Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks53 from public data
- Dissociated neural substrates underlying impulsive choice and impulsive action130
Clearly neural dissociations between impulsive choice and impulsive action are demonstrated, which provide new insights into the nature of impulsivity, and have implications for impulsivity-related disorders.
- Deep Animation Video Interpolation in the Wild127
This work formally defines and study the animation video interpolation problem for the first time, and proposes an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner, which outperforms existing state-of-the-art interpolation methods for animation videos.
- Enhanced Quadratic Video Interpolation100
This work improves the performance of QVI from three facets and proposes an enhanced quadratic video interpolation (EQVI) model that won the first place in the AIM2020 Video Temporal Super-Resolution Challenge and devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process.
- The Role of the Frontal and Parietal Cortex in Proactive and Reactive Inhibitory Control: A Transcranial Direct Current Stimulation Study84
A causal role of the right IFG, but not the right IPL, in both reactive and proactive inhibitory control is suggested, using transcranial direct current stimulation and the stop signal task.
- Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment70
A GPT-based model is proposed, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader's and the follower's movements, and devise an off-policy reinforcement learning strategy that allows the model to explore viable trajectories from out-of-distribution samplings, guided by human-defined rewards.
- Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting63
This work proposes a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain and argues that Question Generation (QG) systems should have stronger control over the logic of generated questions.
- Dissociated roles of the parietal and frontal cortices in the scope and control of attention during visual working memory51
The results suggest that the PPC and PFC are involved in the scope and control of attention, respectively, and provide a deeper understanding of the neural mechanisms of WM.
- GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement48
A novel two-stage framework that focuses on utilizing bidirectional relations within verbs and roles is proposed, and extensive experimental results show that the renovated framework outperforms other state-of-the-art methods under various metrics.
- Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization46
Human judgments on Gigaword and CNN/Daily Mail datasets show that the proposed distributional semantics reward (DSR) has distinct superiority in capturing the lexical and compositional diversity of natural language.
- 39
- Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data28
A crash frequency predicting model for urban expressway considering collision types is proposed and indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level.
- An intelligent cross-border transaction system based on consortium blockchain: A case study in Shenzhen, China25
A new consortium blockchain system, namely asymmetric consortium blockchain (ACB), is proposed to ensure the implementation of cross-border transactions and is efficient to make the profit allocation fairer for the participants and keep intelligent for the new cross- border transaction system.
- Distinct neural substrates for visual short‐term memory of actions25
The results provide clear evidence for a distinct neural system for maintaining actions in VSTM, which supports the core knowledge system theory and the domain‐specific and domain‐general architectures of V STM.
- AIM 2020 Challenge on Video Temporal Super-Resolution22
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis, and proposes the enhanced quadratic video interpolation method.
- AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results20
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
- Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health17
It is found that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses, and is recommended that visualization recommendation designers explore ways of integrating context and expectation into their systems.
- NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results16
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain, and some existing methods are evaluated, showing promising solutions to the weakly-supervised videoquality mapping problem.
- 9
- Research of Deep Learning and Adaptive Threshold-Based Signaling Storm Prediction and Top Cause Tracking8
The method enables the top cause of the signaling storm in advance to be identified, so that the network optimization team can address the issues that will arise in advance, maximizing the stop-loss.
- 8
- Demonstration of VegaPlus: Optimizing Declarative Visualization Languages7
This work proposes a system that takes declarative visualization specifications as input and automatically optimizes the resulting visualization execution plans by offloading computational-intensive operations to a separate database management system (DBMS).
- 7
- 6
- 5
- TransKS: An Anomaly Detection Method for Telecommunication Networks Based on Deep Learning5
A multi-dimensional sequence anomaly detection method named TransKS is proposed that integrates Transform and K-SIGMA on dynamic variable thresholds and applies it on the VoLTE and 5G core network and has a higher accuracy rate of anomaly detection.
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 2026-10-15. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.