Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in augmentation alleviate this data-scarce problem. In contrast, paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) exploit the distribution support of unlabeled instances few-shot learning. Specifically, we first train linear classifier labeled examples...

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of constant SDS present variational score (VSD), principled particle-based framework explain address aforementioned issues generation. We show that is special case VSD leads poor samples...

Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these require an avalanche of expensive human labeled training data iterations train their large number parameters. This severely limits scalability the real-world long-tail distributed categories, some which are with a instances, but only few manually annotated. Learning from such extremely limited examples is known as Few-Shot (FSL). Different prior arts that leverage...

Noisy training set usually leads to the degradation of generalization and robustness neural networks. In this paper, we propose using a theoretically guaranteed noisy label detection framework detect remove data for Learning with Labels (LNL). Specifically, design penalized regression model linear relation between network features one-hot labels, where are identified by non-zero mean shift parameters solved in model. To make scalable datasets that contain large number categories data, split...

In this paper we present advanced representation learning study on integrating deep techniques and sparse approximation, including diffusion models, for flow field analysis reconstruction. Key applications include super-resolution reconstruction, inpainting, fluid-structure interaction, transient internal analyses, reduced-order modeling. The introduces two novel methods: diffusions tasks a sparsity-boosted low-rank model inpainting. By leveraging cutting-edge methodologies in computational...

Large brain imaging databases contain a wealth of information on organization in the populations they target, and individual variability. While such have been used to study group-level features directly, are currently underutilized as resource inform single-subject analysis. Here, we propose leveraging contained large functional magnetic resonance (fMRI) by establishing population priors employ an empirical Bayesian framework. We focus estimation networks source signals independent component...

Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose novel approach, called Coarse-to-Fine Segmentation (C2F-Seg), addresses problem by progressively modeling the amodal segmentation. C2F-Seg initially reduces learning space from pixel-level image to vector-quantized latent space. This enables us better handle long-range dependencies learn coarse-grained segment visual features segments. However,...

The probabilistic shaping (PS) technique can effectively reduce the average power of transmitted signal by transmitting high-energy symbols with low probability and low-energy high probability, which helps to improve tolerance system nonlinear effect fiber. In this work, a low-complexity intra-symbol bit-weighted distribution matching (Intra-SBWDM)-based PS 16-ary quadrature amplitude modulation (PS-16QAM) scheme is implemented commercial off-the-shelf field programmable gate array chip for...

We propose and experimentally demonstrate a low-complexity constant modulus algorithm (CMA) based on FPGA for 4-channel wavelength-division-multiplexing passive optical network (WDM-PON) employing 4-level pulse amplitude modulation (PAM4). The processes the 4 wavelength channels sequentially. In order to reduce logic resources required by parallel equalization algorithm, an optimization structure of mode (PCMA) was proposed in this paper. Different from classic all-parallel CMA, our PCMA...

We propose a low-complexity equalization scheme with non-uniform quantization and rotational update mechanism (RUM). The is verified in implementation of DDLMS for 92-Gbaud 10-km offline 14.7456-Gbaud 25-km FPGA-based real time PA M4 IM/DD experimental transmission, results show up to 99.5% multiplications (DSP resource usage) are reduced comparing normal equalizer . A large number equivalent taps can be achieved based on several active taps.

We experimentally demonstrated a low-complexity probabilistic shaping (PS) 64QAM-OFDM in an IM-DD-based W-band RoF System. After 45-km SSMF and 4-m wireless transmission, the experimental results show that its receiver sensitivity is better than of uniformly distributed-OFDM (UD-OFDM).

A noisy training set usually leads to the degradation of generalization and robustness neural networks. In this article, we propose a novel theoretically guaranteed clean sample selection framework for learning with labels. Specifically, first present Scalable Penalized Regression (SPR) method, model linear relation between network features one-hot SPR, data are identified by zero mean-shift parameters solved in regression model. We show that SPR can recover under some conditions. Under...

The authors propose a thinned knowledge‐aided space–time adaptive processing (STAP) scheme based on structural prior information of the clutter covariance matrix (CCM). Due to low‐rank Toeplitz‐ block ‐Toeplitz structure CCM, CCM can be expressed by series basis matrices ridge. In contrast expression Vandermonde decomposition this avoid searching subspace. This also allows reducing dimension STAP and estimating with compressed data. Based expression, derive closed‐form estimate using...

In this paper, we propose an embarrassingly simple approach for one-shot learning. Our insight is that the tasks have domain gap to network pretrained and thus some features from are not relevant, or harmful specific task. Therefore, directly prune a task rather than update it via optimized scheme with complex structure. Without bells whistles, our yet effective method achieves leading performances on miniImageNet (60.63%) tieredImageNet (69.02%) 5-way setting. The best trial can hit 66.83%...

Encouraged by the growing availability of pre-trained 2D diffusion models, image-to-3D generation leveraging Score Distillation Sampling (SDS) is making remarkable progress. Most existing methods combine novel-view lifting from models which usually take reference image as a condition while applying hard L2 supervision at view. Yet heavily adhering to prone corrupting inductive knowledge model leading flat or distorted 3D frequently. In this work, we reexamine in novel perspective and present...

In recent years, there has been strong interest in neuroscience studies to investigate brain organization through networks of regions that demonstrate functional connectivity (FC). Several well-known have consistently identified both task-related and resting-state magnetic resonance imaging (rs-fMRI) across different study populations. These are extracted from observed fMRI using data-driven analytic methods such as independent component analysis. A notable limitation these FC is they do not...

Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in augmentation alleviate this data-scarce problem. In contrast, paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) exploit the distribution support of unlabeled instances few-shot learning. Specifically, we first train linear classifier labeled examples...

The maximum space-time degrees of freedom (DOF) is restricted to the number antennas and pulses. In this paper, a novel sparse space time adaptive processing (STAP) scheme proposed based on concept minimum redundancy arrays.We arrange array geometry temporal sampler interval make location joint samples satisfies interval. According structural information imposed clutter covariance matrix (CCM), original CCM estimated by series basis accurately, higher dimension can be reconstructed while...

Lip detection is the major and important step in lip-reading system. In this paper, a novel method proposed. Firstly, since combination of YCbCr HSV color model has good segmentation power on skin color, we used scheme to avoid interference background original images. Then, use Hough transform locate eyeballs which are close circular after face detected. third step, new approach proposed extract lips based transformation model. More specifically, width lip area obtained by distribution...

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges analyzing matrices, including high dimensionality of networks, unknown latent sources underlying observed connectivity, and large number connections leading to spurious findings. this paper we propose a...