Search Results for author: Yuan Shen

Found 19 papers, 4 papers with code

Injecting Image Details into CLIP's Feature Space

no code implementations31 Aug 2022 Zilun Zhang, Cuifeng Shen, Yuan Shen, Huixin Xiong, Xinyu Zhou

Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e. g., 224), subtle details are lost in the feature representation if we input high-resolution images (e. g., 2240).

Retrieval

Distributed Scheduling at Non-Signalized Intersections with Mixed Cooperative and Non-Cooperative Vehicles

no code implementations30 Jul 2022 Feihong Yang, Yuan Shen

Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems.

Management Scheduling +1

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)

no code implementations8 Jul 2022 Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, Katherine Driggs-Campbell

In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.

Driver Attention Monitoring

Learning Dynamic View Synthesis With Few RGBD Cameras

no code implementations22 Apr 2022 Shengze Wang, Youngjoong Kwon, Yuan Shen, Qian Zhang, Andrei State, Jia-Bin Huang, Henry Fuchs

Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency.

Novel View Synthesis

A Minimax Framework for Two-Agent Scheduling with Inertial Constraints

no code implementations13 Jan 2022 Feihong Yang, Yuan Shen

Autonomous agents are promising in applications such as intelligent transportation and smart manufacturing, and scheduling of agents has to take their inertial constraints into consideration.

Scheduling

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset

no code implementations19 Nov 2021 Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, Katie Driggs-Campbell

In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.

Driver Attention Monitoring

AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style

no code implementations6 Jul 2021 Yuzi Yan, Xu Tan, Bohan Li, Guangyan Zhang, Tao Qin, Sheng Zhao, Yuan Shen, Wei-Qiang Zhang, Tie-Yan Liu

While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.

Effectively Leveraging Attributes for Visual Similarity

1 code implementation ICCV 2021 Samarth Mishra, Zhongping Zhang, Yuan Shen, Ranjitha Kumar, Venkatesh Saligrama, Bryan Plummer

This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.

Retrieval

AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data

1 code implementation20 Apr 2021 Yuzi Yan, Xu Tan, Bohan Li, Tao Qin, Sheng Zhao, Yuan Shen, Tie-Yan Liu

In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.

Building Mental Models through Preview of Autopilot Behaviors

no code implementations12 Apr 2021 Yuan Shen, Niviru Wijayaratne, Katherine Driggs-Campbell

Effective human-vehicle collaboration requires an appropriate un-derstanding of vehicle behavior for safety and trust.

Future prediction

AutoPreview: A Framework for Autopilot Behavior Understanding

no code implementations25 Feb 2021 Yuan Shen, Niviru Wijayaratne, Peter Du, Shanduojiao Jiang, Katherine Driggs Campbell

The behavior of self driving cars may differ from people expectations, (e. g. an autopilot may unexpectedly relinquish control).

Self-Driving Cars

Generalized Maximum Entropy for Supervised Classification

1 code implementation10 Jul 2020 Santiago Mazuelas, Yuan Shen, Aritz Pérez

The maximum entropy principle advocates to evaluate events' probabilities using a distribution that maximizes entropy among those that satisfy certain expectations' constraints.

Classification General Classification

To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles

no code implementations21 Jun 2020 Yuan Shen, Shanduojiao Jiang, Yanlin Chen, Katie Driggs Campbell

Explainable AI, in the context of autonomous systems, like self-driving cars, has drawn broad interests from researchers.

Self-Driving Cars

SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

no code implementations CVPR 2020 Yanru Huang, Feiyu Zhu, Zheni Zeng, Xi Qiu, Yuan Shen, Jia-Nan Wu

We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task.

Multi-Object Tracking

Can AI decrypt fashion jargon for you?

no code implementations18 Mar 2020 Yuan Shen, Shanduojiao Jiang, Muhammad Rizky Wellyanto, Ranjitha Kumar

Finally, we trained a deep learning model that can explicitly predict and explain high level fashion concepts in a product image with its low level and domain specific fashion features.

A Classification Framework for Partially Observed Dynamical Systems

no code implementations7 Jul 2016 Yuan Shen, Peter Tino, Krasimira Tsaneva-Atanasova

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space.

Classification General Classification

Deep Learning Stock Volatility with Google Domestic Trends

2 code implementations15 Dec 2015 Ruoxuan Xiong, Eric P. Nichols, Yuan Shen

We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors.

Computational Finance

Variational Inference for Diffusion Processes

no code implementations NeurIPS 2007 Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John S. Shawe-Taylor

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed.

Variational Inference

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