Search Results for author: Lingbo Liu

Found 38 papers, 23 papers with code

MoPE: Parameter-Efficient and Scalable Multimodal Fusion via Mixture of Prompt Experts

1 code implementation14 Mar 2024 Ruixiang Jiang, Lingbo Liu, Changwen Chen

Building upon this disentanglement, we introduce the mixture of prompt experts (MoPE) technique to enhance expressiveness.

Disentanglement Multimodal Deep Learning +1

Conditional Prompt Tuning for Multimodal Fusion

1 code implementation28 Nov 2023 Ruixiang Jiang, Lingbo Liu, Changwen Chen

We show that the representation of one modality can effectively guide the prompting of another modality for parameter-efficient multimodal fusion.

SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting

1 code implementation16 Nov 2023 Hefeng Wu, Yandong Chen, Lingbo Liu, Tianshui Chen, Keze Wang, Liang Lin

In the localization stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query tensor to predict the confidence, location, and size of each potential object.

Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction

no code implementations15 Nov 2023 Guangyin Jin, Lingbo Liu, Fuxian Li, Jincai Huang

In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism.

Graph Learning Traffic Prediction

STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning

1 code implementation ICCV 2023 Tao Han, Lei Bai, Lingbo Liu, Wanli Ouyang

Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms.

feature selection Object Counting

DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback

1 code implementation13 Jun 2023 Junfan Lin, Yuying Zhu, Lingbo Liu, Yang Liu, Guanbin Li, Liang Lin

1) The travel time of a vehicle is delayed feedback on the effectiveness of TSC policy at each traffic intersection since it is obtained after the vehicle has left the road network.

Reinforcement Learning (RL)

Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer

no code implementations30 May 2023 Yang Zhang, Lingbo Liu, Xinyu Xiong, Guanbin Li, Guoli Wang, Liang Lin

In this work, we propose a novel end-to-end wind power forecasting model named Hierarchical Spatial-Temporal Transformer Network (HSTTN) to address the long-term WPF problems.


CLIP-Count: Towards Text-Guided Zero-Shot Object Counting

1 code implementation12 May 2023 Ruixiang Jiang, Lingbo Liu, Changwen Chen

Specifically, we propose CLIP-Count, the first end-to-end pipeline that estimates density maps for open-vocabulary objects with text guidance in a zero-shot manner.

Cross-Part Crowd Counting Cross-Part Evaluation +6

Visual Tuning

no code implementations10 May 2023 Bruce X. B. Yu, Jianlong Chang, Haixin Wang, Lingbo Liu, Shijie Wang, Zhiyu Wang, Junfan Lin, Lingxi Xie, Haojie Li, Zhouchen Lin, Qi Tian, Chang Wen Chen

With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.

Urban Regional Function Guided Traffic Flow Prediction

no code implementations17 Mar 2023 Kuo Wang, Lingbo Liu, Yang Liu, Guanbin Li, Fan Zhou, Liang Lin

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest.

Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training

1 code implementation CVPR 2023 Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, Chang Wen Chen

During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion.

Language Modelling Zero-Shot Learning

Towards a Unified View on Visual Parameter-Efficient Transfer Learning

1 code implementation3 Oct 2022 Bruce X. B. Yu, Jianlong Chang, Lingbo Liu, Qi Tian, Chang Wen Chen

Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.

Action Recognition Image Classification +2

Prompt-Matched Semantic Segmentation

no code implementations22 Aug 2022 Lingbo Liu, Jianlong Chang, Bruce X. B. Yu, Liang Lin, Qi Tian, Chang-Wen Chen

Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks.

Representation Learning Segmentation +2

Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation

1 code implementation21 Jun 2022 Shuaicheng Li, Feng Zhang, Rui-Wei Zhao, Rui Feng, Kunlin Yang, Lingbo Liu, Jun Hou

Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.

Action Detection Temporal Action Proposal Generation

Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels

1 code implementation23 May 2022 Tianshui Chen, Tao Pu, Lingbo Liu, Yukai Shi, Zhijing Yang, Liang Lin

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.

Multi-label Image Recognition with Partial Labels

TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning

2 code implementations7 Dec 2021 Yang Liu, Keze Wang, Lingbo Liu, Haoyuan Lan, Liang Lin

To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and proposes a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy.

Action Recognition Contrastive Learning +5

Road Network Guided Fine-Grained Urban Traffic Flow Inference

1 code implementation29 Sep 2021 Lingbo Liu, Mengmeng Liu, Guanbin Li, Ziyi Wu, Junfan Lin, Liang Lin

Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture.

GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer

1 code implementation ICCV 2021 Shuaicheng Li, Qianggang Cao, Lingbo Liu, Kunlin Yang, Shinan Liu, Jun Hou, Shuai Yi

It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.

Group Activity Recognition

Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark

1 code implementation19 Jul 2021 Haopeng Li, Lingbo Liu, Kunlin Yang, Shinan Liu, Junyu Gao, Bin Zhao, Rui Zhang, Jun Hou

Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

1 code implementation2 Jul 2021 Lingbo Liu, Yuying Zhu, Guanbin Li, Ziyi Wu, Lei Bai, Liang Lin

In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e. g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership.

Time Series Analysis

GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning

1 code implementation Findings (ACL) 2021 Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xing, Liang Lin

Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4, 998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.

Math Mathematical Reasoning +1

Efficient Crowd Counting via Structured Knowledge Transfer

2 code implementations23 Mar 2020 Lingbo Liu, Jiaqi Chen, Hefeng Wu, Tianshui Chen, Guanbin Li, Liang Lin

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications.

Crowd Counting Transfer Learning

Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction

2 code implementations14 Jan 2020 Lingbo Liu, Jingwen Chen, Hefeng Wu, Jiajie Zhen, Guanbin Li, Liang Lin

To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.

Representation Learning

Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction

2 code implementations2 Sep 2019 Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, Zhaocheng He, Bowen Du, Liang Lin

Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.

Representation Learning Traffic Prediction

Crowd Counting with Deep Structured Scale Integration Network

no code implementations ICCV 2019 Lingbo Liu, Zhilin Qiu, Guanbin Li, Shufan Liu, Wanli Ouyang, Liang Lin

Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people.

Crowd Counting Representation Learning

Investigation of wind pressures on tall building under interference effects using machine learning techniques

no code implementations20 Aug 2019 Gang Hu, Lingbo Liu, DaCheng Tao, Jie Song, K. C. S. Kwok

This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.

BIG-bench Machine Learning

Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction

no code implementations15 May 2019 Lingbo Liu, Zhilin Qiu, Guanbin Li, Qing Wang, Wanli Ouyang, Liang Lin

Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.

Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning

no code implementations10 Dec 2018 Lingbo Liu, Guanbin Li, Yuan Xie, Yizhou Yu, Qing Wang, Liang Lin

In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.

Face Alignment Face Detection +2

Attentive Crowd Flow Machines

no code implementations1 Sep 2018 Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin

Traffic flow prediction is crucial for urban traffic management and public safety.


Crowd Counting using Deep Recurrent Spatial-Aware Network

no code implementations2 Jul 2018 Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin

Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera's perspective that causes huge appearance variations in people's scales and rotations.

Crowd Counting Management

DISC: Deep Image Saliency Computing via Progressive Representation Learning

no code implementations13 Nov 2015 Tianshui Chen, Liang Lin, Lingbo Liu, Xiaonan Luo, Xuelong. Li

Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details.

object-detection Representation Learning +2

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