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- Jiawei HanSuggested from co-authorship
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Works99 from public data
- 2,450
- 1,717
- RESA: Recurrent Feature-Shift Aggregator for Lane Detection380
A novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN and achieves state-of-the-art results on two popular lane detection benchmarks (CULane and Tusimple).
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- Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion284
A novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above and a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds.
- Graph Transformer for Graph-to-Sequence Learning257
A new model, known as Graph Transformer, is proposed that uses explicit relation encoding and allows direct communication between two distant nodes and provides a more efficient way for global graph structure modeling.
- Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System223
This study presents PPTOD, a unified plug-and-play model for task-oriented dialogue, and introduces a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora.
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- Community Mining from Multi-relational Networks178
This paper systematically analyzes the problem of mining hidden communities on heterogeneous social networks and proposes a new method for learning an optimal linear combination of these relations which can best meet the user's expectation.
- Neural Word Segmentation Learning for Chinese162
A novel neural framework is proposed which thoroughly eliminates context windows and can utilize complete segmentation history and employs a gated combination neural network over characters to produce distributed representations of word candidates, which are then given to a long short-term memory (LSTM) language scoring model.
- AMR Parsing via Graph-Sequence Iterative Inference124
A new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy.
- Neural Machine Translation with Monolingual Translation Memory121
A new framework that uses monolingual memory and performs learnable memory retrieval in a cross-lingual manner and outperforms strong TM-augmented NMT baselines using bilingual TM.
- Recent Advances in Retrieval-Augmented Text Generation116
The generic paradigm of retrieval-augmented text generation is highlighted, notable works for different text generation tasks including dialogue generation, machine translation, and other generation tasks are reviewed, and some limitations and shortcomings are pointed out to facilitate future research.
- Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory99
This paper proposes a new framework which exploits retrieval results via a skeleton-to-response paradigm and shows that this approach significantly improves the informativeness of the generated responses.
- Whom to mention99
An in-depth study of mention mechanism and a recommendation scheme to solve the essential question of whom to mention in a tweet are presented and the advantage of the proposed algorithm compared with the traditional recommendation methods is demonstrated.
- Fast and Accurate Neural Word Segmentation for Chinese96
This paper proposes a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks of current neural models, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.
- Charge-Based Prison Term Prediction with Deep Gating Network93
This paper argues that charge-based prison term prediction (CPTP) not only better fits realistic needs, but also makes the total jail term prediction more accurate and interpretable.
- Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework91
A novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator is presented.
- COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning84
A novel algorithm named COP (correlation-based pruning) is developed, which can detect the redundant filters efficiently and enable the cross-layer filter comparison through global normalization, and adds parameter-quantity and computational-cost regularization terms to the importance.
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- Tracking people in RGBD videos using deep learning and motion clues72
This work proposes to train a deep convolutional neural network, which improves tracking performance, to classify people and a motion model based on spatial and kinetic clues is combined with the network to track people in the scene.
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- Describe What to Change62
This work proposes a novel unsupervised approach, based on image-to-image translation, that alters the attributes of a given image through a command-like sentence such as "change the hair color to black".
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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