Search Results for author: Yingwei Pan

Found 43 papers, 15 papers with code

Representing Videos as Discriminative Sub-graphs for Action Recognition

no code implementations CVPR 2021 Dong Li, Zhaofan Qiu, Yingwei Pan, Ting Yao, Houqiang Li, Tao Mei

For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.

Action Recognition Graph Learning +1

Smart Director: An Event-Driven Directing System for Live Broadcasting

no code implementations11 Jan 2022 Yingwei Pan, Yue Chen, Qian Bao, Ning Zhang, Ting Yao, Jingen Liu, Tao Mei

To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events.

Event Detection

Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training

no code implementations11 Jan 2022 Yehao Li, Jiahao Fan, Yingwei Pan, Ting Yao, Weiyao Lin, Tao Mei

Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource downstream tasks.

Image Captioning Language Modelling +2

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

1 code implementation14 Dec 2021 Jingyang Lin, Yingwei Pan, Rongfeng Lai, Xuehang Yang, Hongyang Chao, Ting Yao

In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue.

Relational Reasoning Scene Text Detection

CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising

no code implementations14 Dec 2021 Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks.

Cross-Modal Retrieval Denoising +4

Transferrable Contrastive Learning for Visual Domain Adaptation

no code implementations14 Dec 2021 Yang Chen, Yingwei Pan, Yu Wang, Ting Yao, Xinmei Tian, Tao Mei

From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.

Contrastive Learning Domain Adaptation +1

A Style and Semantic Memory Mechanism for Domain Generalization

no code implementations ICCV 2021 Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei

Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.

Domain Generalization

X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics

1 code implementation18 Aug 2021 Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, Tao Mei

Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.

Cross-Modal Retrieval Image Captioning +4

A Low Rank Promoting Prior for Unsupervised Contrastive Learning

no code implementations5 Aug 2021 Yu Wang, Jingyang Lin, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.

Contrastive Learning Image Classification +4

Contextual Transformer Networks for Visual Recognition

4 code implementations26 Jul 2021 Yehao Li, Ting Yao, Yingwei Pan, Tao Mei

Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.

Instance Segmentation Object Detection +1

Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network

1 code implementation27 Jan 2021 Yehao Li, Yingwei Pan, Ting Yao, Jingwen Chen, Tao Mei

Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging.

Joint Contrastive Learning with Infinite Possibilities

1 code implementation NeurIPS 2020 Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei

This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.

Contrastive Learning

SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

3 code implementations3 Aug 2020 Ting Yao, Yiheng Zhang, Zhaofan Qiu, Yingwei Pan, Tao Mei

In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives.

Action Recognition Contrastive Learning +3

Pre-training for Video Captioning Challenge 2020 Summary

no code implementations27 Jul 2020 Yingwei Pan, Jun Xu, Yehao Li, Ting Yao, Tao Mei

The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.

Video Captioning

Single Shot Video Object Detector

1 code implementation7 Jul 2020 Jiajun Deng, Yingwei Pan, Ting Yao, Wengang Zhou, Houqiang Li, Tao Mei

Single shot detectors that are potentially faster and simpler than two-stage detectors tend to be more applicable to object detection in videos.

Object Detection

Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training

no code implementations5 Jul 2020 Yingwei Pan, Yehao Li, Jianjie Luo, Jun Xu, Ting Yao, Tao Mei

In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding.

Question Answering Video Captioning +2

Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation

no code implementations CVPR 2020 Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei

A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.

Unsupervised Domain Adaptation

Learning a Unified Sample Weighting Network for Object Detection

1 code implementation CVPR 2020 Qi Cai, Yingwei Pan, Yu Wang, Jingen Liu, Ting Yao, Tao Mei

To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.

General Classification Object Detection

X-Linear Attention Networks for Image Captioning

1 code implementation CVPR 2020 Yingwei Pan, Ting Yao, Yehao Li, Tao Mei

Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2$^{nd}$ order interactions across multi-modal inputs.

Fine-Grained Visual Recognition Image Captioning +2

Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019

2 code implementations8 Oct 2019 Yingwei Pan, Yehao Li, Qi Cai, Yang Chen, Ting Yao

Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data.

Domain Adaptation

Hierarchy Parsing for Image Captioning

no code implementations ICCV 2019 Ting Yao, Yingwei Pan, Yehao Li, Tao Mei

It is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image.

Image Captioning

Deep Metric Learning with Density Adaptivity

no code implementations9 Sep 2019 Yehao Li, Ting Yao, Yingwei Pan, Hongyang Chao, Tao Mei

The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric.

Metric Learning

Mocycle-GAN: Unpaired Video-to-Video Translation

no code implementations26 Aug 2019 Yang Chen, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei

Unsupervised image-to-image translation is the task of translating an image from one domain to another in the absence of any paired training examples and tends to be more applicable to practical applications.

Motion Estimation Translation +1

daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices

1 code implementation16 Aug 2019 Jianhao Zhang, Yingwei Pan, Ting Yao, He Zhao, Tao Mei

It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations.

Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation

no code implementations1 Aug 2019 Jing Wang, Yingwei Pan, Ting Yao, Jinhui Tang, Tao Mei

A valid question is how to encapsulate such gists/topics that are worthy of mention from an image, and then describe the image from one topic to another but holistically with a coherent structure.

Image Paragraph Captioning

vireoJD-MM at Activity Detection in Extended Videos

no code implementations20 Jun 2019 Fuchen Long, Qi Cai, Zhaofan Qiu, Zhijian Hou, Yingwei Pan, Ting Yao, Chong-Wah Ngo

This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019.

Action Detection Action Localization +1

Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019

no code implementations14 Jun 2019 Zhaofan Qiu, Dong Li, Yehao Li, Qi Cai, Yingwei Pan, Ting Yao

This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, dense-captioning events in videos, and spatio-temporal action localization.

Action Recognition Spatio-Temporal Action Localization

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

1 code implementation3 May 2019 Jingwen Chen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao, Tao Mei

Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations.

Video Captioning

Transferrable Prototypical Networks for Unsupervised Domain Adaptation

no code implementations CVPR 2019 Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, Tao Mei

Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar.

Unsupervised Domain Adaptation

Exploring Object Relation in Mean Teacher for Cross-Domain Detection

1 code implementation CVPR 2019 Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Ling-Yu Duan, Ting Yao

The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student.

Unsupervised Domain Adaptation

Exploring Visual Relationship for Image Captioning

no code implementations ECCV 2018 Ting Yao, Yingwei Pan, Yehao Li, Tao Mei

Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections.

Image Captioning

Memory Matching Networks for One-Shot Image Recognition

no code implementations CVPR 2018 Qi Cai, Yingwei Pan, Ting Yao, Chenggang Yan, Tao Mei

In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.

One-Shot Learning

Jointly Localizing and Describing Events for Dense Video Captioning

no code implementations CVPR 2018 Yehao Li, Ting Yao, Yingwei Pan, Hongyang Chao, Tao Mei

A valid question is how to temporally localize and then describe events, which is known as "dense video captioning."

Dense Video Captioning

To Create What You Tell: Generating Videos from Captions

no code implementations23 Apr 2018 Yingwei Pan, Zhaofan Qiu, Ting Yao, Houqiang Li, Tao Mei

In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.

Deep Semantic Hashing with Generative Adversarial Networks

no code implementations23 Apr 2018 Zhaofan Qiu, Yingwei Pan, Ting Yao, Tao Mei

Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an adversary stream to distinguish synthetic images from real ones, a hash stream for encoding image representations to hash codes and a classification stream.

General Classification Image Retrieval

Video Captioning with Transferred Semantic Attributes

no code implementations CVPR 2017 Yingwei Pan, Ting Yao, Houqiang Li, Tao Mei

Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community.

Video Captioning

Boosting Image Captioning with Attributes

no code implementations ICCV 2017 Ting Yao, Yingwei Pan, Yehao Li, Zhaofan Qiu, Tao Mei

Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing.

Image Captioning

Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition

no code implementations CVPR 2015 Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, Tao Mei

In many real-world applications, we are often facing the problem of cross domain learning, i. e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain.

Domain Adaptation Object Recognition

Jointly Modeling Embedding and Translation to Bridge Video and Language

no code implementations CVPR 2016 Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui

Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics.


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