134 citations · 511 across the 22 of their papers we have counts for
23 papers
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…
CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification
Huidong Liu, Shaoyuan Xu, Jinmiao Fu +5
Modern Web systems such as social media and e-commerce contain rich contents expressed in images and text. Leveraging information from multi-modalities can improve the performance…
TransZero: Attribute-guided Transformer for Zero-Shot Learning
Shiming Chen, Ziming Hong, Yang Liu +6
Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descripti…
Cross Modal Retrieval with Querybank Normalisation
Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin +2
Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-…
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
Yang Liu, Keze Wang, Lingbo Liu +2
Video self-supervised learning is a challenging task, which requires significant expressive power from the model to leverage rich spatial-temporal knowledge and generate effective…
Causal Homotopy
Sridhar Mahadevan
We characterize homotopical equivalences between causal DAG models, exploiting the close connections between partially ordered set representations of DAGs (posets) and finite Alexa…
Deconvolutional Networks on Graph Data
Jia Li, Jiajin Li, Yang Liu +3
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct th…
A Short Study on Compressing Decoder-Based Language Models
Tianda Li, Yassir El Mesbahi, Ivan Kobyzev +6
Pre-trained Language Models (PLMs) have been successful for a wide range of natural language processing (NLP) tasks. The state-of-the-art of PLMs, however, are extremely large to b…
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Shiming Chen, Guo-Sen Xie, Yang Liu +5
Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge…
Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation
Jiabo Huang, Yang Liu, Shaogang Gong +1
Video activity localisation has recently attained increasing attention due to its practical values in automatically localising the most salient visual segments corresponding to the…
Improving Factual Consistency of Abstractive Summarization on Customer Feedback
Yang Liu, Yifei Sun, Vincent Gao
E-commerce stores collect customer feedback to let sellers learn about customer concerns and enhance customer order experience. Because customer feedback often contains redundant i…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…
TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu +4
In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerfu…
MogFace: Towards a Deeper Appreciation on Face Detection
Yang Liu, Fei Wang, Jiankang Deng +3
Benefiting from the pioneering design of generic object detectors, significant achievements have been made in the field of face detection. Typically, the architectures of the backb…
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
Chang Qi, Junyang Chen, Guizhi Xu +3
Recently deep learning methods, in particular, convolutional neural networks (CNNs), have led to a massive breakthrough in the range of computer vision. Also, the large-scale annot…
QuerYD: A video dataset with high-quality text and audio narrations
Andreea-Maria Oncescu, João F. Henriques, Yang Liu +2
We introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video. A unique feature of our dataset is the availability of two audio tracks for each video…
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
Yang Liu, Keze Wang, Guanbin Li +1
Existing vision-based action recognition is susceptible to occlusion and appearance variations, while wearable sensors can alleviate these challenges by capturing human motion with…
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
Samuel Albanie, Yang Liu, Arsha Nagrani +18
We present a new video understanding pentathlon challenge, an open competition held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.…
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
Rong Kang, Jieqi Shi, Xueming Li +2
As the foundation of driverless vehicle and intelligent robots, Simultaneous Localization and Mapping(SLAM) has attracted much attention these days. However, non-geometric modules…
U-Net: Machine Reading Comprehension with Unanswerable Questions
Fu Sun, Linyang Li, Xipeng Qiu +1
Machine reading comprehension with unanswerable questions is a new challenging task for natural language processing. A key subtask is to reliably predict whether the question is un…
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
Sheng Chen, Jia Guo, Yang Liu +2
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 no…
Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework
Yang Liu
To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal informati…
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Sheng Chen, Yang Liu, Xiang Gao +1
We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verifi…