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Found 189 papers, 74 papers with code
OPPO’s Machine Translation Systems for WMT20
In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs.
Epona: Autoregressive Diffusion World Model for Autonomous Driving
In this work, we propose Epona, an autoregressive diffusion world model that enables localized spatiotemporal distribution modeling through two key innovations: 1) Decoupled spatiotemporal factorization that separates temporal dynamics modeling from fine-grained future world generation, and 2) Modular trajectory and video prediction that seamlessly integrate motion planning with visual modeling in an end-to-end framework.
ScEdit: Script-based Assessment of Knowledge Editing
Knowledge Editing (KE) has gained increasing attention, yet current KE tasks remain relatively simple.
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
Recent advances in autonomous driving research towards motion planners that are robust, safe, and adaptive.
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks
Unmanned Aerial Vehicles (UAVs) pose an escalating security concerns as the market for consumer and military UAVs grows.
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification
Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats.
On the Input-Output Monotonicity of Voltage Dynamics of Power System with Grid-Forming Converters
Integration of renewable resources is profoundly reshaping the dynamics of modern power systems.
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory
In this work, we propose \textbf{CityNavAgent}, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN.
AccidentSim: Generating Physically Realistic Vehicle Collision Videos from Real-World Accident Reports
In this paper, we introduce AccidentSim, a novel framework that generates physically realistic vehicle collision videos by extracting and utilizing the physical clues and contextual information available in real-world vehicle accident reports.
LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images
Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial for prevention.
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models
Traditional Reinforcement Learning (RL) suffers from replicating human-like behaviors, generalizing effectively in multi-agent scenarios, and overcoming inherent interpretability issues. These tasks are compounded when deep environment understanding, agent coordination and dynamic optimization are required.
OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models
The model fully leverages Mamba-2's high computational and memory efficiency, extending its capabilities from text generation to multimodal generation.
Customized SAM 2 for Referring Remote Sensing Image Segmentation
Although Segment Anything Model 2 (SAM 2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and generating effective prompts from text descriptions.
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
Some studies integrate vision-language models (VLMs) into autonomous driving, but they typically rely on pre-trained models with simple supervised fine-tuning (SFT) on driving data, without further exploration of training strategies or optimizations specifically tailored for planning.
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates.
Ranked #8 on
NavSim
on OpenScene
LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework
Recent advancements in event-based recognition have demonstrated significant promise, yet most existing approaches rely on extensive training, limiting their adaptability for efficient processing of event-driven visual content.
Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation
We propose an seeding strategy to carve Mamba from trained Transformer and a three-stage distillation recipe, which can effectively transfer the knowledge from Transformer to Mamba while preserving multimodal capabilities.
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning
By leveraging 3DGS techniques, we construct a photorealistic digital replica of the real physical world, enabling the AD policy to extensively explore the state space and learn to handle out-of-distribution scenarios through large-scale trial and error.
MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims to segment objects in a video according to textual descriptions, which requires the integration of multimodal information and temporal dynamics perception.
Referring Expression Segmentation
Referring Video Object Segmentation
+3
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes.
Enhancing Retrosynthesis with Conformer: A Template-Free Method
Retrosynthesis plays a crucial role in the fields of organic synthesis and drug development, where the goal is to identify suitable reactants that can yield a target product molecule.
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
We formulate reconstruction as a mapping from incomplete observed data to full reconstructed fields.
DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
However, prior works tend to produce unsatisfactory results, as the classic GPT framework is designed to handle 1D contextual information, such as text, and lacks the inherent ability to model the spatial and temporal dynamics essential for video generation.
3D Registration in 30 Years: A Survey
3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc.
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
Accordingly, their effectiveness is significantly influenced by the precision of the separated clean set, prior knowledge of noise, and the robustness of SSL.
Ranked #1 on
Learning with noisy labels
on CIFAR-10N-Random3
ShiftedBronzes: Benchmarking and Analysis of Domain Fine-Grained Classification in Open-World Settings
In real-world applications across specialized domains, addressing complex out-of-distribution (OOD) challenges is a common and significant concern.
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models
Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements.
Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration
Monocular camera calibration is essential for many 3D vision tasks.
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
However, the numerous denoising steps in the robotic diffusion policy and the more dynamic, open-world nature of traffic scenes pose substantial challenges for generating diverse driving actions at a real-time speed.
Ranked #13 on
NavSim
on OpenScene
Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning
Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations.
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
In contrast, Large Vision-Language Models (LVLMs) excel in scene understanding and reasoning.
DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model
The ability of this world model to capture the evolution of the environment is crucial for planning in autonomous driving.
HE-Drive: Human-Like End-to-End Driving with Vision Language Models
In this paper, we propose HE-Drive: the first human-like-centric end-to-end autonomous driving system to generate trajectories that are both temporally consistent and comfortable.
Generalizable Prompt Tuning for Vision-Language Models
Moreover, to generate more expressive prompts, the study introduces a class-wise augmentation from the visual modality, resulting in significant robustness to a wider range of unseen classes.
OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity
In this paper, we introduce OccRWKV, an efficient semantic occupancy network inspired by Receptance Weighted Key Value (RWKV).
Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration
Existing All-in-One image restoration methods often fail to perceive degradation types and severity levels simultaneously, overlooking the importance of fine-grained quality perception.
Accelerating Chance-constrained SCED via Scenario Compression
This paper studies some compression methods to accelerate the scenario-based chance-constrained security-constrained economic dispatch (SCED) problem.
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users
However, a limitation of these methods is that the mapping function is trained on overlapping users across domains, while only a small number of overlapping users are available for training.
VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling
However, the original VAR model is constrained to class-conditioned synthesis, relying solely on textual captions for guidance.
The Transmission Value of Energy Storage and Fundamental Limitations
This study addresses the transmission value of energy storage in electric grids.
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification.
Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking
This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
Occupancy as Set of Points
Owing to the inherent flexibility of the point-based representation, OSP achieves strong performance compared with existing methods and excels in terms of training and inference adaptability.
The Role of Electric Grid Research in Addressing Climate Change
Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders.
ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention
Recently, linear complexity sequence modeling networks have achieved modeling capabilities similar to Vision Transformers on a variety of computer vision tasks, while using fewer FLOPs and less memory.
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations.
Cross-to-merge training with class balance strategy for learning with noisy labels
Although sample selection is a mainstream method in the field of learning with noisy labels, which aims to mitigate the impact of noisy labels during model training, the testing performance of these methods exhibits significant fluctuations across different noise rates and types.
Ranked #3 on
Learning with noisy labels
on Clothing1M
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning
Learning robust and scalable visual representations from massive multi-view video data remains a challenge in computer vision and autonomous driving.
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging.
Ranked #27 on
NavSim
on OpenScene
LSRFormer: Efficient Transformer Supply Convolutional Neural Networks with Global Information for Aerial Image Segmentation
Convolutional Neural Networks (CNNs) can capture local context information well but cannot model the global dependencies.
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to be the next-generation backbone for vision foundation models.
Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain
However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations.
Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery
A set of rewards measuring the localization accuracy, the accuracy of predicted labels, and the scale consistency among nearby patches are designed in the agent to guide the scale optimization.
An empirical study of next-basket recommendations
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
A Gronwall Inequality Based Approach to Transient Stability Assessment for Power Grids
This paper proposes a novel Gronwall inequality-based method for transient stability assessment for power systems.
Affective Video Content Analysis: Decade Review and New Perspectives
In this study, we comprehensively review the development of AVCA over the past decade, particularly focusing on the most advanced methods adopted to address the three major challenges of video feature extraction, expression subjectivity, and multimodal feature fusion.
Circuit as Set of Points
In our work, we propose a novel perspective for circuit design by treating circuit components as point clouds and using Transformer-based point cloud perception methods to extract features from the circuit.
Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
The latter requires more efficiency, thus the pre-calculated LUT (look-up table) has become a popular solution.
FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware.
A Vision-Centric Approach for Static Map Element Annotation
The recent development of online static map element (a. k. a.
Techno-Economic Analysis of Synthetic Fuel Production from Existing Nuclear Power Plants across the United States
Low carbon synfuel can displace transport fossil fuels such as diesel and jet fuel and help achieve the decarbonization of the transportation sector at a global scale, but large-scale cost-effective production facilities are needed.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs).
Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation
Furthermore, the frequency awareness of the model is reinforced by encouraging the model to distinguish frequency signals.
DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images
The local attention path (LAP) uses efficient stripe convolution to generate local attention, which can alleviate the loss of information caused by down-sampling operation in the GAP and supplement the spatial details.
Extracting Buildings In Remote Sensing Images
Semantic Segmentation
Conditional Perceptual Quality Preserving Image Compression
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information.
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
Universal Rates for Multiclass Learning
Pseudo-cubes are a structure, rooted in the work of Daniely and Shalev-Shwartz (2014), and recently shown by Brukhim, Carmon, Dinur, Moran, and Yehudayoff (2022) to characterize PAC learnability (i. e., uniform rates) for multiclass classification.
ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration
We then leverage degradation-aware visual prompts to establish a controllable and universal model for image restoration, called ProRes, which is applicable to an extensive range of image restoration tasks.
Augmenting Greybox Fuzzing with Generative AI
Large language models (LLM) pre-trained with an enormous amount of natural language corpus have proved to be effective for understanding the implicit format syntax and generating format-conforming inputs.
SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction
Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model.
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abstract map elements with a variety of geometric patterns as unified point sequence representation, which can be extended to most map elements in the driving scene.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation
Fortunately, we have identified two observations that help us achieve the best of both worlds: 1) query-based methods demonstrate superiority over dense proposal-based methods in open-world instance segmentation, and 2) learning localization cues is sufficient for open world instance segmentation.
Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph
The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history.
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Ranked #29 on
Bench2Drive
on Bench2Drive
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning.
Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan
Conclusions: The proposed deep learning algorithms can be an effective tool for early identification of hemorrhage etiologies based on NCCT scans.
Deep Learning Approach to Predict Hemorrhage in Moyamoya Disease
Objective: Reliable tools to predict moyamoya disease (MMD) patients at risk for hemorrhage could have significant value.
FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization
To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption.
BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation
Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance.
Ranked #6 on
Bird's-Eye View Semantic Segmentation
on nuScenes
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles.
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
Non-reversible Parallel Tempering for Deep Posterior Approximation
Notably, in big data scenarios, we obtain an appealing communication cost $O(P\log P)$ based on the optimal window size.
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs).
Multi-Camera Calibration Free BEV Representation for 3D Object Detection
In advanced paradigms of autonomous driving, learning Bird's Eye View (BEV) representation from surrounding views is crucial for multi-task framework.
Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment
We collected a new clinical IMV dataset with GMA annotations, and our experiments show that SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the CRNN-based GMA prediction performance.
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes.
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer
In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration.
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system.
Ranked #9 on
3D Lane Detection
on OpenLane-V2 val
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
MixSKD mutually distills feature maps and probability distributions between the random pair of original images and their mixup images in a meaningful way.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Polar Parametrization for Vision-based Surround-View 3D Detection
Based on Polar Parametrization, we propose a surround-view 3D DEtection TRansformer, named PolarDETR.
Featurized Query R-CNN
The query mechanism introduced in the DETR method is changing the paradigm of object detection and recently there are many query-based methods have obtained strong object detection performance.
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
GKT leverages the geometric priors to guide the transformer to focus on discriminative regions and unfolds kernel features to generate BEV representation.
Functional Linear Regression of Cumulative Distribution Functions
In particular, given $n$ samples with $d$ basis functions, we show estimation error upper bounds of $\widetilde O(\sqrt{d/n})$ for fixed design, random design, and adversarial context cases.
Contrastive Siamese Network for Semi-supervised Speech Recognition
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition.
Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey
Graph Neural Networks (GNNs) are new inference methods developed in recent years and are attracting growing attention due to their effectiveness and flexibility in solving inference and learning problems over graph-structured data.
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling
Privacy protection is a crucial issue in machine learning algorithms, and the current privacy protection is combined with traditional artificial neural networks based on real values.
Spiking Neural Operators for Scientific Machine Learning
We demonstrate this new approach for classification using the SNN in the branch, achieving results comparable to the literature.
Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation
The invariance of illumination or inherent difference between two images is fully explored so as to make up for the lack of labels for nighttime images.
Learning Dynamic View Synthesis With Few RGBD Cameras
Experiments on the HTI dataset show that our method outperforms the baseline per-frame image fidelity and spatial-temporal consistency.
Cross-Image Relational Knowledge Distillation for Semantic Segmentation
Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples.
Illumination-Invariant Active Camera Relocalization for Fine-Grained Change Detection in the Wild
Active camera relocalization (ACR) is a new problem in computer vision that significantly reduces the false alarm caused by image distortions due to camera pose misalignment in fine-grained change detection (FGCD).
Atomic Filter: a Weak Form of Shift Operator for Graph Signals
In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals.
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation
Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy.
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception
For segmentation, we integrate AziNorm into KPConv.
Sparse Instance Activation for Real-Time Instance Segmentation
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation.
Ranked #7 on
Real-time Instance Segmentation
on MSCOCO
An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multi-phase Image Segmentation
In this paper, we propose an active contour model with a local variance force (LVF) term that can be applied to multi-phase image segmentation problems.
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks
Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs.
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information
The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities.
Learning Quality-aware Representation for Multi-person Pose Regression
Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i. e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates.
AdaptivePose: Human Parts as Adaptive Points
Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency.
Road-aware Monocular Structure from Motion and Homography Estimation
The Depth-CNN and Pose-CNN estimate dense depth map and ego-motion respectively, solving SFM, while the Pose-CNN and Ground-CNN followed by a homography layer solve the ground plane estimation problem.
On Convergence of Federated Averaging Langevin Dynamics
We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i. i. d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence.
Spatial-context-aware deep neural network for multi-class image classification
Multi-label image classification is a fundamental but challenging task in computer vision.
Monocular Road Planar Parallax Estimation
It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames.
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules
Our Spiking CapsNet fully combines the strengthens of SNN and CapsNet, and shows strong robustness to noise and affine transformation.
Aspect-driven User Preference and News Representation Learning for News Recommendation
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning
Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions.
Adversarial Relighting Against Face Recognition
To this end, we first propose the physical modelbased adversarial relighting attack (ARA) denoted as albedoquotient-based adversarial relighting attack (AQ-ARA).
ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization
Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm.
Hierarchical Aggregation for 3D Instance Segmentation
Instance segmentation on point clouds is a fundamental task in 3D scene perception.
Ranked #4 on
3D Instance Segmentation
on S3DIS
(mCov metric, using extra
training data)
Pseudo Facial Generation With Extreme Poses for Face Recognition
Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances.
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness.
Hyperspectral Image Denoising with Log-Based Robust PCA
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs).
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network
Instead, exploiting multi-view information is a practical way to achieve absolute 3D human pose estimation.
Monocular 3D Human Pose Estimation
Weakly-supervised 3D Human Pose Estimation
+1
THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
Reducing Streaming ASR Model Delay with Self Alignment
From LibriSpeech evaluation, self alignment outperformed existing schemes: 25% and 56% less delay compared to FastEmit and constrained alignment at the similar word error rate.
Deep Online Correction for Monocular Visual Odometry
Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases.
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective
The outputs from the teacher network are used as soft labels for supervising the training of a new network.
Ranked #36 on
Knowledge Distillation
on ImageNet
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification
By studying two unsupervised person re-ID methods in a cross-method way, we point out a hard negative problem is handled implicitly by their designs of data augmentations and PK sampler respectively.
Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos
Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds.
Time Series Analysis
Video Forensics
Cryptography and Security
Stacked Homography Transformations for Multi-View Pedestrian Detection
This task is confronted with two challenges: how to establish the 3D correspondences from views to the BEV map and how to assemble occupancy information across views.
Ranked #2 on
Multiview Detection
on CVCS
(MODA (1m) metric)
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective
In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels.
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention.
Matrix optimization based Euclidean embedding with outliers
Euclidean embedding from noisy observations containing outlier errors is an important and challenging problem in statistics and machine learning.
ResizeMix: Mixing Data with Preserved Object Information and True Labels
Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.
Causality-Aware Neighborhood Methods for Recommender Systems
In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations.
DataVault: A Data Storage Infrastructure for the Einstein Toolkit
Data sharing is essential in the numerical simulations research.
General Relativity and Quantum Cosmology Databases
Predicting seasonal influenza using supermarket retail records
In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.
On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration.
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data.
Transformer Transducer: One Model Unifying Streaming and Non-streaming Speech Recognition
In this paper we present a Transformer-Transducer model architecture and a training technique to unify streaming and non-streaming speech recognition models into one model.
Deep Momentum Uncertainty Hashing
It embodies the uncertainty of the hashing network to the corresponding input image.
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
In the classification tree, as the number of parent class nodes are significantly less, their logits are less noisy and can be utilized to suppress the wrong/noisy logits existed in the fine-grained class nodes.
Ranked #5 on
Few-Shot Object Detection
on LVIS v1.0 val
SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
In this paper, we propose a semi-supervised body parsing model, termed SiamParseNet (SPN), to jointly learn single frame body parsing and label propagation between frames in a semi-supervised fashion.
OPPO's Machine Translation System for the IWSLT 2020 Open Domain Translation Task
In this paper, we demonstrate our machine translation system applied for the Chinese-Japanese bidirectional translation task (aka.
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
Then for the novel task, we prove that the minimization of the $\ell_1$-regularized log-determinant Bregman divergence with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log(|S_{\text{off}}|))$ where $|S_{\text{off}}|$ is the number of off-diagonal elements in the support union and is much less than $N$ for sparse matrices.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.
Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation
Finally, the proposed hybrid loss in a four hierarchy-pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries.
Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection
Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition.
Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss
We present results on the LibriSpeech dataset showing that limiting the left context for self-attention in the Transformer layers makes decoding computationally tractable for streaming, with only a slight degradation in accuracy.
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.
CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description
As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task.
FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.
Ranked #1 on
Semantic Segmentation
on BDD
Neural Architecture Search
Real-Time Semantic Segmentation
+1
AugFPN: Improving Multi-scale Feature Learning for Object Detection
In this paper, we begin by first analyzing the design defects of feature pyramid in FPN, and then introduce a new feature pyramid architecture named AugFPN to address these problems.
Learning Where to Focus for Efficient Video Object Detection
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur.
Ranked #25 on
Video Object Detection
on ImageNet VID
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
Ranked #3 on
Face Verification
on CFP-FP
Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs
In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs.
Improved Mix-up with KL-Entropy for Learning From Noisy Labels
On the websites, there exist a lot of image data which contains inaccurate annotations, but training on these datasets may make networks easier to over-fit the noisy labels and cause performance degradation.
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression and achieved many promising results.
VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
In this paper, we propose a novel network design mechanism for efficient embedded computing.
Ranked #5 on
Face Verification
on CFP-FP
Densely Connected Search Space for More Flexible Neural Architecture Search
We revisit the search space design in most previous NAS methods and find the number and widths of blocks are set manually.
Ranked #91 on
Neural Architecture Search
on ImageNet
FrameRank: A Text Processing Approach to Video Summarization
In constructing the dataset, because of the subjectivity of user-generated video summarization, we manually annotate 25 summaries for each video, which are in total 1300 summaries.
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Skull stripping for brain MR images is a basic segmentation task.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
High Fidelity Face Manipulation with Extreme Poses and Expressions
Furthermore, due to the lack of high-resolution face manipulation databases to verify the effectiveness of our method, we collect a new high-quality Multi-View Face (MVF-HQ) database.
Progressive Sparse Local Attention for Video object detection
Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features.
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network
With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data.
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
In our implementations, architectures are first searched on a small dataset, e. g., CIFAR-10.
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists.
Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts
To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI).
Networking and Internet Architecture
Joint Neural Architecture Search and Quantization
Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices.
In-the-wild Facial Expression Recognition in Extreme Poses
In the computer research area, facial expression recognition is a hot research problem.
Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification
We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation.
Reinforced Evolutionary Neural Architecture Search
To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE- NAS), which is an evolutionary method with the reinforced mutation for NAS.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.
Quality Classified Image Analysis with Application to Face Detection and Recognition
We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.
Automatic Visual Theme Discovery from Joint Image and Text Corpora
Furthermore, not all tags have the same descriptive power for visual contents and large vocabulary available from natural language could result in a very diverse set of keywords.
6D Dynamic Camera Relocalization From Single Reference Image
Based on inexpensive platform with unreliable absolute repositioning accuracy (ARA), we propose a hand-eye calibration free strategy to actively relocate camera into the same 6D pose that produces the input reference image, by sequentially correcting 3D relative rotation and translation.
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder
Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications.
Topical differences between Chinese language Twitter and Sina Weibo
Using a large corpus of Weibo and Chinese language tweets, covering the period from January $1$ to December $31$, $2012$, we obtain a list of topics using clustered \#tags that we can then use to compare the two platforms.
Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations
To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions.
Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine
To simplify the number of times of optimization in experimental works, here, we use artificial neural network (ANN) and support vector machine (SVM) models for the prediction of yields of 3\b{eta}-O-phthalic ester of betulinic acid synthesized by betulinic acid and phthalic anhydride using lipase as biocatalyst.
