arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 811 records · Page 45Linked to original sources

arXiv2022

Active Learning for Deep Visual Tracking

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this paper, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNNs model. Under the guidance of active learning, the tracker based on the trained deep CNNs model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multi-frame collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest neighbor discrimination method based on the average nearest neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks.

cs.CV↗

arXiv2022

Computational discovery of spin-polarized semimetals in spinel materials

The materials with spin-polarized electronic states have attracted a huge amount of interest due to their potential applications in spintronics. Based on first-principles calculations, we study the electronic characteristics of a series of AB2X4 chalcogeniden spinel structures and propose two promising candidates, VZn2O4 and VCd2S4, are spin-polarized semimetal materials. Both of them have ferromagnetic ground states. Their bands near the Fermi level are completely spin-polarized and form two types of nodal rings in the spin-up channel, and the large gaps in the spin-down channel prevent the spin-flip. Further symmetry analysis reveals that the nodal rings are protected by the glide mirror or mirror symmetries. Significantly, these nodal rings connect with each other and form a nodal chain structure, which can be well described by a simple four-band tight-binding (TB) model. The two ternary chalcogeniden spinel materials with a fully spin polarized nodal chain can serve as a prominent platform in the future applications of spintronic.

cond-mat.mtrl-sci↗

arXiv2022

The Type Icn SN 2021csp: Implications for the Origins of the Fastest Supernovae and the Fates of Wolf-Rayet Stars

We present observations of SN 2021csp, the second example of a newly-identified type of supernova (Type Icn) hallmarked by strong, narrow, P Cygni carbon features at early times. The SN appears as a fast and luminous blue transient at early times, reaching a peak absolute magnitude of -20 within 3 days due to strong interaction between fast SN ejecta (v ~ 30000 km/s) and a massive, dense, fast-moving C/O wind shed by the WC-like progenitor months before explosion. The narrow line features disappear from the spectrum 10-20 days after explosion and are replaced by a blue continuum dominated by broad Fe features, reminiscent of Type Ibn and IIn supernovae and indicative of weaker interaction with more extended H/He-poor material. The transient then abruptly fades ~60 days post-explosion when interaction ceases. Deep limits at later phases suggest minimal heavy-element nucleosynthesis, a low ejecta mass, or both, and imply an origin distinct from that of classical Type Ic supernovae. We place SN 2021csp in context with other fast-evolving interacting transients, and discuss various progenitor scenarios: an ultrastripped progenitor star, a pulsational pair-instability eruption, or a jet-driven fallback supernova from a Wolf-Rayet star. The fallback scenario would naturally explain the similarity between these events and radio-loud fast transients, and suggests a picture in which most stars massive enough to undergo a WR phase collapse directly to black holes at the end of their lives.

astro-ph.HE↗

arXiv2022

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.

eess.IV↗

arXiv2021

Doubly Robust Adaptive LASSO for Effect Modifier Discovery

Effect modification occurs when the effect of the treatment on an outcome differs according to the level of a third variable (the effect modifier, EM). A natural way to assess effect modification is by subgroup analysis or include the interaction terms between the treatment and the covariates in an outcome regression. The latter, however, does not target a parameter of a marginal structural model (MSM) unless a correctly specified outcome model is specified. Our aim is to develop a data-adaptive method to select effect modifying variables in an MSM with a single time point exposure. A two-stage procedure is proposed. First, we estimate the conditional outcome expectation and propensity score and plug these into a doubly robust loss function. Second, we use the adaptive LASSO to select the EMs and estimate MSM coefficients. Post-selection inference is then used to obtain coverage on the selected EMs. Simulations studies are performed in order to verify the performance of the proposed methods.

stat.ME↗

arXiv2021

Omni-supervised Facial Expression Recognition via Distilled Data

Facial expression plays an important role in understanding human emotions. Most recently, deep learning based methods have shown promising for facial expression recognition. However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training. To solve this issue, prior works employ the pretrain-and-finetune strategy, i.e., utilize a large amount of unlabeled data to pretrain the network and then finetune it by the labeled data. As the labeled data is in a small amount, the final network performance is still restricted. From a different perspective, we propose to perform omni-supervised learning to directly exploit reliable samples in a large amount of unlabeled data for network training. Particularly, a new dataset is firstly constructed using a primitive model trained on a small number of labeled samples to select samples with high confidence scores from a face dataset, i.e., MS-Celeb-1M, based on feature-wise similarity. We experimentally verify that the new dataset created in such an omni-supervised manner can significantly improve the generalization ability of the learned FER model. However, as the number of training samples grows, computational cost and training time increase dramatically. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images, significantly improving the training efficiency. We have conducted extensive experiments on widely used benchmarks, where consistent performance gains can be achieved under various settings using the proposed framework. More importantly, the distilled dataset has shown its capabilities of boosting the performance of FER with negligible additional computational costs.

cs.CV↗

arXiv2021

D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D$^2$LV, unsupervised pre-training substitutes the commonly-used supervised one. When training, we design a set of basic and six advanced transformations, and a simple but effective baseline learns robust representation. During testing, a global-local and local-global matching strategy is proposed. The strategy performs local-verification between reference and query images. Experiments demonstrate that the proposed method is effective. The proposed approach ranks first out of 1,103 participants on the Facebook AI Image Similarity Challenge: Matching Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track1-Submission.

cs.CV↗

arXiv2021

Bag of Tricks and A Strong baseline for Image Copy Detection

Image copy detection is of great importance in real-life social media. In this paper, a bag of tricks and a strong baseline are proposed for image copy detection. Unsupervised pre-training substitutes the commonly-used supervised one. Beyond that, we design a descriptor stretching strategy to stabilize the scores of different queries. Experiments demonstrate that the proposed method is effective. The proposed baseline ranks third out of 526 participants on the Facebook AI Image Similarity Challenge: Descriptor Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track2-Submission.

cs.CV↗

arXiv2021

SemGloVe: Semantic Co-occurrences for GloVe from BERT

GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local context window, which might lead to limited word pairs and potentially semantic irrelevant word pairs. In this paper, we propose SemGloVe, which distills semantic co-occurrences from BERT into static GloVe word embeddings. Particularly, we propose two models to extract co-occurrence statistics based on either the masked language model or the multi-head attention weights of BERT. Our methods can extract word pairs without limiting by the local window assumption and can define the co-occurrence weights by directly considering the semantic distance between word pairs. Experiments on several word similarity datasets and four external tasks show that SemGloVe can outperform GloVe.

cs.CL↗

arXiv2021

An imaging polarimetry survey of Type Ia supernovae: are peculiar extinction and polarization properties produced by circumstellar or interstellar matter?

Some highly reddened Type Ia supernovae (SNe Ia) display low total-to-selective extinction ratios ($R_V \lesssim 2$) in comparison to that of typical Milky Way dust ($R_V \approx 3.3$), and polarization curves that rise steeply to blue wavelengths, with peak polarization values at short wavelengths ($λ_{\rm max} < 0.4$ $μ$m) in comparison to the typical Galactic values ($λ_{\rm max} \approx 0.55$ $μ$m). Understanding the source of these properties could provide insight into the progenitor systems of SNe Ia. We aim to determine whether they are the result of the host galaxy's interstellar dust or circumstellar dust. This is accomplished by analysing the continuum polarization of 66 SNe Ia in dust-rich spiral galaxies and 13 SNe Ia in dust-poor elliptical galaxies as a function of normalised galactocentric distance. We find that there is a general trend of SNe Ia in spiral galaxies displaying increased polarization values when located closer to the host galaxies' centre, while SNe Ia in elliptical host galaxies display low polarization. Furthermore, all highly polarized SNe Ia in spiral host galaxies display polarization curves rising toward blue wavelengths, while no evidence of such polarization properties is shown in elliptical host galaxies. This indicates that the source of the peculiar polarization curves is likely the result of interstellar material as opposed to circumstellar material. The peculiar polarization and extinction properties observed toward some SNe Ia may be explained by the radiative torque disruption mechanism induced by the SN or the interstellar radiation field.

astro-ph.GA↗

arXiv2021

Ringing of a black hole in a dark matter halo

Recently, we obtained the simple metrics of a spherically symmetric black hole in a dark matter halo, and extended to the case of rotation. As the characteristic sound of black holes, quasinormal modes (QNMs) are one of the important means to understand black holes currently. Based on these two metrics of a spherically symmetric black hole, we study the QNMs of cold dark matter (CDM) and scalar field dark matter (SFDM) models using the methods of the material field perturbations and the gravitational perturbation, and make comparisons with the Schwarzschild black hole. Our results show that black hole QNMs of CDM and SFDM in a dark matter halo are different from the Schwarzschild black hole, unlike a Schwarzschild black hole with a prominent power-law tail. The different kinds models of dark matter can be distinguished by their QNMs. The time of QNMs ringing and frequencies increase with increasing parameter $l$. The overall QNMs of CDM are stronger than that of SFDM in the same condition, which is easier to be detected. In addition, QNMs frequencies using the sixth-order WKB method and the Prony method are in good agreement.

gr-qc↗

arXiv2021

Residues of bosonic string scattering amplitudes and the Lauricella functions

We calculate explicitly residues of all n-point Koba-Nielsen (KN) amplitudes by using on-shell recursion relation of string scattering amplitudes (SSA). In addition, we show that the residues of all SSA including the KN amplitudes can be expressed in terms of the Lauricella functions. This result demonstrates the exact SL(K+3,C) symmetry of the tree-level open bosonic string theory. Moreover, we derive an iteration relation among the residues of a given SSA. This iteration relation is related to the SL(K+3,C) symmetry and can presumably be used to soften the well-known hard SSA.

hep-th↗

arXiv2021

Associating Objects with Transformers for Video Object Segmentation

This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios. The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources. To solve the problem, we propose an Associating Objects with Transformers (AOT) approach to match and decode multiple objects uniformly. In detail, AOT employs an identification mechanism to associate multiple targets into the same high-dimensional embedding space. Thus, we can simultaneously process multiple objects' matching and segmentation decoding as efficiently as processing a single object. For sufficiently modeling multi-object association, a Long Short-Term Transformer is designed for constructing hierarchical matching and propagation. We conduct extensive experiments on both multi-object and single-object benchmarks to examine AOT variant networks with different complexities. Particularly, our R50-AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (84.1% J&F), DAVIS 2017 (84.9%), and DAVIS 2016 (91.1%), while keeping more than $3\times$ faster multi-object run-time. Meanwhile, our AOT-T can maintain real-time multi-object speed on the above benchmarks. Based on AOT, we ranked 1st in the 3rd Large-scale VOS Challenge.

cs.CV↗

arXiv2021

Echoes of novel black-bounce spacetimes

Gravitational wave echoes can be used as a significant observable to understand the properties of black holes horizon. In addition, echoes would also closely relate to the unique properties of compact objects. In this work we study the evolution of electromagnetic field and scalar field under the background of novel black-bounce spacetimes. Our results show an obvious echoes signal that can characterize the properties of novel black-bounce spacetimes, and a detailed analysis about the characteristics of the echoes signal is given. By studying the quasinormal ringdown of the three states of novel black-bounce spacetimes, including black holes in $0 2 M$, we find that the echoes signal only appears when $a>2M$ in this spacetime, but when the parameter $a$ increases to a threshold, the echoes signal will be transformed into a quasinormal ringdown of the two-way traversable wormhole.

gr-qc↗

arXiv2021

Spectropolarimetry of the Type Ia SN 2019ein rules out significant global asphericity of the ejecta

Detailed spectropolarimetric studies may hold the key to probing the explosion mechanisms and the progenitor scenarios of Type Ia supernovae (SNe Ia). We present multi-epoch spectropolarimetry and imaging polarimetry of SN 2019ein, an SN Ia showing high expansion velocities at early phases. The spectropolarimetry sequence spans from $\sim -11$ to $+$10 days relative to peak brightness in the $B$-band. We find that the level of the continuum polarization of SN 2019ein, after subtracting estimated interstellar polarization, is in the range $0.0-0.3\%$, typical for SNe Ia. The polarization position angle remains roughly constant before and after the SN light-curve peak, implying that the inner regions share the same axisymmetry as the outer layers. We observe high polarization ($\sim 1\%$) across both the Si II $\lambda6355$ and Ca II near-infrared triplet features. These two lines also display complex polarization modulations. The spectropolarimetric properties of SN 2019ein rule out a significant departure from spherical symmetry of the ejecta for up to a month after the explosion. These observations disfavour merger-induced and double-detonation models for SN 2019ein. The imaging polarimetry shows weak evidence for a modest increase in polarization after $\sim 20$ days since the $B$-band maximum. If this rise is real and is observed in other SNe Ia at similar phases, we may have seen, for the first time, an aspherical interior similar to what has been previously observed for SNe IIP. Future polarization observations of SNe Ia extending to post-peak epochs will help to examine the inner structure of the explosion.

astro-ph.HE↗

arXiv2021

A general framework for scintillation in nanophotonics

Bombardment of materials by high-energy particles (e.g., electrons, nuclei, X- and $γ$-ray photons) often leads to light emission, known generally as scintillation. Scintillation is ubiquitous and enjoys widespread applications in many areas such as medical imaging, X-ray non-destructive inspection, night vision, electron microscopy, and high-energy particle detectors. A large body of research focuses on finding new materials optimized for brighter, faster, and more controlled scintillation. Here, we develop a fundamentally different approach based on integrating nanophotonic structures into scintillators to enhance their emission. To start, we develop a unified and ab initio theory of nanophotonic scintillators that accounts for the key aspects of scintillation: the energy loss by high-energy particles, as well as the light emission by non-equilibrium electrons in arbitrary nanostructured optical systems. This theoretical framework allows us, for the first time, to experimentally demonstrate nearly an order-of-magnitude enhancement of scintillation, in both electron-induced, and X-ray-induced scintillation. Our theory also allows the discovery of structures that could eventually achieve several orders-of-magnitude scintillation enhancement. The framework and results shown here should enable the development of a new class of brighter, faster, and higher-resolution scintillators with tailored and optimized performances - with many potential applications where scintillators are used.

physics.optics↗

arXiv2021

Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks

In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts. In this paper, we introduce Sub-bit Neural Networks (SNNs), a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are inspired by an empirical observation, showing that binary kernels learnt at convolutional layers of a BNN model are likely to be distributed over kernel subsets. As a result, unlike existing methods that binarize weights one by one, SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Specifically, our method includes a random sampling step generating layer-specific subsets of the kernel space, and a refinement step learning to adjust these subsets of binary kernels via optimization. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs. For instance, on ImageNet, SNNs of ResNet-18/ResNet-34 with 0.56-bit weights achieve 3.13/3.33 times runtime speed-up and 1.8 times compression over conventional BNNs with moderate drops in recognition accuracy. Promising results are also obtained when applying SNNs to binarize both weights and activations. Our code is available at https://github.com/yikaiw/SNN.

cs.CV↗

arXiv2021

Dynamic Slimmable Denoising Network

Recently, tremendous human-designed and automatically searched neural networks have been applied to image denoising. However, previous works intend to handle all noisy images in a pre-defined static network architecture, which inevitably leads to high computational complexity for good denoising quality. Here, we present dynamic slimmable denoising network (DDS-Net), a general method to achieve good denoising quality with less computational complexity, via dynamically adjusting the channel configurations of networks at test time with respect to different noisy images. Our DDS-Net is empowered with the ability of dynamic inference by a dynamic gate, which can predictively adjust the channel configuration of networks with negligible extra computation cost. To ensure the performance of each candidate sub-network and the fairness of the dynamic gate, we propose a three-stage optimization scheme. In the first stage, we train a weight-shared slimmable super network. In the second stage, we evaluate the trained slimmable super network in an iterative way and progressively tailor the channel numbers of each layer with minimal denoising quality drop. By a single pass, we can obtain several sub-networks with good performance under different channel configurations. In the last stage, we identify easy and hard samples in an online way and train a dynamic gate to predictively select the corresponding sub-network with respect to different noisy images. Extensive experiments demonstrate our DDS-Net consistently outperforms the state-of-the-art individually trained static denoising networks.

cs.CV↗