Search Results for author:
Found 28 papers, 13 papers with code
BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation.
Ranked #21 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(VMAF metric)
MTL-SLT: Multi-Task Learning for Spoken Language Tasks
The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Attention-Guided Low-Rank Tensor Completion
In this work, we propose an attention-guided low-rank tensor completion (AGTC) algorithm, which can faithfully restore the original structures of data tensors using deep unfolding attention-guided tensor factorization.
Deep Unfolding Tensor Rank Minimization With Generalized Detail Injection for Pansharpening
Finally, the iterative algorithm is unfolded into a multistage deep network, in which the optimization variables are solved by closed-form solutions and a data-driven regularizer in each stage.
3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking.
H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields
Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction.
Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields
Text-driven localized editing of 3D objects is particularly difficult as locally mixing the original 3D object with the intended new object and style effects without distorting the object's form is not a straightforward process.
Multiscale Coarse-to-Fine Guided Screenshot Demoiréing
In this letter, we propose a multiscale coarse-to-fine guided screenshot demoiréing algorithm.
Cross-modal transformers for infrared and visible image fusion
In this work, we propose a cross-modal transformer-based fusion (CMTFusion) algorithm for infrared and visible image fusion that captures global interactions by faithfully extracting complementary information from source images.
Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis
While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties.
Harmonic (Quantum) Neural Networks
Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation.
Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging
We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image.
Deep Unrolled Low-Rank Tensor Completion for High Dynamic Range Imaging
In this paper, we propose an algorithm unrolling approach to ghost-free HDR image synthesis algorithm that unrolls an iterative low-rank tensor completion algorithm into deep neural networks to take advantage of the merits of both learning- and model-based approaches while overcoming their weaknesses.
Depth Map Decomposition for Monocular Depth Estimation
We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features.
Ranked #46 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer
Current keyword spotting systems are typically trained with a large amount of pre-defined keywords.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
openFEAT: Improving Speaker Identification by Open-set Few-shot Embedding Adaptation with Transformer
Household speaker identification with few enrollment utterances is an important yet challenging problem, especially when household members share similar voice characteristics and room acoustics.
Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
First, we predict symmetric bilateral motion fields to interpolate an anchor frame.
Ranked #7 on
Video Frame Interpolation
on Vimeo90K
On joint training with interfaces for spoken language understanding
Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
End-to-end Neural Diarization: From Transformer to Conformer
We propose a new end-to-end neural diarization (EEND) system that is based on Conformer, a recently proposed neural architecture that combines convolutional mappings and Transformer to model both local and global dependencies in speech.
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing
We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks.
BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakers
We present a novel online end-to-end neural diarization system, BW-EDA-EEND, that processes data incrementally for a variable number of speakers.
BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation.
Ranked #3 on
Video Frame Interpolation
on Middlebury
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Cross-modal Learning for Multi-modal Video Categorization
Multi-modal machine learning (ML) models can process data in multiple modalities (e. g., video, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding, activity recognition).
Exploiting Temporal Coherence for Multi-modal Video Categorization
Multimodal ML models can process data in multiple modalities (e. g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding).
Semantic Line Detection and Its Applications
Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps.
Ranked #3 on
Line Detection
on SEL
A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis
For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames.
