arXiv subjects

Yang Zhou

Publications and source records attributed to Yang Zhou.

At least 361 records · Page 20Linked to original sources

arXiv2024

Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM

Reconstructing 3D humans from a single image has been extensively investigated. However, existing approaches often fall short on capturing fine geometry and appearance details, hallucinating occluded parts with plausible details, and achieving generalization across unseen and in-the-wild datasets. We present Human-LRM, a diffusion-guided feed-forward model that predicts the implicit field of a human from a single image. Leveraging the power of the state-of-the-art reconstruction model (i.e., LRM) and generative model (i.e Stable Diffusion), our method is able to capture human without any template prior, e.g., SMPL, and effectively enhance occluded parts with rich and realistic details. Our approach first uses a single-view LRM model with an enhanced geometry decoder to get the triplane NeRF representation. The novel view renderings from the triplane NeRF provide strong geometry and color prior, from which we generate photo-realistic details for the occluded parts using a diffusion model. The generated multiple views then enable reconstruction with high-quality geometry and appearance, leading to superior overall performance comparing to all existing human reconstruction methods.

cs.CV↗

arXiv2024

A Survey of Lottery Ticket Hypothesis

The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i.e., winning tickets) that can achieve even better performance than the original model when trained in isolation. While LTH has been proved both empirically and theoretically in many works, there still are some open issues, such as efficiency and scalability, to be addressed. Also, the lack of open-source frameworks and consensual experimental setting poses a challenge to future research on LTH. We, for the first time, examine previous research and studies on LTH from different perspectives. We also discuss issues in existing works and list potential directions for further exploration. This survey aims to provide an in-depth look at the state of LTH and develop a duly maintained platform to conduct experiments and compare with the most updated baselines.

cs.LG↗

arXiv2024

LRM: Large Reconstruction Model for Single Image to 3D

We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image. We train our model in an end-to-end manner on massive multi-view data containing around 1 million objects, including both synthetic renderings from Objaverse and real captures from MVImgNet. This combination of a high-capacity model and large-scale training data empowers our model to be highly generalizable and produce high-quality 3D reconstructions from various testing inputs, including real-world in-the-wild captures and images created by generative models. Video demos and interactable 3D meshes can be found on our LRM project webpage: https://yiconghong.me/LRM.

cs.CV↗

arXiv2024

95 GeV excess in a $CP$-violating $μ$-from-$ν$ SSM

The CMS and ATLAS have recently reported their results searching for light Higgs boson with mass around 95 GeV, based on the full Run 2 data set. In the framework of the CP-violating (CPV) $μν$SSM, we discuss a $\sim$ 2.9$σ$ (local) excess at 95 GeV in the light Higgs boson search in the diphoton decay mode as reported by ATLAS and CMS, together with a $\sim$ 2$σ$ excess (local) in the $b\bar{b}$ final state at LEP in the same mass range. By introducing CPV phases as well as by mixing CP-even Higgs and CP-odd Higgs, a lighter Higgs boson in the $μν$SSM can be produced, which can account for the "di-photon excess".

hep-ph↗

arXiv2024

Deformable One-shot Face Stylization via DINO Semantic Guidance

This paper addresses the complex issue of one-shot face stylization, focusing on the simultaneous consideration of appearance and structure, where previous methods have fallen short. We explore deformation-aware face stylization that diverges from traditional single-image style reference, opting for a real-style image pair instead. The cornerstone of our method is the utilization of a self-supervised vision transformer, specifically DINO-ViT, to establish a robust and consistent facial structure representation across both real and style domains. Our stylization process begins by adapting the StyleGAN generator to be deformation-aware through the integration of spatial transformers (STN). We then introduce two innovative constraints for generator fine-tuning under the guidance of DINO semantics: i) a directional deformation loss that regulates directional vectors in DINO space, and ii) a relative structural consistency constraint based on DINO token self-similarities, ensuring diverse generation. Additionally, style-mixing is employed to align the color generation with the reference, minimizing inconsistent correspondences. This framework delivers enhanced deformability for general one-shot face stylization, achieving notable efficiency with a fine-tuning duration of approximately 10 minutes. Extensive qualitative and quantitative comparisons demonstrate our superiority over state-of-the-art one-shot face stylization methods. Code is available at https://github.com/zichongc/DoesFS

cs.CV↗

arXiv2024

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO.

cs.LG↗

arXiv2024

Generating Non-Stationary Textures using Self-Rectification

This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel twostep approach wherein users first modify a reference texture using standard image editing tools, yielding an initial rough target for the synthesis. Subsequently, our proposed method, termed "self-rectification", automatically refines this target into a coherent, seamless texture, while faithfully preserving the distinct visual characteristics of the reference exemplar. Our method leverages a pre-trained diffusion network, and uses self-attention mechanisms, to gradually align the synthesized texture with the reference, ensuring the retention of the structures in the provided target. Through experimental validation, our approach exhibits exceptional proficiency in handling non-stationary textures, demonstrating significant advancements in texture synthesis when compared to existing state-of-the-art techniques. Code is available at https://github.com/xiaorongjun000/Self-Rectification

cs.CV↗

arXiv2024

Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow

While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopardize not only individual vehicles but also overall traffic safety and efficiency. While much research has focused on communication attacks, sensing attacks, which are equally critical, have garnered less attention. To address this gap, this study offers a comprehensive review of potential sensing attacks and their impact on target vehicles, focusing on commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic sensors, and GPS. Based on this review, we discuss the feasibility of integrating hardware-in-the-loop experiments with microscopic traffic simulations. We also design baseline scenarios to analyze the macro-level impact of sensing attacks on traffic flow. This study aims to bridge the research gap between individual vehicle sensing attacks and broader macroscopic impacts, thereby laying the foundation for future systemic understanding and mitigation.

eess.SY↗

arXiv2024

ActAnywhere: Subject-Aware Video Background Generation

Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io.

cs.CV↗

arXiv2024

Jump Cut Smoothing for Talking Heads

A jump cut offers an abrupt, sometimes unwanted change in the viewing experience. We present a novel framework for smoothing these jump cuts, in the context of talking head videos. We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks. To achieve motion, we interpolate the keypoints and landmarks between the end frames around the cut. We then use an image translation network from the keypoints and source frames, to synthesize pixels. Because keypoints can contain errors, we propose a cross-modal attention scheme to select and pick the most appropriate source amongst multiple options for each key point. By leveraging this mid-level representation, our method can achieve stronger results than a strong video interpolation baseline. We demonstrate our method on various jump cuts in the talking head videos, such as cutting filler words, pauses, and even random cuts. Our experiments show that we can achieve seamless transitions, even in the challenging cases where the talking head rotates or moves drastically in the jump cut.

cs.CV↗

arXiv2024

Entanglement Structure Detection via Computer Vision

Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, there still lacks a universal and effective way to detecting entanglement structures, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid CNN-Transformer model for both the classification of GHZ and W states and the detection of various entanglement structures. By leveraging the feature extraction capabilities of CNNs and the powerful modeling abilities of Transformers, we can not only effectively reduce the time and computational resources required for the training process but also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for independent detection of multipartite entanglement.

quant-ph↗

arXiv2023

Understanding Heterogeneity of Automated Vehicles and Its Traffic-level Impact: A Stochastic Behavioral Perspective

This paper develops a stochastic and unifying framework to examine variability in car-following (CF) dynamics of commercial automated vehicles (AVs) and its direct relation to traffic-level dynamics. The asymmetric behavior (AB) model by Chen at al. (2012a) is extended to accommodate a range of CF behaviors by AVs and compare with the baseline of human-driven vehicles (HDVs). The parameters of the extended AB (EAB) model are calibrated using an adaptive sequential Monte Carlo method for Approximate Bayesian Computation (ABC-ASMC) to stochastically capture various uncertainties including model mismatch resulting from unknown AV CF logic. The estimated posterior distributions of the parameters reveal significant differences in CF behavior (1) between AVs and HDVs, and (2) across AV developers, engine modes, and speed ranges, albeit to a lesser degree. The estimated behavioral patterns and simulation experiments further reveal mixed platoon dynamics in terms of traffic throughout reduction and hysteresis.

stat.AP↗

arXiv2023

High precision atom interferometer-based dynamic gravimeter measurement by eliminating the cross-coupling effect

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses AI sensors and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement are 0.42 mGal and 0.46 mGal, which were improved by factors of 4.18 and 4.21 by optimizing the cross-coupling effect.

physics.app-ph↗

arXiv2023

In-Hand 3D Object Reconstruction from a Monocular RGB Video

Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately capturing the shape within the hand-object contact region due to occlusion. In this paper, we propose a novel method that deals with surface reconstruction under occlusion by incorporating priors of 2D occlusion elucidation and physical contact constraints. For the former, we introduce an object amodal completion network to infer the 2D complete mask of objects under occlusion. To ensure the accuracy and view consistency of the predicted 2D amodal masks, we devise a joint optimization method for both amodal mask refinement and 3D reconstruction. For the latter, we impose penetration and attraction constraints on the local geometry in contact regions. We evaluate our approach on HO3D and HOD datasets and demonstrate that it outperforms the state-of-the-art methods in terms of reconstruction surface quality, with an improvement of $52\%$ on HO3D and $20\%$ on HOD. Project webpage: https://east-j.github.io/ihor.

cs.CV↗

arXiv2023

AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Federated Learning (FL) has achieved significant achievements recently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suffer from severe efficiency bottlenecks on the server. While enabling collaborative training without a central server, existing decentralized FL approaches either focus on the synchronous mechanism that deteriorates FL convergence or ignore device staleness with an asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments with three unique contributions. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation. Extensive experimentation on four public datasets and four models demonstrates the strength of AEDFL in terms of accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computation costs (up to 42.3% lower).

cs.DC↗

arXiv2023

FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).

cs.DC↗

arXiv2023

Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains

Despite the successful application of convolutional neural networks (CNNs) in object detection tasks, their efficiency in detecting faults from freight train images remains inadequate for implementation in real-world engineering scenarios. Existing modeling shortcomings of spatial invariance and pooling layers in conventional CNNs often ignore the neglect of crucial global information, resulting in error localization for fault objection tasks of freight trains. To solve these problems, we design a spatial-wise dynamic distillation framework based on multi-layer perceptron (MLP) for visual fault detection of freight trains. We initially present the axial shift strategy, which allows the MLP-like architecture to overcome the challenge of spatial invariance and effectively incorporate both local and global cues. We propose a dynamic distillation method without a pre-training teacher, including a dynamic teacher mechanism that can effectively eliminate the semantic discrepancy with the student model. Such an approach mines more abundant details from lower-level feature appearances and higher-level label semantics as the extra supervision signal, which utilizes efficient instance embedding to model the global spatial and semantic information. In addition, the proposed dynamic teacher can jointly train with students to further enhance the distillation efficiency. Extensive experiments executed on six typical fault datasets reveal that our approach outperforms the current state-of-the-art detectors and achieves the highest accuracy with real-time detection at a lower computational cost. The source code will be available at \url{https://github.com/MVME-HBUT/SDD-FTI-FDet}.

cs.CV↗

arXiv2023

DINO-Mix: Enhancing Visual Place Recognition with Foundational Vision Model and Feature Mixing

Utilizing visual place recognition (VPR) technology to ascertain the geographical location of publicly available images is a pressing issue for real-world VPR applications. Although most current VPR methods achieve favorable results under ideal conditions, their performance in complex environments, characterized by lighting variations, seasonal changes, and occlusions caused by moving objects, is generally unsatisfactory. In this study, we utilize the DINOv2 model as the backbone network for trimming and fine-tuning to extract robust image features. We propose a novel VPR architecture called DINO-Mix, which combines a foundational vision model with feature aggregation. This architecture relies on the powerful image feature extraction capabilities of foundational vision models. We employ an MLP-Mixer-based mix module to aggregate image features, resulting in globally robust and generalizable descriptors that enable high-precision VPR. We experimentally demonstrate that the proposed DINO-Mix architecture significantly outperforms current state-of-the-art (SOTA) methods. In test sets having lighting variations, seasonal changes, and occlusions (Tokyo24/7, Nordland, SF-XL-Testv1), our proposed DINO-Mix architecture achieved Top-1 accuracy rates of 91.75%, 80.18%, and 82%, respectively. Compared with SOTA methods, our architecture exhibited an average accuracy improvement of 5.14%.

cs.CV↗