864 citations · 1.9k across the 57 of their papers we have counts for
59 papers
End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system
Zhengyi Zhang, Pan Zhou
End-to-end (E2E) speech recognition architectures assemble all components of traditional speech recognition system into a single model. Although it simplifies ASR system, it introd…
Exploring Motion and Appearance Information for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Pan Zhou +1
This paper addresses temporal sentence grounding. Previous works typically solve this task by learning frame-level video features and align them with the textual information. A maj…
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization
Wenbo Gou, Wen Shi, Jian Lou +3
Natural language video localization (NLVL) is an important task in the vision-language understanding area, which calls for an in-depth understanding of not only computer vision and…
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Yang Peng, Ping Liu, Yawei Luo +3
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning…
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
Yuxuan Liang, Pan Zhou, Roger Zimmermann +1
While transformers have shown great potential on video recognition with their strong capability of capturing long-range dependencies, they often suffer high computational costs ind…
MetaFormer Is Actually What You Need for Vision
Weihao Yu, Mi Luo, Pan Zhou +5
Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent…
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition
Guolin Zheng, Yubei Xiao, Ke Gong +3
Unifying acoustic and linguistic representation learning has become increasingly crucial to transfer the knowledge learned on the abundance of high-resource language data for low-r…
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Pan Zhou
A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence…
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos
Daizong Liu, Xiaoye Qu, Jianfeng Dong +1
We address the problem of temporal sentence localization in videos (TSLV). Traditional methods follow a top-down framework which localizes the target segment with pre-defined segme…
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network
Zhikang Zou, Xiaoye Qu, Pan Zhou +4
Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applic…
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
Pan Zhou, Caiming Xiong, Xiao-Tong Yuan +1
For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assig…
Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
Wei Wei, Jiayi Liu, Xianling Mao +4
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge i…
Exploiting Global Contextual Information for Document-level Named Entity Recognition
Zanbo Wang, Wei Wei, Xianling Mao +4
Most existing named entity recognition (NER) approaches are based on sequence labeling models, which focus on capturing the local context dependencies. However, the way of taking o…
Prototypical Graph Contrastive Learning
Shuai Lin, Pan Zhou, Zi-Yuan Hu +6
Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generall…
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things
Huming Qiu, Hua Ma, Zhi Zhang +6
Though deep neural network models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations render deployment on mobi…
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition
Zhichao Wang, Wenwen Yang, Pan Zhou +1
Recently, attention-based encoder-decoder (AED) end-to-end (E2E) models have drawn more and more attention in the field of automatic speech recognition (ASR). AED models, however,…
Darts-Conformer: Towards Efficient Gradient-Based Neural Architecture Search For End-to-End ASR
Xian Shi, Pan Zhou, Wei Chen +1
Neural architecture search (NAS) has been successfully applied to tasks like image classification and language modeling for finding efficient high-performance network architectures…
Context-aware Biaffine Localizing Network for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Jianfeng Dong +5
This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence quer…
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Wenxiao Wang, Tianhao Wang, Lun Wang +4
Deep learning techniques have achieved remarkable performance in wide-ranging tasks. However, when trained on privacy-sensitive datasets, the model parameters may expose private in…
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Shuai Lin, Pan Zhou, Xiaodan Liang +4
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded di…
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation
Daizong Liu, Shuangjie Xu, Xiao-Yang Liu +3
This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these…
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Daizong Liu, Dongdong Yu, Changhu Wang +1
Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearanc…
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Yubei Xiao, Ke Gong, Pan Zhou +3
Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulat…
User-based Network Embedding for Collective Opinion Spammer Detection
Ziyang Wang, Wei Wei, Xian-Ling Mao +3
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the infl…