arXiv subjects

Zonglin Li

Publications and source records attributed to Zonglin Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Shape-Guided Clothing Warping for Virtual Try-On

Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS) transformation or appearance flow to deform in-shop clothing for aligning with the person's body. Despite their promising performance, these methods often lack precise control over fine details, leading to inconsistencies in shape between clothing and the person's body as well as distortions in exposed limb regions. To tackle these challenges, we propose a novel shape-guided clothing warping method for virtual try-on, dubbed SCW-VTON, which incorporates global shape constraints and additional limb textures to enhance the realism and consistency of the warped clothing and try-on results. To integrate global shape constraints for clothing warping, we devise a dual-path clothing warping module comprising a shape path and a flow path. The former path captures the clothing shape aligned with the person's body, while the latter path leverages the mapping between the pre- and post-deformation of the clothing shape to guide the estimation of appearance flow. Furthermore, to alleviate distortions in limb regions of try-on results, we integrate detailed limb guidance by developing a limb reconstruction network based on masked image modeling. Through the utilization of SCW-VTON, we are able to generate try-on results with enhanced clothing shape consistency and precise control over details. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/SCW-VTON.

cs.CV↗

arXiv2025

Progressive Limb-Aware Virtual Try-On

Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/PL-VTON.

cs.CV↗

arXiv2025

Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints

In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitations, we introduce Path-Adaptive Matting (PAM), a framework that dynamically adjusts network paths based on image contexts and computational cost constraints. We formulate the training of the computational cost-constrained matting network as a bilevel optimization problem, jointly optimizing the matting network and the path estimator. Building on this formalization, we design a path-adaptive matting architecture by incorporating path selection layers and learnable connect layers to estimate optimal paths and perform efficient inference within a unified network. Furthermore, we propose a performance-aware path-learning strategy to generate path labels online by evaluating a few paths sampled from the prior distribution of optimal paths and network estimations, enabling robust and efficient online path learning. Experiments on five image matting datasets demonstrate that the proposed PAM framework achieves competitive performance across a range of computational cost constraints.

cs.CV↗

arXiv2025

Reducing disorder in PbTe nanowires for Majorana research

Material challenges are the key issue in Majorana research where surface disorder constrains device performance. Here, we tackle this challenge by embedding PbTe nanowires within a lattice-constant-matched crystal. The wire edges are shaped by self-organized growth instead of lithography, resulting in nearly-atomic-flat facets along both cross-sectional and longitudinal directions. Quantized conductance is observed at zero magnetic field with channel lengths maximally reaching 1.7 $μ$m, significantly surpassing the state-of-the-art of III-V nanowires (an order-of-magnitude improvement compared to InSb). Coupling PbTe to a Pb film unveils a flat interface spanning microns and a large superconducting gap of 1 meV. Our result not only represents a stride toward meeting the stringent low-disorder requirement for Majoranas, but may also open the door to various hybrid quantum devices requiring a low level of disorder.

cond-mat.mes-hall↗

arXiv2025

Anisotropy of PbTe nanowires with and without a superconductor

We investigate the anisotropic behaviors in PbTe and PbTe-Pb hybrid nanowires. In previous studies on PbTe, wire-to-wire variations in anisotropy indicate poor device control, posing a serious challenge for applications. Here, we achieve reproducible anisotropy in PbTe nanowires through a substantial reduction of disorder. We then couple PbTe to a superconductor Pb, and observe a pronounced deviation in the anisotropy behavior compared to bare PbTe nanowires. This deviation is gate-tunable and attributed to spin-orbit interaction and orbital effect, controlled by charge transfer between Pb and PbTe. These results provide a guidance for the controlled engineering of exotic quantum states in this hybrid material platform.

cond-mat.mes-hall↗

arXiv2024

Real-space study of zero-field correlation in tetralayer rhombohedral graphene

Rhombohedral graphene (RG) has emerged as a promising platform for exploring exotic quantum phenomena, such as quantum magnetism, unconventional superconductivity, and fractional quantum anomalous Hall effects. Despite its potential, atomic-scale investigations of RG remain limited, hindering a detailed microscopic understanding of the origins of these correlated states. In this study, we employ scanning probe microscopy and spectroscopy to probe the intrinsic electronic states in trilayer and tetralayer RG. We identify a correlated insulating state with a 17 meV gap at the charge neutrality point in tetralayer RG, which is absent in the trilayer configuration. This gap is suppressed by applying a perpendicular magnetic field or doping the charge carrier density and does not exhibit inter-valley coherence patterns. We attribute this phenomenon to a symmetry-broken layer antiferromagnetic state, characterized by ferrimagnetic ordering in the outermost layers and antiferromagnetic coupling between them. To further investigate this magnetic correlated state, we conduct local scattering experiments. Within the correlated regime, a bound state emerges around a non-magnetic impurity but is absent near magnetic impurities, suggesting that non-magnetic doping induces a spin texture in the ferrimagnetic surface layers. Outside the correlated regime, Friedel oscillations are observed, allowing precise determination of the band dispersion in tetralayer RG. These findings provide atomic-scale evidences of zero-field correlations in RG and may be extended to study other exotic phases in RG.

cond-mat.mes-hall↗

arXiv2024

Continuous Approximations for Improving Quantization Aware Training of LLMs

Model compression methods are used to reduce the computation and energy requirements for Large Language Models (LLMs). Quantization Aware Training (QAT), an effective model compression method, is proposed to reduce performance degradation after quantization. To further minimize this degradation, we introduce two continuous approximations to the QAT process on the rounding function, traditionally approximated by the Straight-Through Estimator (STE), and the clamping function. By applying both methods, the perplexity (PPL) on the WikiText-v2 dataset of the quantized model reaches 9.0815, outperforming 9.9621 by the baseline. Also, we achieve a 2.76% improvement on BoolQ, and a 5.47% improvement on MMLU, proving that the step sizes and weights can be learned more accurately with our approach. Our method achieves better performance with the same precision, model size, and training setup, contributing to the development of more energy-efficient LLMs technology that aligns with global sustainability goals.

cs.LG↗

arXiv2024

Quantized Andreev conductance in semiconductor nanowires

Clean one-dimensional electron systems can exhibit quantized conductance. The plateau conductance doubles if the transport is dominated by Andreev reflection. Here, we report quantized conductance observed in both Andreev and normal-state transports in PbTe-Pb and PbTe-In hybrid nanowires. The Andreev plateau is observed at $4e^2/h$, twice of the normal plateau value of $2e^2/h$. In comparison, Andreev conductance in the best-optimized III-V nanowires is non-quantized due to mode-mixing induced dips (a disorder effect), despite the quantization of normal-state transport. The negligible mode mixing in PbTe hybrids indicates an unprecedented low-disorder transport regime for nanowire devices, beneficial for Majorana researches.

cond-mat.mes-hall↗

arXiv2024

Revisiting Context Aggregation for Image Matting

Traditional studies emphasize the significance of context information in improving matting performance. Consequently, deep learning-based matting methods delve into designing pooling or affinity-based context aggregation modules to achieve superior results. However, these modules cannot well handle the context scale shift caused by the difference in image size during training and inference, resulting in matting performance degradation. In this paper, we revisit the context aggregation mechanisms of matting networks and find that a basic encoder-decoder network without any context aggregation modules can actually learn more universal context aggregation, thereby achieving higher matting performance compared to existing methods. Building on this insight, we present AEMatter, a matting network that is straightforward yet very effective. AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced axis-wise learning (AEAL) blocks to build a basic network with strong context aggregation learning capability. Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data. Extensive experiments on five popular matting datasets demonstrate that the proposed AEMatter outperforms state-of-the-art matting methods by a large margin.

cs.CV↗

arXiv2024

SQUID oscillations in PbTe nanowire networks

Network structures by semiconductor nanowires hold great promise for advanced quantum devices, especially for applications in topological quantum computing. In this study, we created networks of PbTe nanowires arranged in loop configurations. Using shadow-wall epitaxy, we defined superconducting quantum interference devices (SQUIDs) using the superconductor Pb. These SQUIDs exhibit oscillations in supercurrent upon the scanning of a magnetic field. Most of the oscillations can be fitted assuming a sinusoidal current-phase relation for each Josephson junction. Under certain conditions, the oscillations are found to be skewed, suggesting possible deviation from a sinusoidal behavior. Our results highlight the potential of PbTe nanowires for building complex quantum devices in the form of networks.

cond-mat.mes-hall↗

arXiv2024

Gate-tunable subband degeneracy in semiconductor nanowires

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, reflected as emergence of the plateau. This degeneracy and its tunable lifting were challenging to observe in previous nanowire experiments, possibly due to disorder. Numerical simulations can qualitatively capture our observation, shedding light on device parameters for future applications.

cond-mat.mes-hall↗

arXiv2024

Gate-Compatible Circuit Quantum Electrodynamics in a Three-Dimensional Cavity Architecture

Semiconductor-based superconducting qubits offer a versatile platform for studying hybrid quantum devices in circuit quantum electrodynamics (cQED) architecture. Most of these cQED experiments utilize coplanar waveguides, where the incorporation of DC gate lines is straightforward. Here, we present a technique for probing gate-tunable hybrid devices using a three-dimensional (3D) microwave cavity. A recess is machined inside the cavity wall for the placement of devices and gate lines. We validate this design using a hybrid device based on an InAs-Al nanowire Josephson junction. The coupling between the device and the cavity is facilitated by a long superconducting strip, the antenna. The Josephson junction and the antenna together form a gatemon qubit. We further demonstrate the gate-tunable cavity shift and two-tone qubit spectroscopy. This technique could be used to probe various quantum devices and materials in a 3D cQED architecture that requires DC gate voltages.

quant-ph↗

arXiv2023

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation. Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.

cs.CL↗

arXiv2023

In situ tuning of dynamical Coulomb blockade on Andreev bound states in hybrid nanowire devices

Electron interactions in quantum devices can exhibit intriguing phenomena. One example is assembling an electronic device in series with an on-chip resistor. The quantum laws of electricity of the device is modified at low energies and temperatures by dissipative interactions induced by the resistor, a phenomenon known as dynamical Coulomb blockade (DCB). The DCB strength is usually non-adjustable in a fixed environment defined by the resistor. Here, we design an on-chip circuit for InAs-Al hybrid nanowires where the DCB strength can be gate-tuned in situ. InAs-Al nanowires could host Andreev or Majorana zero-energy states. This technique enables tracking the evolution of the same state while tuning the DCB strength from weak to strong. We observe the transition from a zero-bias conductance peak to split peaks for Andreev zero-energy states. Our technique opens the door to in situ tuning interaction strength on zero-energy states.

cond-mat.mes-hall↗

arXiv2023

ResMem: Learn what you can and memorize the rest

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e.g. a neural network) by fitting the model's residuals with a $k$-nearest neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. Empirically, we show that ResMem consistently improves the test set generalization of the original prediction model across various standard vision and natural language processing benchmarks. Theoretically, we formulate a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over the base predictor.

cs.LG↗

arXiv2023

On the number variance of sequences with small additive energy

For a real-valued sequence $(x_n)_{n=1}^\infty$, denote by $S_N(\ell)$ the number of its first $N$ fractional parts lying in a random interval of size $\ell:=L/N$, where $L=o(N)$ as $N\to\infty$. We study the variance of $S_N(\ell)$ (the number variance) for sequences of the form $x_n=αa_n$, where $(a_n)_{n=1}^\infty$ is a sequence of distinct integers. We show that if the additive energy of the sequence $(a_n)_{n=1}^\infty$ is bounded from above by $N^{5/2-\varepsilon}/L$ for some $\varepsilon>0$, then for almost all $α$, the number variance is asymptotic to $L$ (Poissonian number variance). This holds in particular for the sequence $x_n=αn^d, d\ge 2$ whenever $L=N^β$ with $0\leβ<1/2$.

math.NT↗

arXiv2023

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers

This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by sparse we mean that on average very few entries (e.g., 3.0% for T5-Base and 6.3% for ViT-B16) are nonzero for each input to MLP. Moreover, larger Transformers with more layers and wider MLP hidden dimensions are sparser as measured by the percentage of nonzero entries. Through extensive experiments we demonstrate that the emergence of sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks, on both training and evaluation data, for Transformers of various configurations, at layers of all depth levels, as well as for other architectures including MLP-mixers and 2-layer MLPs. We show that sparsity also emerges using training datasets with random labels, or with random inputs, or with infinite amount of data, demonstrating that sparsity is not a result of a specific family of datasets. We discuss how sparsity immediately implies a way to significantly reduce the FLOP count and improve efficiency for Transformers. Moreover, we demonstrate perhaps surprisingly that enforcing an even sparser activation via Top-k thresholding with a small value of k brings a collection of desired but missing properties for Transformers, namely less sensitivity to noisy training data, more robustness to input corruptions, and better calibration for their prediction confidence.

cs.LG↗

arXiv2023

Geometry and distribution of roots of $μ^2\equiv D\bmod m$ with $D \equiv 1 \bmod 4$

We extend the geometric interpretation of the roots of the quadratic congruence $μ^2 \equiv D \pmod m$ as the tops of certain geodesics in the hyperbolic plane to the case when $D$ is square-free and $D\equiv 1\pmod 4$. This allows us to establish limit laws for the fine-scale statistics of the roots using results by Marklof and the second author.

math.NT↗