiqiang Shen, Marios Savvides

This paper presents a novel knowledge distillation based model compression framework consisting of a student ensemble.

Knowledge Distillation Model Compression

MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks

1 code implementation17 Sep 2020 Zhiqiang Shen, Marios Savvides

Our result can be regarded as a strong baseline using knowledge distillation, and to our best knowledge, this is also the first method that is able to boost vanilla ResNet-50 to surpass 80% on ImageNet without architecture modification or additional training data.

Knowledge Distillation

Channel-wise Alignment for Adaptive Object Detection

no code implementations7 Sep 2020 Hang Yang, Shan Jiang, Xinge Zhu, Mingyang Huang, Zhiqiang Shen, Chunxiao Liu, Jianping Shi

Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information.

Instance Segmentation Object Detection +1

Cross-Supervised Object Detection

no code implementations26 Jun 2020 Zitian Chen, Zhiqiang Shen, Jiahui Yu, Erik Learned-Miller

After learning a new object category from image-level annotations (with no object bounding boxes), humans are remarkably good at precisely localizing those objects.

Object Detection

Joint Multi-Dimension Pruning via Numerical Gradient Update

no code implementations18 May 2020 Zechun Liu, Xiangyu Zhang, Zhiqiang Shen, Zhe Li, Yichen Wei, Kwang-Ting Cheng, Jian Sun

To tackle these three naturally different dimensions, we proposed a general framework by defining pruning as seeking the best pruning vector (i. e., the numerical value of layer-wise channel number, spacial size, depth) and construct a unique mapping from the pruning vector to the pruned network structures.

Binarizing MobileNet via Evolution-based Searching

no code implementations CVPR 2020 Hai Phan, Zechun Liu, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, Zhiqiang Shen

Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy.

ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions

1 code implementation ECCV 2020 Zechun Liu, Zhiqiang Shen, Marios Savvides, Kwang-Ting Cheng

In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost.

Solving Missing-Annotation Object Detection with Background Recalibration Loss

2 code implementations12 Feb 2020 Han Zhang, Fangyi Chen, Zhiqiang Shen, Qiqi Hao, Chenchen Zhu, Marios Savvides

In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.

Object Detection

SAPD:Soft Anchor-Point Detector

1 code implementation arXiv 2019 Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, Marios Savvides

In this work, we aim at finding a new balance of speed and accuracy for anchor-free detectors.

Object Detection

Soft Anchor-Point Object Detection

2 code implementations ECCV 2020 Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, Marios Savvides

In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.

Object Detection

SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

1 code implementation6 Nov 2019 Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, Marios Savvides

Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.

Object Detection

Exploring Simple and Transferable Recognition-Aware Image Processing

1 code implementation21 Oct 2019 Zhuang Liu, Hung-Ju Wang, Tinghui Zhou, Zhiqiang Shen, Bingyi Kang, Evan Shelhamer, Trevor Darrell

Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks and training datasets.

Image Retrieval Recommendation Systems

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement

no code implementations22 Aug 2019 Zhiqiang Shen, Zhankui He, Wanyun Cui, Jiahui Yu, Yutong Zheng, Chenchen Zhu, Marios Savvides

In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.

Knowledge Distillation

MoBiNet: A Mobile Binary Network for Image Classification

no code implementations29 Jul 2019 Hai Phan, Dang Huynh, Yihui He, Marios Savvides, Zhiqiang Shen

MobileNet and Binary Neural Networks are two among the most widely used techniques to construct deep learning models for performing a variety of tasks on mobile and embedded platforms. In this paper, we present a simple yet efficient scheme to exploit MobileNet binarization at activation function and model weights.

Binarization Classification +2

Towards Instance-level Image-to-Image Translation

no code implementations CVPR 2019 Zhiqiang Shen, Mingyang Huang, Jianping Shi, xiangyang xue, Thomas Huang

The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation.

Image-to-Image Translation Object Detection +1

Transfer Learning for Sequences via Learning to Collocate

no code implementations ICLR 2019 Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang

Transfer learning aims to solve the data sparsity for a target domain by applying information of the source domain.


MEAL: Multi-Model Ensemble via Adversarial Learning

1 code implementation6 Dec 2018 Zhiqiang Shen, Zhankui He, xiangyang xue

In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN.

Object Detection from Scratch with Deep Supervision

1 code implementation25 Sep 2018 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method.

General Classification Object Detection

TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

no code implementations ECCV 2018 Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, JinJun Xiong, Jiashi Feng, Thomas Huang

This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).

Multiple Instance Learning Weakly Supervised Object Detection +1

Improving Object Detection from Scratch via Gated Feature Reuse

2 code implementations4 Dec 2017 Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, Marios Savvides

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).

Object Detection

DSOD: Learning Deeply Supervised Object Detectors from Scratch

4 code implementations ICCV 2017 Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue

State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks.

General Classification Object Detection

Weakly Supervised Dense Video Captioning

no code implementations CVPR 2017 Zhiqiang Shen, Jianguo Li, Zhou Su, Minjun Li, Yurong Chen, Yu-Gang Jiang, xiangyang xue

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences.

Dense Video Captioning Language Modelling +1

Iterative Object and Part Transfer for Fine-Grained Recognition

no code implementations29 Mar 2017 Zhiqiang Shen, Yu-Gang Jiang, Dequan Wang, xiangyang xue

On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.

Multiple Granularity Descriptors for Fine-Grained Categorization

no code implementations ICCV 2015 Dequan Wang, Zhiqiang Shen, Jie Shao, Wei zhang, xiangyang xue, Zheng Zhang

Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.

Do More Dropouts in Pool5 Feature Maps for Better Object Detection

no code implementations24 Sep 2014 Zhiqiang Shen, xiangyang xue

In these fields, the outputs of all layers of CNNs are usually considered as a high dimensional feature vector extracted from an input image and the correspondence between finer level feature vectors and concepts that the input image contains is all-important.

General Classification Image Classification +1

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