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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TLDR
This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting. Expand
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
TLDR
This paper revisits GCN based CF models from two aspects and proposes a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user- item interaction data. Expand
Learning A Task-Specific Deep Architecture For Clustering
TLDR
It is discovered that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation, leading to a carefully crafted deep model benefiting from both. Expand
DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting
TLDR
A novel deep model called Dilated-Attention-Deformable ConvNet (DADNet), which consists of two schemes: multi-scale dilated attention and deformable convolutional DME (Density Map Estimation). Expand
Semi-supervised multi-graph hashing for scalable similarity search
TLDR
A semi-supervised Multi-Graph Hashing (MGH) framework is proposed that can effectively integrate the multiple modalities with optimized weights in a multi-graph learning scheme and can be more effective for fast similarity search. Expand
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
TLDR
A data-limited TrojanNet detector (TND) is proposed, which can detect a TrojanNet without accessing any data samples, and it is shown that such a TND can be built by leveraging the internal response of hidden neurons, which exhibits the Trojan behavior even at random noise inputs. Expand
Iterative Context-Aware Graph Inference for Visual Dialog
TLDR
This work proposes a novel Context-Aware Graph (CAG) neural network, where each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. Expand
USAR: An Interactive User-specific Aesthetic Ranking Framework for Images
TLDR
A novel and user-friendly aesthetic ranking framework via powerful deep neural network and a small amount of user interaction, which can automatically estimate and rank the aesthetic characteristics of images in accordance with users' preference. Expand
Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
TLDR
A Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos to facilitate video retrieval with complex queries, thereby achieving a better video retrieval performance. Expand
Question-Aware Tube-Switch Network for Video Question Answering
TLDR
A novel Question- Aware Tube-Switch Network (TSN) for video question answering which contains a Mix module to synchronously combine the appearance and motion representation at time slice level and a Switch module to adaptively choose appearance or motion tube as primary at each reasoning step, guiding the multi-hop reasoning process. Expand
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