Search Results for author:
Found 34 papers, 2 papers with code
3D Fluid Flow Reconstruction Using Compact Light Field PIV
In this paper, we present a PIV solution that uses a compact lenslet-based light field camera to track dense particles floating in the fluid and reconstruct the 3D fluid flow.
Predicting Preschoolers' Externalizing Problems with Mother-Child Interaction Dynamics and Deep Learning
The deep learning model is a useful tool to further improve prediction accuracy.
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation
Constrained decoding approaches aim to control the meaning or style of text generated by a Pre-trained Language Model (PLM) using specific target words during inference.
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input.
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms.
Towards Multi-Subsession Conversational Recommendation
Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession.
Textureless Deformable Surface Reconstruction with Invisible Markers
Leveraging the markers, we design a multi-camera system that captures surface deformation under the UV light and the visible light in a time multiplexing fashion.
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
Designs, Motion Mechanism, Motion Coordination, and Communication of Bionic Robot Fishes: A Survey
Various methods are needed to narrow the gap in swimming performance between robot fishes and fish.
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling
Recovering 3D geometry of underwater scenes is challenging because of non-linear refraction of light at the water-air interface caused by the camera housing.
Polarimetric Helmholtz Stereopsis
Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i. e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance.
A Neural Rendering Framework for Free-Viewpoint Relighting
We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs.
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits
Despite the success in model reduction of convolutional neural networks (CNNs), neural network quantization methods have not yet been studied on GANs, which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models.
PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera
Particle Imaging Velocimetry (PIV) estimates the flow of fluid by analyzing the motion of injected particles.
Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field
In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces with arbitrary material in one shot.
3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile.
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.
Cryptography and Security Hardware Architecture
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture
In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing.
QGAN: Quantized Generative Adversarial Networks
The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones.
Programmable Neural Network Trojan for Pre-Trained Feature Extractor
Existing studies have explored the outsourced training attack scenario and transfer learning attack scenario in some small datasets for specific domains, with limited numbers of fixed target classes.
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Increasing the sparsity granularity can lead to better hardware utilization, but it will compromise the sparsity for maintaining accuracy.
Learning to Dodge A Bullet: Concyclic View Morphing via Deep Learning
The bullet-time effect, presented in feature film ``The Matrix", has been widely adopted in feature films and TV commercials to create an amazing stopping-time illusion.
Crossbar-aware neural network pruning
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc.
Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler
Different from developing neural networks (NNs) for general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, such as limited precision of network signals and parameters, constrained computation scale, and limited types of non-linear functions.
Ambient Occlusion via Compressive Visibility Estimation
This enables a sparsity-prior based solution for iteratively recovering the surface normal, the surface albedo, and the visibility function from a small number of images.
Saliency Detection on Light Field
Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions.
Image Pre-compensation: Balancing Contrast and Ringing
In particular, we show that linear tone mapping eliminates ringing but incurs severe contrast loss, while non-linear tone mapping functions such as Gamma curves slightly enhances contrast but introduces ringing.
Manhattan Scene Understanding via XSlit Imaging
Specifically, we prove that parallel 3D lines map to 2D curves in an XSlit image and they converge at an XSlit Vanishing Point (XVP).
Reconstructing Gas Flows Using Light-Path Approximation
By observing the LF-probe through the gas flow, we acquire a dense set of ray-ray correspondences and then reconstruct their light paths.
