Search Results for author: Gaofeng Meng

Found 43 papers, 15 papers with code

OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction

no code implementations30 Oct 2024 Hongbo Zhao, Lue Fan, Yuntao Chen, Haochen Wang, Yuran Yang, Xiaojuan Jin, Yixin Zhang, Gaofeng Meng, Zhaoxiang Zhang

By publishing and maintaining the dataset, we provide a high-quality benchmark for satellite-based map construction and downstream tasks like autonomous driving.

Autonomous Driving Diversity

A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem

no code implementations15 Oct 2024 Kun Ding, Ying Wang, Gaofeng Meng, Shiming Xiang

As such, this survey paper first proposes a unified computational framework from the perspective of Representer Theorem and then derives many of the existing methods by specializing this framework.

Language Modelling Survey

Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation

no code implementations11 Oct 2024 Kun Ding, Qiang Yu, Haojian Zhang, Gaofeng Meng, Shiming Xiang

Weight Calibration introduces a precision matrix into the weight function to adequately model the relation between training samples, transforming the existing cache model to a Gaussian Process (GP) regressor, which could be more accurate than N-W estimator.

Language Modelling text similarity

AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization

1 code implementation11 Jul 2024 Shixiong Xu, Chenghao Zhang, Lubin Fan, Gaofeng Meng, Shiming Xiang, Jieping Ye

In this study, we introduce a new problem raised by social media and photojournalism, named Image Address Localization (IAL), which aims to predict the readable textual address where an image was taken.

Contrastive Learning Transfer Learning

A Multimodal Transformer for Live Streaming Highlight Prediction

no code implementations15 Jun 2024 Jiaxin Deng, Shiyao Wang, Dong Shen, Liqin Zhao, Fan Yang, Guorui Zhou, Gaofeng Meng

Therefore, we propose a novel Border-aware Pairwise Loss to learn from a large-scale dataset and utilize user implicit feedback as a weak supervision signal.

Highlight Detection

MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion

no code implementations15 Jun 2024 Jiaxin Deng, Shiyao Wang, Yuchen Wang, Jiansong Qi, Liqin Zhao, Guorui Zhou, Gaofeng Meng

To alleviate the sparsity issue of gifting behaviors, we present a novel Graph-guided Interest Expansion (GIE) approach that learns both user and streamer representations on large-scale gifting graphs with multi-modal attributes.

Xwin-LM: Strong and Scalable Alignment Practice for LLMs

1 code implementation30 May 2024 Bolin Ni, Jingcheng Hu, Yixuan Wei, Houwen Peng, Zheng Zhang, Gaofeng Meng, Han Hu

In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs).

Defying Imbalanced Forgetting in Class Incremental Learning

no code implementations22 Mar 2024 Shixiong Xu, Gaofeng Meng, Xing Nie, Bolin Ni, Bin Fan, Shiming Xiang

This intriguing phenomenon, discovered in replay-based Class Incremental Learning (CIL), highlights the imbalanced forgetting of learned classes, as their accuracy is similar before the occurrence of catastrophic forgetting.

class-incremental learning Class Incremental Learning +2

ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer

no code implementations26 Jun 2023 Jiaxin Deng, Dong Shen, Shiyao Wang, Xiangyu Wu, Fan Yang, Guorui Zhou, Gaofeng Meng

However, most previous works treat the live as a whole item and explore the Click-through-Rate (CTR) prediction framework on item-level, neglecting that the dynamic changes that occur even within the same live room.

Click-Through Rate Prediction Dynamic Time Warping +1

Free Lunch for Generating Effective Outlier Supervision

no code implementations17 Jan 2023 Sen Pei, Jiaxi Sun, Richard Yi Da Xu, Bin Fan, Shiming Xiang, Gaofeng Meng

Generally, existing approaches in dealing with out-of-distribution (OOD) detection mainly focus on the statistical difference between the features of OOD and in-distribution (ID) data extracted by the classifiers.

Out of Distribution (OOD) Detection

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

no code implementations19 Nov 2022 Jiaxin Deng, Dong Shen, Haojie Pan, Xiangyu Wu, Ximan Liu, Gaofeng Meng, Fan Yang, Size Li, Ruiji Fu, Zhongyuan Wang

Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG.

Common Sense Reasoning Knowledge Graph Embedding +4

Domain Decorrelation with Potential Energy Ranking

1 code implementation25 Jul 2022 Sen Pei, Jiaxi Sun, Richard Yi Da Xu, Shiming Xiang, Gaofeng Meng

PoER helps the neural networks to capture label-related features which contain the domain information first in shallow layers and then distills the label-discriminative representations out progressively, enforcing the neural networks to be aware of the characteristic of objects and background which is vital to the generation of domain-invariant features.

Domain Generalization

Gradient Concealment: Free Lunch for Defending Adversarial Attacks

no code implementations21 May 2022 Sen Pei, Jiaxi Sun, Xiaopeng Zhang, Gaofeng Meng

Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks.

Robust classification

Continual Stereo Matching of Continuous Driving Scenes With Growing Architecture

1 code implementation CVPR 2022 Chenghao Zhang, Kun Tian, Bin Fan, Gaofeng Meng, Zhaoxiang Zhang, Chunhong Pan

The deep stereo models have achieved state-of-the-art performance on driving scenes, but they suffer from severe performance degradation when tested on unseen scenes.

Continual Learning RAG +1

Differentiable Convolution Search for Point Cloud Processing

no code implementations ICCV 2021 Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo, Gaofeng Meng, Qi Tian, Weiming Hu, Chunhong Pan

It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.

Alleviating Mode Collapse in GAN via Diversity Penalty Module

no code implementations5 Aug 2021 Sen Pei, Richard Yi Da Xu, Shiming Xiang, Gaofeng Meng

We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution.

Data Augmentation Diversity

Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement

no code implementations11 Mar 2021 Chi Zhang, Zihang Lin, Liheng Xu, Zongliang Li, Wei Tang, Yuehu Liu, Gaofeng Meng, Le Wang, Li Li

The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i. e. style feature, and the feature representing the invariant semantic content, i. e. content feature.

Disentanglement Image Generation +1

Spatio-Temporal Graph Structure Learning for Traffic Forecasting

no code implementations AAAI 2020 Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting.

Graph structure learning Time Series +2

FontGAN: A Unified Generative Framework for Chinese Character Stylization and De-stylization

no code implementations28 Oct 2019 Xiyan Liu, Gaofeng Meng, Shiming Xiang, Chunhong Pan

In our model, we decouple character images into style representation and content representation, which facilitates more precise control of these two types of variables, thereby improving the quality of the generated results.

Joint haze image synthesis and dehazing with mmd-vae losses

no code implementations15 May 2019 Zongliang Li, Chi Zhang, Gaofeng Meng, Yuehu Liu

Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs.

Autonomous Driving Image Dehazing +2

Differentiable Architecture Search with Ensemble Gumbel-Softmax

no code implementations6 May 2019 Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan

For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency.

Neural Architecture Search

Deep Discriminative Clustering Analysis

no code implementations5 May 2019 Jianlong Chang, Yiwen Guo, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Traditional clustering methods often perform clustering with low-level indiscriminative representations and ignore relationships between patterns, resulting in slight achievements in the era of deep learning.

Clustering

DetNAS: Backbone Search for Object Detection

2 code implementations NeurIPS 2019 Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun

In this work, we present DetNAS to use Neural Architecture Search (NAS) for the design of better backbones for object detection.

General Classification Image Classification +4

Structure-Aware Convolutional Neural Networks

1 code implementation NeurIPS 2018 Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.

Action Recognition Activity Detection +5

Joint Neural Architecture Search and Quantization

no code implementations23 Nov 2018 Yukang Chen, Gaofeng Meng, Qian Zhang, Xinbang Zhang, Liangchen Song, Shiming Xiang, Chunhong Pan

Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices.

Model Compression Neural Architecture Search +1

Exploiting Vector Fields for Geometric Rectification of Distorted Document Images

no code implementations ECCV 2018 Gaofeng MENG, Yuanqi SU, Ying Wu, Shiming Xiang, Chunhong Pan

This paper proposes a segment-free method for geometric rectification of a distorted document image captured by a hand-held camera.

Reinforced Evolutionary Neural Architecture Search

1 code implementation1 Aug 2018 Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, Xinggang Wang

To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE- NAS), which is an evolutionary method with the reinforced mutation for NAS.

Neural Architecture Search Semantic Segmentation

AMVH: Asymmetric Multi-Valued Hashing

no code implementations CVPR 2017 Cheng Da, Shibiao Xu, Kun Ding, Gaofeng Meng, Shiming Xiang, Chunhong Pan

(2) A multi-integer-embedding is employed for compressing the whole database, which is modeled by binary sparse representation with fixed sparsity.

Extraction of Virtual Baselines From Distorted Document Images Using Curvilinear Projection

no code implementations ICCV 2015 Gaofeng Meng, Zuming Huang, Yonghong Song, Shiming Xiang, Chunhong Pan

In this paper, we propose an efficient method for accurate extraction of these virtual visual cues from a curved document image.

Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity

no code implementations2 Sep 2014 Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Chunhong Pan

Based on this observation, we exploit a learning-based sparsity method to simultaneously learn the HU results and a sparse guidance map.

Hyperspectral Unmixing

Spectral Unmixing via Data-guided Sparsity

no code implementations13 Mar 2014 Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization and understanding.

Hyperspectral Unmixing

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