Search Results for author:
Found 69 papers, 27 papers with code
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
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
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
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
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
Attention Guided Network for Retinal Image Segmentation
Learning structural information is critical for producing an ideal result in retinal image segmentation.
Tactics2D: A Reinforcement Learning Environment Library with Generative Scenarios for Driving Decision-making
For access to the source code and participation in discussions, visit the official GitHub page for Tactcis2D at https://github. com/WoodOxen/Tactics2D.
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 #17 on
3D Object Detection
on ScanNetV2
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.
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.
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.
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
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 Image Retrieval
on Flickr30k-CN
(using extra training data)
Zero-Shot Cross-Modal Retrieval
Zero-shot Image Retrieval
+3
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.
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.
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.
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 #7 on
Math Word Problem Solving
on Math23K
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 #2 on
Learning with noisy labels
on Clothing1M
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 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.
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.
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.
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.
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.
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.
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
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
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.
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
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.
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.
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.
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
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
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
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.
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.
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 #7 on
Multiview Detection
on MultiviewX
Self-supervised Contrastive Attributed Graph Clustering
Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
Nevertheless, existing multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix and often overlook the inter-view information.
