arXiv subjects

Xiaobo Lu

Publications and source records attributed to Xiaobo Lu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder

Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attention due to their compact representation and low computational overhead, making them suitable for onboard deployment and quantization. However, existing pillar-based detectors still suffer from information loss along height dimension and large numerical distribution difference during pillar feature encoding (PFE), which severely limits their performance and quantization potential. To address above issue, we first unveil the importance of different input information during PFE and identify the height dimension as a key factor in enhancing 3D detection performance. Motivated by this observation, we propose a height-aware pillar feature encoder, called PillarHist. Specifically, PillarHist statistics the discrete distribution of points at different heights within one pillar with the information entropy guidance. This simple yet effective design greatly preserves the information along the height dimension while significantly reducing the computation overhead of the PFE. Meanwhile, PillarHist also constrains the arithmetic distribution of PFE input to a stable range, making it quantization-friendly. Notably, PillarHist operates exclusively within the PFE stage to enhance performance, enabling seamless integration into existing pillar-based methods without introducing complex operations. Extensive experiments show the effectiveness of PillarHist in terms of both efficiency and performance.

cs.CV↗

arXiv2025

Unconventional Orbital Magnetism in Graphene-based Fractional Chern Insulators

Orbital magnetism in graphene originates from correlation-driven spontaneous valley symmetry breaking1-7. It can lead to various anomalous transport phenomena such as integer and fractional quantum anomalous Hall effects8-11. In general, the in-plane magnetic field B|| primarily couples to the spin degrees of freedom in graphene and has long been presumed to have a negligible effect on orbital magnetism due to the ultra-weak spin-orbit coupling12-18. In this work, we report multiple unconventional orbital magnetic phenomena that are highly sensitive to the B|| field in graphene/hBN superlattices hosting both integer and fractional Chern insulators (FCIs). We observed chirality-switching behaviors of the Chern insulator at moiré filling factor ν = 1 under a finite B_par, demonstrating that both the C = +-1 states are permissible ground states at zero perpendicular magnetic field B_per. For the FCI at ν = 2/3, we observed topological phase transitions between two states characterized by Hall resistivity \r{ho}xy = +-3h/2e2 under both B_per and B_par fields. In-plane B|| field can effectively suppress the FCI state at zero B_per field and enhance the FCI state with the opposite chirality, as resolved in Landau fan diagrams. Moreover, we observed rich phase transitions at 1 < ν < 2, accompanied by intervalley coherence and anomalous Hall effects (AHE) that can be triggered by sweeping either B_per or B_par. Our work has unveiled new properties of orbital magnetism, providing a new knob for engineering various AHE in graphene.

cond-mat.mes-hall↗

arXiv2025

Tunable Fractional Chern Insulators in Rhombohedral Graphene Superlattices

Fractional Chern insulators (FCIs) showing a transport effect with fractionally quantized Hall plateaus emerging under zero magnetic field, provide a radically new opportunity to engineer topological quantum electronics. By construction of topological flat band with moire engineering, intrinsic FCIs have been observed in twisted MoTe2 system and rhombohedral pentalayer graphene/hBN moire superlattices with anomalous Hall resistivity quantization number C <= 2/3 including the gapless composite Fermi-liquid state with C = 1/2. Here, we experimentally demonstrate a new system of rhombohedral hexalayer graphene (RHG)/hBN moire superlattices, which exhibit both integer and fractional quantum anomalous Hall effects with rich tunability including electric displacement field, perpendicular magnetic field and in-plane magnetic field. By tuning the electrical and magnetic fields at 0 < v < 1, we have observed a quantum phase transition showing a sign reversal of the Hall resistivity at finite magnetic fields. Surprisingly, the FCI state at v = 2/3 survives in the phase transitions, exhibiting a robust quantized Hall resistivity across both phases. Finally we have further demonstrated the indispensable role moire potential plays in the formation of the flat Chern band from a theoretical perspective. Our work has established RHG/hBN moire superlattices as a promising platform for exploring quasi-particles with fractional charge and non-Abelian anyons at zero magnetic field.

cond-mat.mes-hall↗

arXiv2025

Flat Band and Many-body Gap in Chirally Twisted Triple Bilayer Graphene

We experimentally investigate the band structures of chirally twisted triple bilayer graphene. The new kind of moiré structure, formed by three pieces of helically stacked Bernal bilayer graphene, has flat bands at charge neutral point based on the continuum approximation. We experimentally confirm the existence of flat bands and directly acquire the gap in-between flat bands as well as between the flat bands and dispersive bands from the capacitance measurements. We discover a finite gap even at zero perpendicular electric field, possibly induced by the Coulomb interaction and ferromagnetism. Our quantitative study not only provides solid evidence for the flat-band and interesting physics, but also introduces a quantitative approach to explore phenomena of similar moiré systems.

cond-mat.mes-hall↗

arXiv2024

Collective Pinning and Vortex Dynamics in type 2 superconducting thin films with Varying Magnetic Field

A perpendicular magnetic field penetrating a thin type-II superconductor slab produces vortices, with one vortex per flux quantum, h/2e. The vortices interact repulsively and form an ordered array (Abrikosov lattice) in clean systems, while strong disorder changes the lattice into a vortex glass. Here we investigate type-II superconducting films (PdBi2 and NbSe2) with surface acoustic waves (SAWs) at mK temperature. When sweeping the magnetic field at an extremely slow rate, we observe a series of spikes in the attenuation and velocity of the SAW, on average separated in field by approximately Hc1. We suspect the following scenario: The vortex-free region at the edges of the film produces an edge barrier across which the vortices can enter or leave. When the applied field changes, the induced supercurrents flowing along this edge region lowers this barrier until there is an instability. At that point, vortices avalanche into (or out of) the bulk and change the vortex crystal, suggested by the sharp jump in each such spike. The vortices then gradually relax to a new stable pinned configuration, leading to a ~30s relaxation after the jump. Our observation enriches the limited experimental evidence on the important topic of real-time vortex dynamics in superconductors.

cond-mat.supr-con↗

arXiv2024

Commensurate and Incommensurate Chern Insulators in Magic-angle Bilayer Graphene

The interplay between strong electron-electron interaction and symmetry breaking can have profound influence on the topological properties of materials. In magic angle twisted bilayer graphene (MATBG), the flat band with a single SU(4) flavor associated with the spin and valley degrees of freedom gains non-zero Chern number when C2z symmetry or C2zT symmetry is broken. Electron-electron interaction can further lift the SU(4) degeneracy, leading to the Chern insulator states. Here we report a complete sequence of zero-field Chern insulators at all odd integer fillings (v = +-1, +-3) with different chirality (C = 1 or -1) in hBN aligned MATBG which structurally breaks C2z symmetry. The Chern states at hole fillings (v = -1, -3), which are firstly observed in this work, host an opposite chirality compared with the electron filling scenario. By slightly doping the v = +-3 states, we have observed new correlated insulating states at incommensurate moiré fillings which is highly suggested to be intrinsic Wigner crystals according to our theoretical calculations. Remarkably, we have observed prominent Streda-formula violation around v = -3 state. By doping the Chern gap at v = -3 with notable number of electrons at finite magnetic field, the Hall resistance Ryx robustly quantizes to ~ h/e2 whereas longitudinal resistance Rxx vanishes, indicating that the chemical potential is pinned within a Chern gap, forming an incommensurate Chern insulator. By providing the first experimental observation of zero-field Chern insulators in the flat valence band, our work fills up the overall topological framework of MATBG with broken C2z symmetry. Our findings also demonstrate that doped topological flat band is an ideal platform to investigate exotic incommensurate correlated topological states.

cond-mat.mes-hall↗

arXiv2024

Rapid infrared imaging for rhombohedral graphene

The extrinsic stacking sequence based on intrinsic crystal symmetry in multilayer two-dimensional materials plays a significant role in determining their electronic and optical properties. Compared with Bernal-stacked (ABA) multilayer graphene, rhombohedral (ABC) multilayer graphene hosts stronger electron-electron interaction due to its unique dispersion at low-energy excitations and has been utiliazed as a unique platform to explore strongly correlated physics. However, discerning the stacking sequence has always been a quite time-consuming process by scanning mapping methods. Here, we report a rapid recognition method for ABC- stacked graphene with high accuracy by infrared imaging based on the distinct optical responses at infrared range. The optical contrast of the image between ABC and ABA stacked graphene is strikingly clear, and the discernibility is comparable to traditional optical Raman microscopy but with higher consistency and throughput. We further demonstrate that the infrared imaging technique can be integrated with dry transfer techniques commonly used in the community. This rapid and convenient infrared imaging technique will significantly improve the sorting efficiency for differently stacked multilayer graphene, thereby accelerating the exploration of the novel emergent correlated phenomena in ABC stacked graphene.

cond-mat.mes-hall↗

arXiv2024

Correlated Charge Density Wave Insulators in Chirally Twisted Triple Bilayer Graphene

Electrons residing in flat-band system can play a vital role in triggering spectacular phenomenology due to relatively large interactions and spontaneous breaking of different degeneracies. In this work we demonstrate chirally twisted triple bilayer graphene, a new moiré structure formed by three pieces of helically stacked Bernal bilayer graphene, as a highly tunable flat-band system. In addition to the correlated insulators showing at integer moiré fillings, commonly attributed to interaction induced symmetry broken isospin flavors in graphene, we observe abundant insulating states at half-integer moiré fillings, suggesting a longer-range interaction and the formation of charge density wave insulators which spontaneously break the moiré translation symmetry. With weak out-of-plane magnetic field applied, as observed half-integer filling states are enhanced and more quarter-integer filling states appear, pointing towards further quadrupling moiré unit cells. The insulating states at fractional fillings combined with Hartree-Fock calculations demonstrate the observation of a new type of correlated charge density wave insulators in graphene and points to a new accessible twist manner engineering correlated moiré electronics.

cond-mat.mes-hall↗

arXiv2024

LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection

Due to highly constrained computing power and memory, deploying 3D lidar-based detectors on edge devices equipped in autonomous vehicles and robots poses a crucial challenge. Being a convenient and straightforward model compression approach, Post-Training Quantization (PTQ) has been widely adopted in 2D vision tasks. However, applying it directly to 3D lidar-based tasks inevitably leads to performance degradation. As a remedy, we propose an effective PTQ method called LiDAR-PTQ, which is particularly curated for 3D lidar detection (both SPConv-based and SPConv-free). Our LiDAR-PTQ features three main components, \textbf{(1)} a sparsity-based calibration method to determine the initialization of quantization parameters, \textbf{(2)} a Task-guided Global Positive Loss (TGPL) to reduce the disparity between the final predictions before and after quantization, \textbf{(3)} an adaptive rounding-to-nearest operation to minimize the layerwise reconstruction error. Extensive experiments demonstrate that our LiDAR-PTQ can achieve state-of-the-art quantization performance when applied to CenterPoint (both Pillar-based and Voxel-based). To our knowledge, for the very first time in lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the same as the FP32 model while enjoying $3\times$ inference speedup. Moreover, our LiDAR-PTQ is cost-effective being $30\times$ faster than the quantization-aware training method. Code will be released at \url{https://github.com/StiphyJay/LiDAR-PTQ}.

cs.CV↗

arXiv2023

FastPillars: A Deployment-friendly Pillar-based 3D Detector

The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference, which puts a hard barrier for deployment, especially for on-device applications. In this paper, to tackle the challenge of efficient 3D object detection from an industry perspective, we devise a deployment-friendly pillar-based 3D detector, termed FastPillars. First, we introduce a novel lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for enhancing small 3D objects. Second, we propose a simple yet effective principle for designing a backbone in pillar-based 3D detection. We construct FastPillars based on these designs, achieving high performance and low latency without SPConv. Extensive experiments on two large-scale datasets demonstrate the effectiveness and efficiency of FastPillars for on-device 3D detection regarding both performance and speed. Specifically, FastPillars delivers state-of-the-art accuracy on Waymo Open Dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based). Our code is publicly available at: https://github.com/StiphyJay/FastPillars.

cs.CV↗

arXiv2023

Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently yields the best performance against state-of-the-art methods on fine-grained benchmark datasets.

cs.IR↗

arXiv2023

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting $ $ triplets, and serving as the foundation for numerous computer vision tasks. The complexity and diversity of human-object interactions in the real world, however, pose significant challenges for both annotation and recognition, particularly in recognizing interactions within an open world context. This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs). The proposed method is dubbed as \emph{\textbf{UniHOI}}. We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. Furthermore, we utilize a LLM (\emph{i.e.} GPT) for interaction interpretation, generating a richer linguistic understanding for complex HOIs. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence. Our efficient architecture design and learning methods effectively unleash the potential of the VL foundation models and LLMs, allowing UniHOI to surpass all existing methods with a substantial margin, under both supervised and zero-shot settings. The code and pre-trained weights are available at: \url{https://github.com/Caoyichao/UniHOI}.

cs.CV↗

arXiv2023

Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets. Despite the challenges posed by the numerous interaction combinations, they also offer opportunities for multimodal learning of visual texts. In this paper, we present a systematic and unified framework (RmLR) that enhances HOI detection by incorporating structured text knowledge. Firstly, we qualitatively and quantitatively analyze the loss of interaction information in the two-stage HOI detector and propose a re-mining strategy to generate more comprehensive visual representation.Secondly, we design more fine-grained sentence- and word-level alignment and knowledge transfer strategies to effectively address the many-to-many matching problem between multiple interactions and multiple texts.These strategies alleviate the matching confusion problem that arises when multiple interactions occur simultaneously, thereby improving the effectiveness of the alignment process. Finally, HOI reasoning by visual features augmented with textual knowledge substantially improves the understanding of interactions. Experimental results illustrate the effectiveness of our approach, where state-of-the-art performance is achieved on public benchmarks. We further analyze the effects of different components of our approach to provide insights into its efficacy.

cs.CV↗

arXiv2022

Multi-Meron Interactions and Statistics in Two-Dimensional Materials

As a fundamental type of topological spin textures in two-dimensional (2D) magnets, a magnetic meron carries half-integer topological charge and forms a pair with its antithesis to keep the stability in materials. However, it is challenging to quantitatively calculate merons and their dynamics by using the widely used continuum model because of the characteristic highly inhomogeneous spin textures. In this work, we develop a discrete method to address the concentrated spin structures around the core of merons. We reveal a logarithmic-scale interaction between merons when their distance is larger than twice their core size and obtain subsequent statistics of meron gas. The model also predicts how these properties of single and paired merons evolve with magnetic exchange interactions, and the results are in excellent agreement with the Monte Carlo simulations using the parameters of real 2D van der Waals magnetic materials. This discrete approach not only shows equilibrium static statistics of meron systems but also is useful to further explore the dynamic properties of merons through the quantified pairing interactions.

cond-mat.mtrl-sci↗

arXiv2022

Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8*) the occluded and tiny faces (16*16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network super-resolves and inpaints occluded LR faces in a coarse-to-fine manner, thus reducing unwanted blurriness and artifacts significantly. Specifically, we design a novel cross-modal transformer module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutual promotion manner. Extensive experiments demonstrate that our Pro-UIGAN achieves visually pleasing HR faces, reaching superior performance in downstream tasks, i.e., face alignment, face parsing, face recognition and expression classification, compared with other state-of-the-art (SotA) methods.

cs.CV↗

arXiv2022

Quantum critical behavior in magic-angle twisted bilayer graphene

The flat bands of magic-angle twisted bilayer graphene (MATBG) host strongly-correlated electronic phases such as correlated insulators, superconductors and a strange-metal state. The latter state, believed to be key for understanding the electronic properties of MATBG, is obscured by various phase transitions and thus could not be unequivocally differentiated from a metal undergoing frequent electron-phonon collisions. Here, we report transport measurements in superconducting MATBG in which the correlated insulator states are suppressed by screening. The uninterrupted metallic ground state shows resistivity that is linear in temperature over three decades and spans a broad range of doping including those where a correlation-driven Fermi surface reconstruction occurs. This strange-metal behavior is distinguished by Planckian scattering rates and a linear magnetoresistivity. In contrast, near charge neutrality or a fully-filled flat band, as well as for devices twisted away from the magic angle, we observe the archetypal Fermi liquid behavior. Our measurements demonstrate the existence of a quantum critical phase whose fluctuations dominate the metallic ground state throughout a continuum of doping. Further, we observe a transition to the strange metal upon suppression of the superconducting order, suggesting a relationship between quantum fluctuations and superconductivity in MATBG.

cond-mat.str-el↗

arXiv2022

Tunable quantum confinement of neutral excitons using electric fields and exciton-charge interactions

Quantum confinement is the discretization of energy when motion of particles is restricted to length scales smaller than their de Broglie wavelength. The experimental realization of this effect has had wide ranging impact in diverse fields of physics and facilitated the development of new technologies. In semiconductor physics, quantum confinement of optically excited quasiparticles, such as excitons or trions, is typically achieved by modulation of material properties - an approach crucially limited by the lack of insitu tunability and scalability of confining potentials. Achieving fully tunable quantum confinement of optical excitations has therefore been an outstanding goal in quantum photonics. Here, we demonstrate electrically controlled quantum confinement of neutral excitons in a gate-defined monolayer p-i-n diode. A combination of dc Stark shift induced by large in-plane fields and a previously unknown confining mechanism based on repulsive interaction between excitons and free charges ensures tight exciton confinement in the narrow neutral region. Quantization of exciton motion manifests in multiple discrete, spectrally narrow, voltage-dependent optical resonances that emerge below the free exciton resonance. Our measurements reveal several unique physical features of these quantum confined excitons, including an in-plane dipolar character, one-dimensional center-of-mass confinement, and strikingly enhanced exciton size in the presence of magnetic fields. Our method provides an experimental route towards creating scalable arrays of identical single photon sources, which will constitute building blocks of strongly correlated photonic systems.

cond-mat.mes-hall↗

arXiv2021

Experimental evidence of plasmarons and effective fine structure constant in electron-doped graphene/h-BN heterostructure

Electron-electron interaction is fundamental in condensed matter physics and can lead to composite quasiparticles called plasmarons, which strongly renormalize the dispersion and carry information of electron-electron coupling strength as defined by the effective fine structure constant $α_{ee}^*$. Although h-BN with unique dielectric properties has been widely used as an important substrate for graphene, so far there is no experimental report of plasmarons in graphene/h-BN yet. Here, we report direct experimental observation of plasmaron dispersion in graphene/h-BN heterostructures through angle-resolved photoemission spectroscopy (ARPES) measurements upon {\it in situ} electron doping. Characteristic diamond-shaped dispersion is observed near the Dirac cone in both 0$^\circ$ (aligned) and 13.5$^\circ$ (twisted) graphene/h-BN, and the electron-electron interaction strength $α_{ee}^*$ is extracted to be $α_{ee}^*\approx0.9\pm 0.1$, highlighting the important role of electron-electron interaction. Our results suggest graphene/h-BN as an ideal platform for investigating strong electron-electron interaction with weak dielectric screening, and lays fundamental physics for gate-tunable nano-electronics and nano-plasmonics.

cond-mat.mes-hall↗