Search Results for author:
Found 27 papers, 12 papers with code
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models.
Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen Concentrations in North Temperate Lakes
Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks.
BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information.
FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
Firstly, a frustum feature encoder module is used to extract per-point features within the frustum region, which preserves scene consistency and is crucial for point-level predictions.
Ranked #2 on
3D Semantic Segmentation
on nuScenes
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment.
Ranked #3 on
Click-Through Rate Prediction
on Avazu
Hierarchical Neural Coding for Controllable CAD Model Generation
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree.
M3PT: A Multi-Modal Model for POI Tagging
In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations.
In vivo labeling and quantitative imaging of neurons using MRI
Developing in vivo neuronal labeling and imaging techniques is crucial for studying the structure and function of neural circuits.
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations.
D3D-HOI: Dynamic 3D Human-Object Interactions from Videos
We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos.
Structured Outdoor Architecture Reconstruction by Exploration and Classification
This paper presents an explore-and-classify framework for structured architectural reconstruction from an aerial image.
Learning Self-Consistency for Deepfake Detection
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
We present a mutual information-based framework for unsupervised image-to-image translation.
On Improving Temporal Consistency for Online Face Liveness Detection
In this paper, we focus on improving the online face liveness detection system to enhance the security of the downstream face recognition system.
Ranked #4 on
Face Anti-Spoofing
on SiW (Protocol 3)
On Improving the Generalization of Face Recognition in the Presence of Occlusions
In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions.
Adversarial Representation Learning for Text-to-Image Matching
For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem.
d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
On the one hand, deep neural networks are effective in learning large datasets.
Ranked #4 on
Domain Adaptation
on SVNH-to-MNIST
$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
Open Source Face Recognition Performance Evaluation Package
Biometrics-related research has been accelerated significantly by deep learning technology.
Robust and High Performance Face Detector
In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks.
Error Correction Maximization for Deep Image Hashing
We propose to use the concept of the Hamming bound to derive the optimal criteria for learning hash codes with a deep network.
Deep Imbalanced Attribute Classification using Visual Attention Aggregation
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem.
A Spatial and Temporal Features Mixture Model with Body Parts for Video-based Person Re-Identification
In this paper we propose a novel Spatial and Temporal Features Mixture Model (STFMM) based on convolutional neural network (CNN) and recurrent neural network (RNN), in which the human body is split into $N$ parts in horizontal direction so that we can obtain more specific features.
Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment
The method relies on Convolutional Point-set Representation (CPR), a carefully designed matrix representation to summarize different layers of information encoded in the set of detected points in the annotated image.
Face Detection Using Improved Faster RCNN
Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection.
Ranked #9 on
Face Detection
on WIDER Face (Hard)
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset and 3% on the IJB-A dataset on average in face identification tasks.
