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Found 31 papers, 20 papers with code
DiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates
Although image compression is fundamental to visual data processing and has inspired numerous standard and learned codecs, these methods still suffer severe quality degradation at extremely low bits per pixel.
TESU-LLM: Training Speech-LLMs Without Speech via Unified Encoder Alignment
Our key insight is to leverage a unified encoder that maps semantically equivalent text and speech inputs to a shared latent space.
Event-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems
Implantable brain-machine interfaces (iBMIs) are evolving to record from thousands of neurons wirelessly but face challenges in data bandwidth, power consumption, and implant size.
Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators
Low-precision ADCs can reduce this overhead but introduce partial-sum quantization errors degrading accuracy.
MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures
The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing (IMC) architectures faces significant challenges due to the mismatch between highdimensional vectors and IMC array sizes, leading to inefficient memory utilization and increased computation cycles.
Low-Rank Compression for IMC Arrays
In this study, we address the challenge of low-rank model compression in the context of in-memory computing (IMC) architectures.
Test-Time Fine-Tuning of Image Compression Models for Multi-Task Adaptability
To address this issue, this paper proposes a fully instance-specific test time fine-tuning (TTFT) for adapting learned image compression (LIC) to both closed-set and open-set machine vision tasks effectively.
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training
Our method reduces the number of patches for the training to 4% to 25%, depending on the resolution and number of clusters, while maintaining high video quality and significantly enhancing training efficiency.
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
With model compression techniques such as quantization and entropy coding, we consistently show over 25x reduced storage and enhanced rendering speed compared to 3DGS for static scenes, while maintaining the quality of the scene representation.
HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation
To address this challenge, this paper proposes the Denoising Adaptive Graph Transformer, HandDAGT, for hand pose estimation.
HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud
Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications.
Continuous Memory Representation for Anomaly Detection
To tackle all of the above challenges, we propose CRAD, a novel anomaly detection method for representing normal features within a "continuous" memory, enabled by transforming spatial features into coordinates and mapping them to continuous grids.
Ranked #33 on
Anomaly Detection
on MVTec AD
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
In this work, we present mip-Grid, a novel approach that integrates anti-aliasing techniques into grid-based representations for radiance fields, mitigating the aliasing artifacts while enjoying fast training time.
Coordinate-Aware Modulation for Neural Fields
Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals.
Compact 3D Gaussian Representation for Radiance Field
On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality.
Ranked #9 on
Novel View Synthesis
on Tanks and Temples
Multi-Scale Bidirectional Recurrent Network with Hybrid Correlation for Point Cloud Based Scene Flow Estimation
Scene flow estimation provides the fundamental motion perception of a dynamic scene, which is of practical importance in many computer vision applications.
HandR2N2: Iterative 3D Hand Pose Estimation Using a Residual Recurrent Neural Network
3D hand pose estimation is a critical task in various human-computer interaction applications.
FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals.
Ranked #3 on
Video Reconstruction
on UVG
Masked Wavelet Representation for Compact Neural Radiance Fields
There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees.
Streamable Neural Fields
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations.
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation
Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks.
ADA-VAD: Unpaired Adversarial Domain Adaptation for Noise-Robust Voice Activity Detection
In this paper, we propose adversarial domain adaptive VAD (ADA-VAD), which is a deep neural network (DNN) based VAD method highly robust to audio samples with various noise types and low SNRs.
Ranked #4 on
Activity Detection
on AVA-Speech
(ROC-AUC metric)
NAS-VAD: Neural Architecture Search for Voice Activity Detection
Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD).
Neural Residual Flow Fields for Efficient Video Representations
Inspired by standard video compression algorithms, we propose a neural field architecture for representing and compressing videos that deliberately removes data redundancy through the use of motion information across video frames.
VW-SDK: Efficient Convolutional Weight Mapping Using Variable Windows for Processing-In-Memory Architectures
In this paper, we introduce a novel mapping algorithm called variable-window SDK (VW-SDK), which adaptively determines the shape of the parallel window that leads to the minimum computing cycles for a given convolutional layer and PIM array.
HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton
With increasing applications of 3D hand pose estimation in various human-computer interaction applications, convolution neural networks (CNNs) based estimation models have been actively explored.
Ranked #3 on
Hand Pose Estimation
on ICVL Hands
Dual Precision Deep Neural Network
On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference.
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity classification on UCF 101 dataset.
Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms
The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform.
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks.
