Search Results for author:
Found 129 papers, 39 papers with code
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO
However, despite the importance of active perception in embodied intelligence, there is little to no exploration of how MLLMs can be equipped with or learn active perception capabilities.
Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time
For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using various rendering engines to complete the rendering process.
Automated Text-to-Table for Reasoning-Intensive Table QA: Pipeline Design and Benchmarking Insights
Reasoning with tabular data holds increasing importance in modern applications, yet comprehensive evaluation methodologies for reasoning-intensive Table Question Answering (QA) tasks remain nascent.
GMatch: Geometry-Constrained Feature Matching for RGB-D Object Pose Estimation
We present GMatch, a learning-free feature matcher designed for robust 6DoF object pose estimation, addressing common local ambiguities in sparse feature matching.
FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization
To overcome this challenge, we propose a novel federated segmentation framework that strikes class consistency, termed FedSaaS.
JAEGER: Dual-Level Humanoid Whole-Body Controller
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy.
From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval
In the second stage, we optimize the text encoder using a small amount of synthetic triplet data, enabling it to effectively extract compositional semantics by combining pseudo-word tokens with modification text for accurate target image retrieval.
Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints
Constrained optimization with multiple functional inequality constraints has significant applications in machine learning.
Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
Low-quality data often suffer from insufficient image details, introducing an extra implicit aspect of camouflage that complicates camouflaged object detection (COD).
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey
Learning-based 3D reconstruction has emerged as a transformative technique in autonomous driving, enabling precise modeling of both dynamic and static environments through advanced neural representations.
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories
While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications.
Versatile Multimodal Controls for Expressive Talking Human Animation
AI-generated content faces similar requirements, where users not only need automatic generation of lip synchronization and basic gestures from audio input but also desire semantically accurate and expressive body movement that can be ``directly guided'' through text descriptions.
M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance
We present M2-omni, a cutting-edge, open-source omni-MLLM that achieves competitive performance to GPT-4o.
A Weight Adaptation Trigger Mechanism in Decomposition-based Evolutionary Multi-Objective Optimisation
ATM-MOEA/D uses an archive to gradually approximate the shape of the Pareto front during the search.
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection
Safety and reliability are crucial for the public acceptance of autonomous driving.
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks.
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling
Open-world interpretation aims to accurately localize and recognize all objects within images by vision-language models (VLMs).
Reversing Flow for Image Restoration
Image restoration aims to recover high-quality (HQ) images from degraded low-quality (LQ) ones by reversing the effects of degradation.
Referencing Where to Focus: Improving VisualGrounding with Referential Query
It consists of the query adaption module that can be seamlessly integrated into CLIP and generate the referential query to provide the prior context for decoder, along with a task-specific decoder.
Cross-View Image Set Geo-Localization
Therefore, we propose a novel task: Cross-View Image Set Geo-Localization (Set-CVGL), which gathers multiple images with diverse perspectives as a query set for localization.
GraphicsDreamer: Image to 3D Generation with Physical Consistency
Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures.
Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings
To address this limitation, we introduce DReSS (Decentrality Related Street-view and Satellite-view dataset), a novel dataset designed to evaluate cross-view geo-localization with a large geographic scope and diverse landscapes, emphasizing the decentrality issue.
STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
This model uses a hypergraph structure to represent spatial features among wind farms.
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation
We then present a new I2V model, named MotionStone, developed with the decoupled motion estimator.
Mimir: Improving Video Diffusion Models for Precise Text Understanding
Text serves as the key control signal in video generation due to its narrative nature.
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
In this paper, we propose a cross-modal visual relocalization system in prior LiDAR maps utilizing intensity textures, which consists of three main modules: map projection, coarse retrieval, and fine relocalization.
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis
Despite significant progress in talking head synthesis since the introduction of Neural Radiance Fields (NeRF), visual artifacts and high training costs persist as major obstacles to large-scale commercial adoption.
Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head Synthesis
Recent advances in diffusion models have endowed talking head synthesis with subtle expressions and vivid head movements, but have also led to slow inference speed and insufficient control over generated results.
Panther: Illuminate the Sight of Multimodal LLMs with Instruction-Guided Visual Prompts
Multimodal large language models (MLLMs) are closing the gap to human visual perception capability rapidly, while, still lag behind on attending to subtle images details or locating small objects precisely, etc.
DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding
This suggests the possibility of adopting dynamic encoding to balance detailed video information preservation with token budget reduction.
Try-On-Adapter: A Simple and Flexible Try-On Paradigm
A key challenge of try-on is to generate realistic images of the model wearing the garments while preserving the details of the garments.
HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation
To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework.
LumiSculpt: A Consistency Lighting Control Network for Video Generation
Lighting plays a pivotal role in ensuring the naturalness of video generation, significantly influencing the aesthetic quality of the generated content.
Multi-head Sequence Tagging Model for Grammatical Error Correction
For this reason, the attention has been shifted to the non-autoregressive or sequence tagging models.
Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replay
P2Value comprehensively considers the possibility of transformers' output and pass rate and can make use of the redundant resources caused by the problem that most programs collected by LLMs fail to pass any tests.
Animate-X: Universal Character Image Animation with Enhanced Motion Representation
Our in-depth analysis suggests to attribute this limitation to their insufficient modeling of motion, which is unable to comprehend the movement pattern of the driving video, thus imposing a pose sequence rigidly onto the target character.
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems.
HOTVCOM: Generating Buzzworthy Comments for Videos
In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose.
StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models
To tackle these challenges, we introduce StyleTokenizer, a zero-shot style control image generation method that aligns style representation with text representation using a style tokenizer.
Social Debiasing for Fair Multi-modal LLMs
Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities.
Egocentric Vision Language Planning
LMMs excel in planning long-horizon tasks over symbolic abstractions but struggle with grounding in the physical world, often failing to accurately identify object positions in images.
ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
Real-vehicle experiments further validate the feasibility and effectiveness of the method proposed in this paper.
POA: Pre-training Once for Models of All Sizes
Once pre-trained, POA allows the extraction of pre-trained models of diverse sizes for downstream tasks.
Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
This paper introduces Chain-of-Sight, a vision-language bridge module that accelerates the pre-training of Multimodal Large Language Models (MLLMs).
MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps
Robust localization is the cornerstone of autonomous driving, especially in challenging urban environments where GPS signals suffer from multipath errors.
BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight
In this paper, we propose BLOS-BEV, a novel BEV segmentation model that incorporates SD maps for accurate beyond line-of-sight perception, up to 200m.
Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data.
Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention
To address this issue, we propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains, and new education systems with limited data are considered target domains.
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification
In HFRP, we fuse the channel features and the spatial features.
Multi-level Reliable Guidance for Unpaired Multi-view Clustering
In cross-view learning, reliable view guidance enhances the confidence of the cluster structures in other views.
Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval
In this paper, we study the problem of zero-shot sketch-based image retrieval (ZS-SBIR).
Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction
This approach solves the key problem of large-scale reconstruction, that is where the data comes from and how to use them.
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
In this paper, we propose a novel semi-supervised active domain adaptation (SS-ADA) framework for semantic segmentation that employs an image-level acquisition strategy.
Monocular Localization with Semantics Map for Autonomous Vehicles
To balance efficiency and accuracy, we propose a novel lightweight visual semantic localization algorithm that employs stable semantic features instead of low-level texture features.
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
In this paper, we explore a problem setting called Open-world Prompt Tuning (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new classes.
HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios.
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science.
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection
This increased inference time has hindered the widespread employment of multispectral pedestrian detection in embedded devices for autonomous systems.
Unpaired Multi-view Clustering via Reliable View Guidance
We assume that the view with a good cluster structure is the reliable view, which acts as a supervisor to guide the clustering of the other views.
SHE-Net: Syntax-Hierarchy-Enhanced Text-Video Retrieval
In particular, text-video retrieval, which aims to find the top matching videos given text descriptions from a vast video corpus, is an essential function, the primary challenge of which is to bridge the modality gap.
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
Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model
Beyond the superiority of the text-to-image diffusion model in generating high-quality images, recent studies have attempted to uncover its potential for adapting the learned semantic knowledge to visual perception tasks.
One-Step Multi-View Clustering Based on Transition Probability
Despite their successes, current methods lack interpretability in the clustering process and do not sufficiently consider the complementary information across different views.
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
Anchor-free Clustering based on Anchor Graph Factorization
Additionally, we evolve the concept of the membership matrix between cluster centers and samples in FKM into an anchor graph encompassing multiple anchor points and samples.
A Survey for Foundation Models in Autonomous Driving
The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD).
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
We then summarize this empirical study into the M2-RAAP recipe, where our technical contributions lie in 1) the data filtering and text re-writing pipeline resulting in 1M high-quality bilingual video-text pairs, 2) the replacement of video inputs with key-frames to accelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy to enhance video features.
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ImageNet
(using extra training data)
Zero-Shot Cross-Modal Retrieval
Zero-shot Image Retrieval
+3
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment
To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels.
Evolutionary Alternating Direction Method of Multipliers for Constrained Multi-Objective Optimization with Unknown Constraints
Constrained multi-objective optimization problems (CMOPs) pervade real-world applications in science, engineering, and design.
Towards Better Vision-Inspired Vision-Language Models
In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models.
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
Ranked #1 on
Zero-shot Classification (unified classes)
on AID
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
It involves Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels.
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems.
Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs
Specifically, we present a new method for constructing the instruction tuning dataset at a low cost by leveraging annotations in existing datasets.
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs.
Ranked #4 on
Video Retrieval
on MSR-VTT-1kA
EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints
Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing.
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC.
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains
Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks.
Vehicle Sequencing at Signal-Free Intersections: Analytical Performance Guarantees Based on PDMP Formulation
Signal-free intersections are a representative application of smart and connected vehicle technologies.
Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition
In this paper, we propose a novel model called Spatial Attention and Syntax Rule Enhanced Tree Decoder (SS-TD), which is equipped with spatial attention mechanism to alleviate the prediction error of tree structure and use syntax masks (obtained from the transformation of syntax rules) to constrain the occurrence of ungrammatical mathematical expression.
High-level semantic feature matters few-shot unsupervised domain adaptation
However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain.
Efficient Generalization Improvement Guided by Random Weight Perturbation
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability.
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data
Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server.
Tensor Robust PCA with Nonconvex and Nonlocal Regularization
To this end, we integrate nonlocal self-similarity into N-TRPCA, and further develop a nonconvex and nonlocal TRPCA (NN-TRPCA) model.
Double-Ended Palindromic Trees: A Linear-Time Data Structure and Its Applications
eertree) is a linear-size data structure that provides access to all palindromic substrings of a string.
SUNet: Scale-aware Unified Network for Panoptic Segmentation
Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for intelligent vehicles.
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation
In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain, which has a mild gradient neutralization mechanism and mitigates the problem that hard samples are barely optimized in entropy-based methods.
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection
Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection.
Self-supervised Contrastive Attributed Graph Clustering
Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels.
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
The recent trend of using large-scale deep neural networks (DNN) to boost performance has propelled the development of the parallel pipelining technique for efficient DNN training, which has resulted in the development of several prominent pipelines such as GPipe, PipeDream, and PipeDream-2BW.
Recall and Learn: A Memory-augmented Solver for Math Word Problems
Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module.
Ranked #8 on
Math Word Problem Solving
on Math23K
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation.
Momentum Accelerates the Convergence of Stochastic AUPRC Maximization
In this paper, we further improve the stochastic optimization of AURPC by (i) developing novel stochastic momentum methods with a better iteration complexity of $O(1/\epsilon^4)$ for finding an $\epsilon$-stationary solution; and (ii) designing a novel family of stochastic adaptive methods with the same iteration complexity, which enjoy faster convergence in practice.
Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds.
Ranked #19 on
3D Object Detection
on SUN-RGBD val
Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on
Instance Segmentation
on nuScenes
Enhancement of Superconductivity Linked with Linear-in-Temperature/Field Resistivity in Ion-Gated FeSe Films
Iron selenide (FeSe) - the structurally simplest iron-based superconductor, has attracted tremendous interest in the past years.
Superconductivity
Evolved Massive Stars at Low-metallicity IV. Using 1.6 $μ$m "H-bump" to identify red supergiant stars: a case study of NGC 6822
Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates.
Solar and Stellar Astrophysics Astrophysics of Galaxies
Deep View Synthesis via Self-Consistent Generative Network
To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information.
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)
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion
Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data.
Class Distribution Alignment for Adversarial Domain Adaptation
To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios.
Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward
The 2D and 3D dimensions of pedestrians are determined from the camera captures and further utilized through two feedforward links connected to the orientation estimator.
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition module is presented to sequentially generate instances from coarse to fine.
Attention Guided Network for Retinal Image Segmentation
Learning structural information is critical for producing an ideal result in retinal image segmentation.
RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation
To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders.
Ranked #3 on
Semantic Segmentation
on ScanNetV2
Resolution-invariant Person Re-Identification
RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively.
A Novel Demodulation and Estimation Algorithm for Blackout Communication: Extract Principal Components with Deep Learning
For reentry or near space communication, owing to the influence of the time-varying plasma sheath channel environment, the received IQ baseband signals are severely rotated on the constellation.
Bi-Directional Cascade Network for Perceptual Edge Detection
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Ranked #2 on
Edge Detection
on BRIND
The Period-Luminosity Relations of Red Supergiants in M33 and M31
The period-luminosity (P-L) relation is analyzed for the RSGs in the fundamental mode.
Solar and Stellar Astrophysics Astrophysics of Galaxies
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation
As a result, our framework can output both the semantic prediction and the instance prediction.
3D Instance Segmentation
3D Semantic Instance Segmentation
+2
Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
Medical Physics
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Visual tracking is confronted by the dilemma to locate a target both}accurately and efficiently, and make decisions online whether and how to adapt the appearance model or even restart tracking.
Instance-level Human Parsing via Part Grouping Network
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #6 on
Human Part Segmentation
on CIHP
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
Ranked #3 on
Temporal Action Proposal Generation
on THUMOS' 14
Conditional Generative Adversarial Network for Structured Domain Adaptation
In recent years, deep neural nets have triumphed over many computer vision problems, including semantic segmentation, which is a critical task in emerging autonomous driving and medical image diagnostics applications.
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images.
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras
Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images.
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear.
A Multi-model Combination Approach for Probabilistic Wind Power Forecasting
Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models.
A Survey of Multi-View Representation Learning
Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment.
Top-N Recommendation on Graphs
To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix.
Compressing Deep Convolutional Networks using Vector Quantization
In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify.
Ranked #1 on
3D Face Modelling
on LFW
Web-Scale Training for Face Identification
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web.
