b'Yaowei Wang'

research

∙ 03/25/2023

Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation

This paper aims to solve the video object segmentation (VOS) task in a s...

0 Zikun Zhou, et al. ∙

research

∙ 03/11/2023

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation

Natural Language Generation (NLG) accepts input data in the form of imag...

0 Bang Yang, et al. ∙

research

∙ 03/01/2023

Backdoor for Debias: Mitigating Model Bias with Backdoor Attack-based Artificial Bias

With the swift advancement of deep learning, state-of-the-art algorithms...

0 Shangxi Wu, et al. ∙

research

∙ 02/23/2023

Unsupervised Domain Adaptation via Distilled Discriminative Clustering

Unsupervised domain adaptation addresses the problem of classifying data...

1 Hui Tang, et al. ∙

research

∙ 02/20/2023

Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey

With the urgent demand for generalized deep models, many pre-trained big...

1 Xiao Wang, et al. ∙

research

∙ 02/03/2023

DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition

As a de facto solution, the vanilla Vision Transformers (ViTs) are encou...

0 Jiayu Jiao, et al. ∙

research

∙ 12/31/2022

Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

There is a growing interest in developing unlearnable examples (UEs) aga...

0 Jiaming Zhang, et al. ∙

research

∙ 12/19/2022

Million-scale Object Detection with Large Vision Model

Over the past few years, developing a broad, universal, and general-purp...

0 Feng Lin, et al. ∙

research

∙ 11/29/2022

Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference

This paper focuses on the prevalent performance imbalance in the stages ...

0 Yabin Wang, et al. ∙

research

∙ 11/28/2022

SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification

Although significant progress has been made in few-shot learning, most o...

0 Fang Peng, et al. ∙

research

∙ 11/23/2022

Integrally Pre-Trained Transformer Pyramid Networks

In this paper, we present an integral pre-training framework based on ma...

0 Yunjie Tian, et al. ∙

research

∙ 11/20/2022

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

Combining the Color and Event cameras (also called Dynamic Vision Sensor...

0 Chuanming Tang, et al. ∙

research

∙ 11/17/2022

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

The main streams of human activity recognition (HAR) algorithms are deve...

0 Xiao Wang, et al. ∙

research

∙ 09/29/2022

Spikformer: When Spiking Neural Network Meets Transformer

We consider two biologically plausible structures, the Spiking Neural Ne...

0 Zhaokun Zhou, et al. ∙

research

∙ 09/07/2022

Semi-supervised Crowd Counting via Density Agency

In this paper, we propose a new agency-guided semi-supervised counting a...

0 Hui Lin, et al. ∙

research

∙ 07/30/2022

DAS: Densely-Anchored Sampling for Deep Metric Learning

Deep Metric Learning (DML) serves to learn an embedding function to proj...

0 Lizhao Liu, et al. ∙

research

∙ 06/17/2022

Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval

Our goal in this research is to study a more realistic environment in wh...

0 Xiao Dong, et al. ∙

research

∙ 05/26/2022

Prompt-based Learning for Unpaired Image Captioning

Unpaired Image Captioning (UIC) has been developed to learn image descri...

0 Peipei Zhu, et al. ∙

research

∙ 04/17/2022

Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification

In this paper, we propose a Global-Supervised Contrastive loss and a vie...

0 Zhijun Hu, et al. ∙

research

∙ 03/30/2022

Fine-Grained Object Classification via Self-Supervised Pose Alignment

Semantic patterns of fine-grained objects are determined by subtle appea...

0 Xuhui Yang, et al. ∙

research

∙ 03/21/2022

Boost Test-Time Performance with Closed-Loop Inference

Conventional deep models predict a test sample with a single forward pro...

7 Shuaicheng Niu, et al. ∙

research

∙ 03/16/2022

Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

The exponentially large discrete search space in mixed-precision quantiz...

0 Chen Tang, et al. ∙

research

∙ 03/11/2022

Peng Cheng Object Detection Benchmark for Smart City

Object detection is an algorithm that recognizes and locates the objects...

0 Yaowei Wang, et al. ∙

research

∙ 03/07/2022

Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition

The goal of unpaired image captioning (UIC) is to describe images withou...

0 Peipei Zhu, et al. ∙

research

∙ 03/05/2022

Boosting Crowd Counting via Multifaceted Attention

This paper focuses on the challenging crowd counting task. As large-scal...

0 Hui Lin, et al. ∙

research

∙ 01/21/2022

Conceptor Learning for Class Activation Mapping

Class Activation Mapping (CAM) has been widely adopted to generate salie...

7 Guangwu Qian, et al. ∙

research

∙ 12/17/2021

Towards End-to-End Image Compression and Analysis with Transformers

We propose an end-to-end image compression and analysis model with Trans...

17 Yuanchao Bai, et al. ∙

research

∙ 12/16/2021

Learning to Share in Multi-Agent Reinforcement Learning

In this paper, we study the problem of networked multi-agent reinforceme...

0 Yuxuan Yi, et al. ∙

research

∙ 12/16/2021

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) aims to improve the generalizati...

0 Rui Liu, et al. ∙

research

∙ 07/04/2021

Direct Measure Matching for Crowd Counting

Traditional crowd counting approaches usually use Gaussian assumption to...

0 Hui Lin, et al. ∙

research

∙ 05/09/2021

Conformer: Local Features Coupling Global Representations for Visual Recognition

Within Convolutional Neural Network (CNN), the convolution operations ar...

12 Zhiliang Peng, et al. ∙

research

∙ 04/02/2021

AAformer: Auto-Aligned Transformer for Person Re-Identification

Transformer is showing its superiority over convolutional architectures ...

0 Kuan Zhu, et al. ∙

research

∙ 03/31/2021

Learning Scalable ℓ_∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression

We propose a novel joint lossy image and residual compression framework ...

0 Yuanchao Bai, et al. ∙

research

∙ 03/08/2021

Contrastive Neural Architecture Search with Neural Architecture Comparators

One of the key steps in Neural Architecture Search (NAS) is to estimate ...

0 Yaofo Chen, et al. ∙

research

∙ 05/12/2020

Compositional Few-Shot Recognition with Primitive Discovery and Enhancing

Few-shot learning (FSL) aims at recognizing novel classes given only few...

11 Yixiong Zou, et al. ∙

research

∙ 10/05/2019

Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning

Few-shot learning, which aims at extracting new concepts rapidly from ex...

0 Limeng Qiao, et al. ∙

research

∙ 05/06/2019

P-ODN: Prototype based Open Deep Network for Open Set Recognition

Most of the existing recognition algorithms are proposed for closed set ...

6 Yu Shu, et al. ∙

research

∙ 01/23/2019

ODN: Opening the Deep Network for Open-set Action Recognition

In recent years, the performance of action recognition has been signific...

0 Yu Shu, et al. ∙

research

∙ 11/16/2016

Deep Transfer Learning for Person Re-identification

Person re-identification (Re-ID) poses a unique challenge to deep learni...

0 Mengyue Geng, et al. ∙

research

∙ 11/16/2016

Learning long-term dependencies for action recognition with a biologically-inspired deep network

Despite a lot of research efforts devoted in recent years, how to effici...

0 Yemin Shi, et al. ∙

research

∙ 11/16/2016

Joint Network based Attention for Action Recognition

By extracting spatial and temporal characteristics in one network, the t...

0 Yemin Shi, et al. ∙

research

∙ 09/10/2016

Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN

Learning the spatial-temporal representation of motion information is cr...

0 Yemin Shi, et al. ∙

Yaowei Wang

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