Search Results for author: Fang Wan

Found 26 papers, 18 papers with code

Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection

2 code implementations6 Feb 2024 Feng Liu, Tengteng Huang, Qianjing Zhang, Haotian Yao, Chi Zhang, Fang Wan, Qixiang Ye, Yanzhao Zhou

Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect detections.

3D Object Detection Denoising +1

ControlCap: Controllable Region-level Captioning

1 code implementation31 Jan 2024 Yuzhong Zhao, Yue Liu, Zonghao Guo, Weijia Wu, Chen Gong, Fang Wan, Qixiang Ye

The multimodal model is constrained to generate captions within a few sub-spaces containing the control words, which increases the opportunity of hitting less frequent captions, alleviating the caption degeneration issue.

Dense Captioning

Proprioceptive Learning with Soft Polyhedral Networks

no code implementations16 Aug 2023 Xiaobo Liu, Xudong Han, Wei Hong, Fang Wan, Chaoyang Song

Proprioception is the "sixth sense" that detects limb postures with motor neurons.

Generative Prompt Model for Weakly Supervised Object Localization

1 code implementation ICCV 2023 Yuzhong Zhao, Qixiang Ye, Weijia Wu, Chunhua Shen, Fang Wan

During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings.

 Ranked #1 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric, using extra training data)

Image Denoising Language Modelling +2

Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection

3 code implementations ICCV 2023 Feng Liu, Xiaosong Zhang, Zhiliang Peng, Zonghao Guo, Fang Wan, Xiangyang Ji, Qixiang Ye

Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.

Few-Shot Object Detection Object +2

Exploiting Knowledge Distillation for Few-Shot Image Generation

no code implementations29 Sep 2021 Xingzhong Hou, Boxiao Liu, Fang Wan, Haihang You

The existing pipeline is first pretraining a source model (which contains a generator and a discriminator) on a large-scale dataset and finetuning it on a target domain with limited samples.

Image Generation Knowledge Distillation +1

Strengthen Learning Tolerance for Weakly Supervised Object Localization

1 code implementation CVPR 2021 Guangyu Guo, Junwei Han, Fang Wan, Dingwen Zhang

Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision.

Object Weakly-Supervised Object Localization

Multiple instance active learning for object detection

1 code implementation CVPR 2021 Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye

Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.

Active Object Detection Multiple Instance Learning +3

Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping

no code implementations29 Jan 2021 Linhan Yang, Xudong Han, Weijie Guo, Fang Wan, Jia Pan, Chaoyang Song

This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping.

Robotics

Domain Contrast for Domain Adaptive Object Detection

no code implementations26 Jun 2020 Feng Liu, Xiaoxong Zhang, Fang Wan, Xiangyang Ji, Qixiang Ye

We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.

Contrastive Learning Object +2

DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation

2 code implementations6 May 2020 Fang Wan, Haokun Wang, Xiaobo Liu, Linhan Yang, Chaoyang Song

We present benchmarking results of the DeepClaw system for a baseline Tic-Tac-Toe task, a bin-clearing task, and a jigsaw puzzle task using three sets of standard robotic hardware.

Robotics

A Lobster-inspired Robotic Glove for Hand Rehabilitation

no code implementations1 Mar 2020 Yao-Hui Chen, Sing Le, Qiao Chu Tan, Oscar Lau, Fang Wan, Chaoyang Song

This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation.

Rigid-Soft Interactive Learning for Robust Grasping

2 code implementations29 Feb 2020 Linhan Yang, Fang Wan, Haokun Wang, Xiaobo Liu, Yujia Liu, Jia Pan, Chaoyang Song

We use soft, stuffed toys for training, instead of everyday objects, to reduce the integration complexity and computational burden and exploit such rigid-soft interaction by changing the gripper fingers to the soft ones when dealing with rigid, daily-life items such as the Yale-CMU-Berkeley (YCB) objects.

Small Data Image Classification

Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance

no code implementations29 Feb 2020 Xia Wu, Haiyuan Liu, Ziqi Liu, Mingdong Chen, Fang Wan, Chenglong Fu, Harry Asada, Zheng Wang, Chaoyang Song

Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries.

Reconfigurable Design for Omni-adaptive Grasp Learning

2 code implementations29 Feb 2020 Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu, Sheng Ge, Chaoyang Song

Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping.

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

2 code implementations29 Feb 2020 Zeyi Yang, Sheng Ge, Fang Wan, Yujia Liu, Chaoyang Song

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment.

FreeAnchor: Learning to Match Anchors for Visual Object Detection

4 code implementations NeurIPS 2019 Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye

In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner.

Object object-detection +1

Utilizing the Instability in Weakly Supervised Object Detection

no code implementations14 Jun 2019 Yan Gao, Boxiao Liu, Nan Guo, Xiaochun Ye, Fang Wan, Haihang You, Dongrui Fan

Weakly supervised object detection (WSOD) focuses on training object detector with only image-level annotations, and is challenging due to the gap between the supervision and the objective.

Multiple Instance Learning Object +2

C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

1 code implementation CVPR 2019 Fang Wan, Chang Liu, Wei Ke, Xiangyang Ji, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.

Multiple Instance Learning Object +3

Min-Entropy Latent Model for Weakly Supervised Object Detection

1 code implementation CVPR 2018 Fang Wan, Pengxu Wei, Zhenjun Han, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.

Image Classification Object +3

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

1 code implementation2 Jan 2019 Caijing Miao, Lingxi Xie, Fang Wan, Chi Su, Hongye Liu, Jianbin Jiao, Qixiang Ye

In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.

Object Localization

Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs

1 code implementation23 May 2017 Fang Wan, Chaoyang Song

In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes.

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