Search Results for author: Shanshan Zhang

Found 32 papers, 12 papers with code

Divide and Conquer: Hybrid Pre-training for Person Search

1 code implementation13 Dec 2023 Yanling Tian, Di Chen, Yunan Liu, Jian Yang, Shanshan Zhang

To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data.

Human Detection Person Search

Human Co-Parsing Guided Alignment for Occluded Person Re-identification

1 code implementation IEEE Transactions on Image Processing 2022 Shuguang Dou, Cairong Zhao, Xinyang Jiang, Shanshan Zhang, Wei-Shi Zheng, WangMeng Zuo

Most supervised methods propose to train an extra human parsing model aside from the ReID model with cross-domain human parts annotation, suffering from expensive annotation cost and domain gap; Unsupervised methods integrate a feature clustering-based human parsing process into the ReID model, but lacking supervision signals brings less satisfactory segmentation results.

Human Parsing Person Re-Identification

Grouped Adaptive Loss Weighting for Person Search

no code implementations23 Sep 2022 Yanling Tian, Di Chen, Yunan Liu, Shanshan Zhang, Jian Yang

A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates.

Model Optimization Multi-Task Learning +2

Learning Audio-Visual embedding for Person Verification in the Wild

no code implementations9 Sep 2022 Peiwen Sun, Shanshan Zhang, Zishan Liu, Yougen Yuan, Taotao Zhang, Honggang Zhang, Pengfei Hu

It has already been observed that audio-visual embedding is more robust than uni-modality embedding for person verification.

Face Verification

DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

1 code implementation12 Jul 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.

object-detection Object Detection +1

Bi-level Alignment for Cross-Domain Crowd Counting

1 code implementation CVPR 2022 Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai, Bernt Schiele

The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data.

AutoML Crowd Counting +2

PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection

1 code implementation30 Mar 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.

object-detection Object Detection +1

PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking

1 code implementation CVPR 2022 Andreas Döring, Di Chen, Shanshan Zhang, Bernt Schiele, Jürgen Gall

Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e. g. person detection or appearance-based association of detected persons.

Human Detection Multi-Object Tracking +5

Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation

no code implementations9 Dec 2021 Gang Li, Xiang Li, Yujie Wang, Shanshan Zhang, Yichao Wu, Ding Liang

Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.

Image Classification Knowledge Distillation +3

Adaptive Group Collaborative Artificial Bee Colony Algorithm

no code implementations2 Dec 2021 Haiquan Wang, Hans-DietrichHaasis, Panpan Du, Xiaobin Xu, Menghao Su, Shengjun Wen, Wenxuan Yue, Shanshan Zhang

As an effective algorithm for solving complex optimization problems, artificial bee colony (ABC) algorithm has shown to be competitive, but the same as other population-based algorithms, it is poor at balancing the abilities of global searching in the whole solution space (named as exploration) and quick searching in local solution space which is defined as exploitation.


An improved bearing fault detection strategy based on artificial bee colony algorithm

no code implementations1 Dec 2021 Haiquan Wang, Wenxuan Yue, Shengjun Wen, Xiaobin Xu, Menghao Su, Shanshan Zhang, Panpan Du

Moreover, XGBoost is used to recognize the faults from the obtained features, and an improved artificial bee colony algorithm(ABC) where the evolution is guided by the importance indices of different search space is proposed to optimize the parameters of XGBoost.

Fault Detection feature selection

Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation

no code implementations NeurIPS 2021 Yunan Liu, Shanshan Zhang, Yang Li, Jian Yang

In this setting, we embed an additional pair of “latent-latent” to reduce the domain gap between the source and different latent domains, allowing the model to adapt well on multiple target domains simultaneously.

Domain Adaptation Meta-Learning +1

Keypoint Message Passing for Video-based Person Re-Identification

1 code implementation16 Nov 2021 Di Chen, Andreas Doering, Shanshan Zhang, Jian Yang, Juergen Gall, Bernt Schiele

Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras.

Representation Learning Video-Based Person Re-Identification

Incremental Generative Occlusion Adversarial Suppression Network for Person ReID

1 code implementation IEEE Transactions on Image Processing 2021 Cairong Zhao, Xinbi Lv, Shuguang Dou, Shanshan Zhang, Jun Wu, Liang Wang

The adversarial suppression branch, embedded with two occlusion suppression module, minimizes the generated occlusion’s response and strengthens attentive feature representation on human non-occluded body regions.

Data Augmentation Person Re-Identification

PoseTrackReID: Dataset Description

no code implementations12 Nov 2020 Andreas Doering, Di Chen, Shanshan Zhang, Bernt Schiele, Juergen Gall

For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID.

Pose Tracking Video-Based Person Re-Identification

Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA

no code implementations30 Oct 2020 Shanshan Zhang, Wen Chen, Shaoyuan Chen

With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications.

Dictionary Learning Scheduling

Person Search via A Mask-Guided Two-Stream CNN Model

no code implementations ECCV 2018 Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, Ying Tai

In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID).

Pedestrian Detection Person Re-Identification +2

Occluded Pedestrian Detection Through Guided Attention in CNNs

no code implementations CVPR 2018 Shanshan Zhang, Jian Yang, Bernt Schiele

In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection.

Pedestrian Detection

Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification

no code implementations16 May 2018 Guodong Ding, Shanshan Zhang, Salman Khan, Zhenmin Tang, Jian Zhang, Fatih Porikli

Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.

Data Augmentation Representation Learning +1

CityPersons: A Diverse Dataset for Pedestrian Detection

2 code implementations CVPR 2017 Shanshan Zhang, Rodrigo Benenson, Bernt Schiele

Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data.

Pedestrian Detection

Semi-supervised Discovery of Informative Tweets During the Emerging Disasters

no code implementations12 Oct 2016 Shanshan Zhang, Slobodan Vucetic

Given the wide range of possible disasters, using a pre-selected set of disaster-related keywords for the discovery is suboptimal.

Classification Clustering +1

How Far are We from Solving Pedestrian Detection?

no code implementations CVPR 2016 Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele

We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector.

Clustering Pedestrian Detection +1

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

1 code implementation3 Jan 2016 Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark, Asma R Bankapur, Daniel D'Andrea, Rosalba Lepore, Christopher S Funk, Indika Kahanda, Karin M Verspoor, Asa Ben-Hur, Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed ME Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T Jones, Samuel Chapman, Dukka B K. C., Ishita K Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E Foulger, Reija Hieta, Duncan Legge, Ruth C Lovering, Michele Magrane, Anna N Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L Dawson, David Lee, Jonathan G Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio Tosatto, Angela del Pozo, José M Fernández, Paolo Maietta, Alfonso Valencia, Michael L Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W Bargsten, Aalt DJ van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C Almeida-e-Silva, Ricardo ZN Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael JE Sternberg, Mark N Wass, Rachael P Huntley, Maria J Martin, Claire O'Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C Babbitt, Steven E Brenner, Michal Linial, Christine A Orengo, Burkhard Rost, Casey S Greene, Sean D Mooney, Iddo Friedberg, Predrag Radivojac

To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.

Quantitative Methods

Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines

no code implementations24 Jun 2015 Jinfu Yang, Jingyu Gao, Guanghui Wang, Shanshan Zhang

However, the DBM is limited in scene recognition due to the fact that natural scene images are usually very large.

Clustering Handwritten Digit Recognition +3

Filtered Feature Channels for Pedestrian Detection

no code implementations CVPR 2015 Shanshan Zhang, Rodrigo Benenson, Bernt Schiele

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.

Optical Flow Estimation Pedestrian Detection

Exploring Human Vision Driven Features for Pedestrian Detection

no code implementations25 Jan 2015 Shanshan Zhang, Christian Bauckhage, Dominik A. Klein, Armin B. Cremers

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture.

Pedestrian Detection

Filtered Channel Features for Pedestrian Detection

no code implementations23 Jan 2015 Shanshan Zhang, Rodrigo Benenson, Bernt Schiele

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.

Optical Flow Estimation Pedestrian Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.