b'Chunjing Xu'

research

∙ 05/02/2023

FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow

There is extensive literature on perceiving road structures by fusing va...

0 Wenchao Ding, et al. ∙

research

∙ 03/03/2023

Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving

Multi-task learning has emerged as a powerful paradigm to solve a range ...

0 Xiwen Liang, et al. ∙

research

∙ 12/14/2022

NLIP: Noise-robust Language-Image Pre-training

Large-scale cross-modal pre-training paradigms have recently shown ubiqu...

0 Runhui Huang, et al. ∙

research

∙ 11/02/2022

P^3OVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection

Inspired by the success of visual-language methods (VLMs) in zero-shot c...

10 Yanxin Long, et al. ∙

research

∙ 09/20/2022

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

Open-world object detection, as a more general and challenging goal, aim...

7 Lewei Yao, et al. ∙

research

∙ 09/19/2022

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

Aiming towards a holistic understanding of multiple downstream tasks sim...

0 Xiwen Liang, et al. ∙

research

∙ 07/18/2022

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

To bridge the gap between supervised semantic segmentation and real-worl...

0 Quande Liu, et al. ∙

research

∙ 05/26/2022

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

Self-supervised learning (SSL), especially contrastive methods, has rais...

0 Zhili Liu, et al. ∙

research

∙ 04/09/2022

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

Existing text-guided image manipulation methods aim to modify the appear...

0 Jianan Wang, et al. ∙

research

∙ 03/18/2022

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

We present Laneformer, a conceptually simple yet powerful transformer-ba...

0 Jianhua Han, et al. ∙

research

∙ 03/15/2022

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

Contemporary deep-learning object detection methods for autonomous drivi...

22 Kaican Li, et al. ∙

research

∙ 02/14/2022

Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework

This paper presents a large-scale Chinese cross-modal dataset for benchm...

0 Jiaxi Gu, et al. ∙

research

∙ 01/10/2022

GhostNets on Heterogeneous Devices via Cheap Operations

Deploying convolutional neural networks (CNNs) on mobile devices is diff...

2 Kai Han, et al. ∙

research

∙ 12/27/2021

An Empirical Study of Adder Neural Networks for Object Detection

Adder neural networks (AdderNets) have shown impressive performance on i...

10 Xinghao Chen, et al. ∙

research

∙ 11/09/2021

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

Unsupervised large-scale vision-language pre-training has shown promisin...

0 Lewei Yao, et al. ∙

research

∙ 10/22/2021

SOFT: Softmax-free Transformer with Linear Complexity

Vision transformers (ViTs) have pushed the state-of-the-art for various ...

13 Jiachen Lu, et al. ∙

research

∙ 09/20/2021

Learning Versatile Convolution Filters for Efficient Visual Recognition

This paper introduces versatile filters to construct efficient convoluti...

0 Kai Han, et al. ∙

research

∙ 09/06/2021

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

We present a flexible and high-performance framework, named Pyramid R-CN...

0 Jiageng Mao, et al. ∙

research

∙ 09/06/2021

Voxel Transformer for 3D Object Detection

We present Voxel Transformer (VoTr), a novel and effective voxel-based T...

0 Jiageng Mao, et al. ∙

research

∙ 07/31/2021

Greedy Network Enlarging

Recent studies on deep convolutional neural networks present a simple pa...

10 Chuanjian Liu, et al. ∙

research

∙ 07/13/2021

CMT: Convolutional Neural Networks Meet Vision Transformers

Vision transformers have been successfully applied to image recognition ...

0 Jianyuan Guo, et al. ∙

research

∙ 07/07/2021

S^3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks

Shift neural networks reduce computation complexity by removing expensiv...

0 Xinlin Li, et al. ∙

research

∙ 06/21/2021

SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving

Aiming at facilitating a real-world, ever-evolving and scalable autonomo...

20 Jianhua Han, et al. ∙

research

∙ 06/21/2021

One Million Scenes for Autonomous Driving: ONCE Dataset

Current perception models in autonomous driving have become notorious fo...

7 Jiageng Mao, et al. ∙

research

∙ 05/12/2021

Winograd Algorithm for AdderNet

Adder neural network (AdderNet) is a new kind of deep model that replace...

0 Wenshuo Li, et al. ∙

research

∙ 03/26/2021

Distilling Object Detectors via Decoupled Features

Knowledge distillation is a widely used paradigm for inheriting informat...

0 Jianyuan Guo, et al. ∙

research

∙ 03/15/2021

Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

Deep learning based methods, especially convolutional neural networks (C...

48 Wenbin Xie, et al. ∙

research

∙ 03/01/2021

Learning Frequency Domain Approximation for Binary Neural Networks

Binary neural networks (BNNs) represent original full-precision weights ...

0 Yixing Xu, et al. ∙

research

∙ 02/27/2021

Transformer in Transformer

Transformer is a type of self-attention-based neural networks originally...

0 Kai Han, et al. ∙

research

∙ 01/25/2021

AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence

Convolutional neural networks (CNN) have been widely used for boosting t...

35 Yunhe Wang, et al. ∙

research

∙ 01/21/2021

GhostSR: Learning Ghost Features for Efficient Image Super-Resolution

Modern single image super-resolution (SISR) system based on convolutiona...

0 Ying Nie, et al. ∙

research

∙ 12/23/2020

A Survey on Visual Transformer

Transformer is a type of deep neural network mainly based on self-attent...

0 Kai Han, et al. ∙

research

∙ 12/01/2020

Pre-Trained Image Processing Transformer

As the computing power of modern hardware is increasing strongly, pre-tr...

52 Hanting Chen, et al. ∙

research

∙ 10/21/2020

SCOP: Scientific Control for Reliable Neural Network Pruning

This paper proposes a reliable neural network pruning algorithm by setti...

0 Yehui Tang, et al. ∙

research

∙ 10/10/2020

Training Binary Neural Networks through Learning with Noisy Supervision

This paper formalizes the binarization operations over neural networks f...

7 Kai Han, et al. ∙

research

∙ 09/28/2020

Kernel Based Progressive Distillation for Adder Neural Networks

Adder Neural Networks (ANNs) which only contain additions bring us a new...

2 Yixing Xu, et al. ∙

research

∙ 09/18/2020

AdderSR: Towards Energy Efficient Image Super-Resolution

This paper studies the single image super-resolution problem using adder...

4 Dehua Song, et al. ∙

research

∙ 09/18/2020

Searching for Low-Bit Weights in Quantized Neural Networks

Quantized neural networks with low-bit weights and activations are attra...

7 Zhaohui Yang, et al. ∙

research

∙ 07/21/2020

Video Super-resolution with Temporal Group Attention

Video super-resolution, which aims at producing a high-resolution video ...

0 Takashi Isobe, et al. ∙

research

∙ 07/16/2020

Multi-Task Pruning for Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks...

5 Xinghao Chen, et al. ∙

research

∙ 07/10/2020

Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer

Video style transfer techniques inspire many exciting applications on mo...

6 Xinghao Chen, et al. ∙

research

∙ 05/29/2020

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

Neural Architecture Search (NAS) refers to automatically design the arch...

12 Zhaohui Yang, et al. ∙

research

∙ 05/14/2020

A Semi-Supervised Assessor of Neural Architectures

Neural architecture search (NAS) aims to automatically design deep neura...

2 Yehui Tang, et al. ∙

research

∙ 03/07/2020

Distilling portable Generative Adversarial Networks for Image Translation

Despite Generative Adversarial Networks (GANs) have been widely used in ...

13 Hanting Chen, et al. ∙

research

∙ 02/23/2020

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks

Deep neural networks often consist of a great number of trainable parame...

72 Yehui Tang, et al. ∙

research

∙ 02/03/2020

Widening and Squeezing: Towards Accurate and Efficient QNNs

Quantization neural networks (QNNs) are very attractive to the industry ...

0 Chuanjian Liu, et al. ∙

research

∙ 12/31/2019

AdderNet: Do We Really Need Multiplications in Deep Learning?

Compared with cheap addition operation, multiplication operation is of m...

31 Hanting Chen, et al. ∙

research

∙ 11/27/2019

GhostNet: More Features from Cheap Operations

Deploying convolutional neural networks (CNNs) on embedded devices is di...

23 Kai Han, et al. ∙

research

∙ 10/07/2019

Unsupervised Image Super-Resolution with an Indirect Supervised Path

The task of single image super-resolution (SISR) aims at reconstructing ...

0 Zhen Han, et al. ∙

research

∙ 09/30/2019

RNAS: Architecture Ranking for Powerful Networks

Neural Architecture Search (NAS) is attractive for automatically produci...

10 Yixing Xu, et al. ∙

Chunjing Xu

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