579 citations · 3k across the 98 of their papers we have counts for
21 papers · 1 filter
Rethinking the Two-Stage Framework for Grounded Situation Recognition
Meng Wei, Long Chen, Wei Ji +2
Grounded Situation Recognition (GSR), i.e., recognizing the salient activity (or verb) category in an image (e.g., buying) and detecting all corresponding semantic roles (e.g., age…
Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E…
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui, Xiang Wang, Jiancan Wu +3
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mos…
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
Junbin Xiao, Angela Yao, Zhiyuan Liu +3
Video question answering requires the models to understand and reason about both the complex video and language data to correctly derive the answers. Existing efforts have been foc…
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models
Yuan Yao, Ao Zhang, Zhengyan Zhang +3
Pre-Trained Vision-Language Models (VL-PTMs) have shown promising capabilities in grounding natural language in image data, facilitating a broad variety of cross-modal tasks. Howev…
How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction
Zikun Hu, Yixin Cao, Lifu Huang +1
Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitati…
Contrastive Learning for Cold-Start Recommendation
Yinwei Wei, Xiang Wang, Qi Li +4
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use co…
Empowering Language Understanding with Counterfactual Reasoning
Fuli Feng, Jizhi Zhang, Xiangnan He +2
Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning. However, existing methods indiscriminately use…
Deconfounded Video Moment Retrieval with Causal Intervention
Xun Yang, Fuli Feng, Wei Ji +2
We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching…
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang +5
Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is la…
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Yujuan Ding, Yunshan Ma, Wai Keung Wong +1
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. Th…
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
Yunshan Ma, Yujuan Ding, Xun Yang +5
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR…
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
Junbin Xiao, Xindi Shang, Angela Yao +1
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on…
Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-reco…
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Yujuan Ding, Yunshan Ma, Lizi Liao +2
Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devote…
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Zhenghao Liu, Xiaoyuan Yi, Maosong Sun +2
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious cor…
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
An Zhang, Xiang Wang, Chengfang Fang +3
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs). However, the redundancy of attribution features and the gradient saturation…
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
Guanghao Yin, Wei Wang, Zehuan Yuan +5
Although single-image super-resolution (SISR) methods have achieved great success on single degradation, they still suffer performance drop with multiple degrading effects in real…
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He +2
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation model…
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Fengbin Zhu, Wenqiang Lei, Chao Wang +3
Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-sc…