Search Results for author: Hang Xu

Found 75 papers, 36 papers with code

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

no code implementations20 Sep 2022 Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei zhang, Zhenguo Li, Chunjing Xu, Hang Xu

We further design a concept dictionary~(with descriptions) from various online sources and detection datasets to provide prior knowledge for each concept.

object-detection Open World Object Detection

Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

no code implementations19 Sep 2022 Xiwen Liang, Yangxin Wu, Jianhua Han, Hang Xu, Chunjing Xu, Xiaodan Liang

Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability.

Autonomous Driving Multi-Task Learning +4

Exploring Visual Interpretability for Contrastive Language-Image Pre-training

1 code implementation15 Sep 2022 Yi Li, Hualiang Wang, Yiqun Duan, Hang Xu, Xiaomeng Li

However, to the best of our knowledge, the visual interpretability of CLIP has not been studied yet.

text similarity

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

1 code implementation14 Sep 2022 Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.

Depth Estimation

RCLane: Relay Chain Prediction for Lane Detection

no code implementations19 Jul 2022 Shenghua Xu, Xinyue Cai, Bin Zhao, Li Zhang, Hang Xu, Yanwei Fu, xiangyang xue

This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.

Lane Detection

Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding

no code implementations18 Jul 2022 Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, Xiaodan Liang

To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes.

Online Clustering Semantic Segmentation +1

Softmax-free Linear Transformers

1 code implementation5 Jul 2022 Jiachen Lu, Li Zhang, Junge Zhang, Xiatian Zhu, Hang Xu, Jianfeng Feng

Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.

CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving

1 code implementation8 Jun 2022 Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Zhenguo Li, Ping Luo

In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.

Autonomous Driving Contrastive Learning +1

Learning Ego 3D Representation as Ray Tracing

1 code implementation8 Jun 2022 Jiachen Lu, Zheyuan Zhou, Xiatian Zhu, Hang Xu, Li Zhang

A self-driving perception model aims to extract 3D semantic representations from multiple cameras collectively into the bird's-eye-view (BEV) coordinate frame of the ego car in order to ground downstream planner.

3D Object Detection Depth Estimation +3

Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing

no code implementations26 May 2022 Zhili Liu, Jianhua Han, Lanqing Hong, Hang Xu, Kai Chen, Chunjing Xu, Zhenguo Li

On the other hand, for existing SSL methods, it is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks.

Self-Supervised Learning

ZeroGen$^+$: Self-Guided High-Quality Data Generation in Efficient Zero-Shot Learning

no code implementations25 May 2022 Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong

To address this problem, we propose a noise-robust bi-level re-weighting framework which is able to learn the per-sample weights measuring the data quality without requiring any gold data.

text-classification Text Classification +1

MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

1 code implementation12 May 2022 Xuesong Chen, Shaoshuai Shi, Benjin Zhu, Ka Chun Cheung, Hang Xu, Hongsheng Li

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots.

Autonomous Driving object-detection +1

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

1 code implementation CVPR 2022 BinBin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, Xiaodan Liang

To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes.

Continual Learning object-detection +1

ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

no code implementations CVPR 2022 Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Chunjing Xu, Yanwei Fu

Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application.

Image Generation Image Manipulation

Point2Seq: Detecting 3D Objects as Sequences

1 code implementation CVPR 2022 Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei zhang, Xiaogang Wang, Xinchao Wang

We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.

3D Object Detection object-detection

Laneformer: Object-aware Row-Column Transformers for Lane Detection

no code implementations18 Mar 2022 Jianhua Han, Xiajun Deng, Xinyue Cai, Zhen Yang, Hang Xu, Chunjing Xu, Xiaodan Liang

We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving.

Autonomous Driving Lane Detection

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

no code implementations15 Mar 2022 Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei zhang, Chunjing Xu, Dit-yan Yeung, Xiaodan Liang, Zhenguo Li, Hang Xu

One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.

Autonomous Driving object-detection +1

Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration

1 code implementation ACL 2022 Xiwen Liang, Fengda Zhu, Lingling Li, Hang Xu, Xiaodan Liang

To improve the ability of fast cross-domain adaptation, we propose Prompt-based Environmental Self-exploration (ProbES), which can self-explore the environments by sampling trajectories and automatically generates structured instructions via a large-scale cross-modal pretrained model (CLIP).

Domain Adaptation Vision-Language Navigation

ZeroGen: Efficient Zero-shot Learning via Dataset Generation

1 code implementation16 Feb 2022 Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).

Knowledge Distillation Natural Language Inference +5

Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

no code implementations14 Feb 2022 Jiaxi Gu, Xiaojun Meng, Guansong Lu, Lu Hou, Minzhe Niu, Xiaodan Liang, Lewei Yao, Runhui Huang, Wei zhang, Xin Jiang, Chunjing Xu, Hang Xu

Furthermore, we release a group of models pre-trained with various image encoders (ViT-B/ViT-L/SwinT) and also apply advanced pre-training techniques into VLP such as locked-image text tuning, token-wise similarity in contrastive learning, and reduced-token interaction.

Contrastive Learning Image Classification +1

FILIP: Fine-grained Interactive Language-Image Pre-Training

no code implementations ICLR 2022 Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, Chunjing Xu

In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective.

Image Classification Zero-Shot Image Classification

SOFT: Softmax-free Transformer with Linear Complexity

1 code implementation NeurIPS 2021 Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing Xu, Tao Xiang, Li Zhang

Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.

EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation

1 code implementation Findings (EMNLP) 2021 Chenhe Dong, Guangrun Wang, Hang Xu, Jiefeng Peng, Xiaozhe Ren, Xiaodan Liang

In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA) since the computational cost of FFN is 2$\sim$3 times larger than MHA.

Data Augmentation Knowledge Distillation

Voxel Transformer for 3D Object Detection

1 code implementation ICCV 2021 Jiageng Mao, Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, Chunjing Xu

We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds.

Ranked #2 on 3D Object Detection on waymo vehicle (L1 mAP metric)

3D Object Detection object-detection +1

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

1 code implementation ICCV 2021 Jiageng Mao, Minzhe Niu, Haoyue Bai, Xiaodan Liang, Hang Xu, Chunjing Xu

To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.

3D Object Detection object-detection

Adversarial Robustness for Unsupervised Domain Adaptation

no code implementations ICCV 2021 Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo, Sung-Ho Bae, Zhenguo Li

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.

Adversarial Robustness Unsupervised Domain Adaptation

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

1 code implementation ICCV 2021 Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung

By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.

Autonomous Driving Image Clustering +2

Unbiased IoU for Spherical Image Object Detection

no code implementations18 Aug 2021 Qiang Zhao, Bin Chen, Hang Xu, Yike Ma, XiaoDong Li, Bailan Feng, Chenggang Yan, Feng Dai

In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.

object-detection Object Detection

G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation

no code implementations ICCV 2021 Lewei Yao, Renjie Pi, Hang Xu, Wei zhang, Zhenguo Li, Tong Zhang

In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs.

Knowledge Distillation object-detection +1

NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models

no code implementations ICCV 2021 Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, Zhenguo Li

Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.

Neural Architecture Search

Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining

1 code implementation ICCV 2021 Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, Xiaodan Liang

In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.

AutoBERT-Zero: Evolving BERT Backbone from Scratch

no code implementations15 Jul 2021 Jiahui Gao, Hang Xu, Han Shi, Xiaozhe Ren, Philip L. H. Yu, Xiaodan Liang, Xin Jiang, Zhenguo Li

Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks.

Inductive Bias Language Modelling +1

SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving

no code implementations21 Jun 2021 Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei zhang, Zhenguo Li, Xiaodan Liang, Chunjing Xu

Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.

Autonomous Driving Instance Segmentation +5

One Million Scenes for Autonomous Driving: ONCE Dataset

1 code implementation21 Jun 2021 Jiageng Mao, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Jingheng Chen, Xiaodan Liang, Yamin Li, Chaoqiang Ye, Wei zhang, Zhenguo Li, Jie Yu, Hang Xu, Chunjing Xu

To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.

3D Object Detection Autonomous Driving +1

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

no code implementations CVPR 2021 Lewei Yao, Renjie Pi, Hang Xu, Wei zhang, Zhenguo Li, Tong Zhang

For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches.

Knowledge Distillation Neural Architecture Search +2

TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search

1 code implementation CVPR 2021 Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li

While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.

Neural Architecture Search Transfer Learning

DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning

1 code implementation NeurIPS 2021 Hang Xu, Kelly Kostopoulou, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis

DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead.

BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening

no code implementations13 May 2021 Wenqi Shao, Hang Yu, Zhaoyang Zhang, Hang Xu, Zhenguo Li, Ping Luo

To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated.

Effective Sparsification of Neural Networks with Global Sparsity Constraint

1 code implementation CVPR 2021 Xiao Zhou, Weizhong Zhang, Hang Xu, Tong Zhang

Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments.

SparseBERT: Rethinking the Importance Analysis in Self-attention

1 code implementation25 Feb 2021 Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James T. Kwok

A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions.

L2E: Learning to Exploit Your Opponent

no code implementations18 Feb 2021 Zhe Wu, Kai Li, Enmin Zhao, Hang Xu, Meng Zhang, Haobo Fu, Bo An, Junliang Xing

In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.

Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search

1 code implementation ICLR 2021 Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, Zhenguo Li

For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.

Model Optimization object-detection +1

DetCo: Unsupervised Contrastive Learning for Object Detection

2 code implementations ICCV 2021 Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, Ping Luo

Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.

Contrastive Learning Image Classification +3

DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

1 code implementation NeurIPS 2021 Kelly Kostopoulou, Hang Xu, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis

This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.

Segmenting Transparent Object in the Wild with Transformer

2 code implementations21 Jan 2021 Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.

Semantic Segmentation Transparent objects

C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing

no code implementations ICCV 2021 Yanning Zhou, Hang Xu, Wei zhang, Bin Gao, Pheng-Ann Heng

The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.

Contrastive Learning Data Augmentation +1

NASOA: Towards Faster Task-oriented Online Fine-tuning

no code implementations1 Jan 2021 Hang Xu, Ning Kang, Gengwei Zhang, Xiaodan Liang, Zhenguo Li

The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.

Neural Architecture Search

TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search

2 code implementations1 Jan 2021 Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li

While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.

Neural Architecture Search Transfer Learning

Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection

no code implementations ICCV 2021 Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei zhang, Zhenguo Li, Luc van Gool

Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.

3D Object Detection object-detection +2

OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research

no code implementations11 Dec 2020 Kai Li, Hang Xu, Enmin Zhao, Zhe Wu, Junliang Xing

Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

2 code implementations NeurIPS 2020 Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin

In this work, we propose an efficient, cooperative and highly automated framework to simultaneously search for all main components including backbone, segmentation branches, and feature fusion module in a unified panoptic segmentation pipeline based on the prevailing one-shot Network Architecture Search (NAS) paradigm.

Instance Segmentation Panoptic Segmentation +1

Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

1 code implementation30 Sep 2020 Okan Köpüklü, Jiapeng Zheng, Hang Xu, Gerhard Rigoll

For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set.

Anomaly Detection Contrastive Learning +1

CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending

1 code implementation ECCV 2020 Hang Xu, Shaoju Wang, Xinyue Cai, Wei zhang, Xiaodan Liang, Zhenguo Li

In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.

Autonomous Driving Lane Detection

AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling

no code implementations ECCV 2020 Wenshuo Ma, Tingzhong Tian, Hang Xu, Yimin Huang, Zhenguo Li

By carefully analyzing the existing bounding box patterns on the feature hierarchy, we design a flexible and tight hyper-parameter space for anchor configurations.

object-detection Object Detection

JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image

1 code implementation ECCV 2020 Linpu Fang, Xingyan Liu, Li Liu, Hang Xu, Wenxiong Kang

The key ideas are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training.

3D Hand Pose Estimation Representation Learning

ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection

no code implementations3 Mar 2020 Chenhan Jiang, Shaoju Wang, Hang Xu, Xiaodan Liang, Nong Xiao

Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain?

Lesion Detection medical image detection

EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement

no code implementations18 Feb 2020 Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li

In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.

object-detection Object Detection

Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

no code implementations18 Feb 2020 Hang Xu, Linpu Fang, Xiaodan Liang, Wenxiong Kang, Zhenguo Li

Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally.

object-detection Object Detection +1

SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection

no code implementations22 Nov 2019 Lewei Yao, Hang Xu, Wei zhang, Xiaodan Liang, Zhenguo Li

In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.

Neural Architecture Search object-detection +1

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

1 code implementation NeurIPS 2020 Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang

In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously.

Neural Architecture Search

Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification

no code implementations ICCV 2019 Hang Xu, Lewei Yao, Wei Zhang, Xiaodan Liang, Zhenguo Li

Abstract Neural architecture search (NAS) has shown great potential in automating the manual process of designing a good CNN architecture for image classification.

Classification General Classification +4

Multi-objective Neural Architecture Search via Predictive Network Performance Optimization

no code implementations25 Sep 2019 Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang

Inspired by the nature of the graph structure of a neural network, we propose BOGCN-NAS, a NAS algorithm using Bayesian Optimization with Graph Convolutional Network (GCN) predictor.

Neural Architecture Search

MANAS: Multi-Agent Neural Architecture Search

no code implementations3 Sep 2019 Fabio Maria Carlucci, Pedro M. Esperança, Marco Singh, Victor Gabillon, Antoine Yang, Hang Xu, Zewei Chen, Jun Wang

The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.

Neural Architecture Search

Reasoning-RCNN: Unifying Adaptive Global Reasoning Into Large-Scale Object Detection

no code implementations CVPR 2019 Hang Xu, Chenhan Jiang, Xiaodan Liang, Liang Lin, Zhenguo Li

In this paper, we address the large-scale object detection problem with thousands of categories, which poses severe challenges due to long-tail data distributions, heavy occlusions, and class ambiguities.

object-detection Object Detection

Hybrid Knowledge Routed Modules for Large-scale Object Detection

1 code implementation NeurIPS 2018 Chenhan Jiang, Hang Xu, Xiangdan Liang, Liang Lin

The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.

object-detection Object Detection

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