116 citations · 225 across the 27 of their papers we have counts for
27 papers
Controlled Dynamics Attractor Transformer
Cheng Zhang, Minnan Luo, Zesheng Yang +3
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) fra…
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
Haochen Han, Qinghua Zheng, Guang Dai +2
Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However, in real-world scenarios, massive multimodal data are harvested from the I…
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts
Zinan Zeng, Sen Ye, Zijian Cai +4
Online movie review websites are valuable for information and discussion about movies. However, the massive spoiler reviews detract from the movie-watching experience, making spoil…
Disentangled Representation Learning with Transmitted Information Bottleneck
Zhuohang Dang, Minnan Luo, Chengyou Jia +4
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Al…
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
Chengyou Jia, Minnan Luo, Zhuohang Dang +3
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentificat…
Clustering based Point Cloud Representation Learning for 3D Analysis
Tuo Feng, Wenguan Wang, Xiaohan Wang +2
Point cloud analysis (such as 3D segmentation and detection) is a challenging task, because of not only the irregular geometries of many millions of unordered points, but also the…
HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention
Sen Ye, Zhaoxuan Tan, Zhenyu Lei +4
Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity o…
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
Zijian Cai, Zhaoxuan Tan, Zhenyu Lei +4
As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial…
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Yuhan Liu, Zhaoxuan Tan, Heng Wang +3
Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter…
Noisy Correspondence Learning with Meta Similarity Correction
Haochen Han, Kaiyao Miao, Qinghua Zheng +1
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting su…
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Heng Wang, Wenqian Zhang, Yuyang Bai +4
Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although p…
Disentangled Generation with Information Bottleneck for Few-Shot Learning
Zhuohang Dang, Jihong Wang, Minnan Luo +3
Few-shot learning (FSL), which aims to classify unseen classes with few samples, is challenging due to data scarcity. Although various generative methods have been explored for FSL…
Generalized Category Discovery with Decoupled Prototypical Network
Wenbin An, Feng Tian, Qinghua Zheng +3
Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Wi…
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Shangbin Feng, Zhaoxuan Tan, Zilong Chen +5
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches ar…
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning
Wenbin An, Feng Tian, Ping Chen +3
Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of…
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
Shujie Yang, Binchi Zhang, Shangbin Feng +4
Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and e…
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Xinshun Feng, Herun Wan, Shangbin Feng +4
Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers.…
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
Zhenyu Lei, Herun Wan, Wenqian Zhang +5
Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify b…
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Zhaoxuan Tan, Zilong Chen, Shangbin Feng +4
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most exi…
Towards Explanation for Unsupervised Graph-Level Representation Learning
Qinghua Zheng, Jihong Wang, Minnan Luo +4
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the inpu…
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Shangbin Feng, Zhaoxuan Tan, Herun Wan +19
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. St…
Noise-Tolerant Learning for Audio-Visual Action Recognition
Haochen Han, Qinghua Zheng, Minnan Luo +3
Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the mode…
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
Chengyou Jia, Minnan Luo, Caixia Yan +2
Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this prob…
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Jihong Wang, Minnan Luo, Jundong Li +3
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rende…