579 citations · 3k across the 98 of their papers we have counts for
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MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
Wei Ji, Long Chen, Yinwei Wei +2
Given an untrimmed video and natural language query, video sentence grounding aims to localize the target temporal moment in the video. Existing methods mainly tackle this task by…
Causal Inference for Knowledge Graph based Recommendation
Yinwei Wei, Xiang Wang, Liqiang Nie +3
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model. By mapping items with the entities in K…
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms
Peng Qi, Yuyan Bu, Juan Cao +5
Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detecti…
Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
Jianwu Fang, Lei-Lei Li, Kuan Yang +3
Traffic accident prediction in driving videos aims to provide an early warning of the accident occurrence, and supports the decision making of safe driving systems. Previous works…
Visually Grounded Commonsense Knowledge Acquisition
Yuan Yao, Tianyu Yu, Ao Zhang +8
Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging pro…
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
Bobo Li, Hao Fei, Fei Li +8
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limi…
Behavioral Intention Prediction in Driving Scenes: A Survey
Jianwu Fang, Fan Wang, Jianru Xue +1
In the driving scene, the road agents usually conduct frequent interactions and intention understanding of the surroundings. Ego-agent (each road agent itself) predicts what behavi…
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
Weiwen Xu, Yang Deng, Wenqiang Lei +3
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substan…
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
An Zhang, Wenchang Ma, Xiang Wang +1
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategie…
PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance
Yang Deng, Wenqiang Lei, Wenxuan Zhang +2
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibit…
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Shengyu Zhang, Bofang Li, Dong Yao +7
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao +5
Effectively representing users lie at the core of modern recommender systems. Since users' interests naturally exhibit multiple aspects, it is of increasing interest to develop mul…
Equivariant and Invariant Grounding for Video Question Answering
Yicong Li, Xiang Wang, Junbin Xiao +1
Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual sce…
Video Graph Transformer for Video Question Answering
Junbin Xiao, Pan Zhou, Tat-Seng Chua +1
This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module whi…
Towards Complex Document Understanding By Discrete Reasoning
Fengbin Zhu, Wenqiang Lei, Fuli Feng +3
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural La…
Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv +6
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In…
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li, Xiang Wang, An zhang +3
Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic infor…
Invariant Grounding for Video Question Answering
Yicong Li, Xiang Wang, Junbin Xiao +2
Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic seman…
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, An Zhang +2
Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advant…
Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang +3
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are…
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
Yuan Yao, Qianyu Chen, Ao Zhang +4
Vision-language pre-training (VLP) has shown impressive performance on a wide range of cross-modal tasks, where VLP models without reliance on object detectors are becoming the mai…
User-controllable Recommendation Against Filter Bubbles
Wenjie Wang, Fuli Feng, Liqiang Nie +1
Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along…
Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang +3
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent tec…
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Yang Deng, Wenxuan Zhang, Weiwen Xu +3
Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users' interests and naturally lead user-e…