Search Results for author:
Found 21 papers, 14 papers with code
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection
Nevertheless, the naive application of VLMs leads to sub-optimal quality, due to the misalignment between embeddings of object images and their visual attributes, which are mainly adjective phrases.
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning
However, existing performance analyses ignores the unique characteristics of continuous-time control problems, is unable to directly estimate the generalization error of the Bellman optimal loss and require a boundedness assumption.
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning
Recent studies show that, without any prior model, the unsupervised restoration learning problem can be optimally formulated as an optimal transport (OT) problem, which has shown promising performance on denoising tasks to approach the performance of supervised methods.
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors.
Optimally Controllable Perceptual Lossy Compression
We prove that arbitrary points of the D-P tradeoff bound can be achieved by a simple linear interpolation between the outputs of a minimum MSE decoder and a specifically constructed perfect perceptual decoder.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
Masks Fusion with Multi-Target Learning For Speech Enhancement
Typically, a single T-F mask is first estimated based on DNN and then used to mask the spectrogram of noisy speech in an order to suppress the noise.
Optimal Transport for Unsupervised Denoising Learning
Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
This paper provides nontrivial results theoretically revealing that, \textit{1}) the cost of achieving perfect perception quality is exactly a doubling of the lowest achievable MSE distortion, \textit{2}) an optimal encoder for the "classic" rate-distortion problem is also optimal for the perceptual compression problem, \textit{3}) distortion loss is unnecessary for training a perceptual decoder.
Scalable Deep Compressive Sensing
In this paper, we develop a general framework named scalable deep compressive sensing (SDCS) for the scalable sampling and reconstruction (SSR) of all existing end-to-end-trained models.
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation
We prove that CAT is a rotation and translation-invariant transformation based on the theoretical analysis.
Revisiting Robust Model Fitting Using Truncated Loss
Furthermore, the new algorithms are applied to various 2D/3D registration problems.
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization
In this paper, we present a HSI restoration method named smooth and robust low rank tensor recovery.
AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods.
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient Algorithm
Besides the convergence to a stationary point for a generalized nonconvex penalty, we provide more deep analysis on a popular and important class of nonconvex penalties which have discontinuous thresholding functions.
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.
Efficient Outlier Removal in Large Scale Global Structure-from-Motion
This work addresses the outlier removal problem in large-scale global structure-from-motion.
The normalized Laplacian spectra of subdivision vertex-edge neighbourhood vertex(edge)-corona for graphs
Whereafter, the normalized Laplacian spectra of $G_1^S\bowtie (G_2^V\cup G_3^E)$ and $G_1^S\diamondsuit(G_2^V\cup G_3^E)$ are respectively determined in terms of the corresponding normalized Laplacian spectra of the connected regular graphs $G_{1}$, $G_{2}$ and $G_{3}$, which extend the corresponding results of [A. Das, P. Panigrahi, Linear Multil.
Combinatorics
Efficient Nonlinear Precoding for Massive MU-MIMO Downlink Systems with 1-Bit DACs
The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS).
Signal Processing Optimization and Control
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties.
