Search Results for author:
Found 59 papers, 31 papers with code
CN: Channel Normalization For Point Cloud Recognition
In 3D recognition, to fuse multi-scale structure information, existing methods apply hierarchical frameworks stacked by multiple fusion layers for integrating current relative locations with structure information from the previous level.
A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction
Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis.
Aspect-Based Sentiment Analysis (ABSA)
Aspect Sentiment Triplet Extraction
+1
Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields
Also, we present Point Encoding to build Multi-scale Radiance Fields that provide discriminative 3D point features.
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #2 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Learning Context-aware Classifier for Semantic Segmentation
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing.
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
We show that together with a reflection direction-dependent radiance, our model achieves high-quality surface reconstruction on reflective surfaces and outperforms the state-of-the-arts by a large margin.
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples.
Lightweight Facial Attractiveness Prediction Using Dual Label Distribution
To make the best use of the dataset, the manual ratings, attractiveness score, and standard deviation are aggregated explicitly to construct a dual label distribution, including the attractiveness distribution and the rating distribution.
Generalized Parametric Contrastive Learning
Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning.
Ranked #4 on
Long-tail Learning
on iNaturalist 2018
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations.
Improving Data Driven Inverse Text Normalization using Data Augmentation
Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
EfficientNeRF: Efficient Neural Radiance Fields
Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes.
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features.
Stratified Transformer for 3D Point Cloud Segmentation
In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.
Ranked #5 on
Semantic Segmentation
on ScanNet
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment
We revisit the one- and two-stage detector distillation tasks and present a simple and efficient semantic-aware framework to fill the gap between them.
EfficientNeRF Efficient Neural Radiance Fields
Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes.
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.
Deep Structured Instance Graph for Distilling Object Detectors
Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks.
Exploring and Improving Mobile Level Vision Transformers
We analyze the reason behind this phenomenon, and propose a novel irregular patch embedding module and adaptive patch fusion module to improve the performance.
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.
Parametric Contrastive Learning
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition.
Ranked #9 on
Long-tail Learning
on iNaturalist 2018
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Semantic segmentation has made tremendous progress in recent years.
Self-Supervised 3D Mesh Reconstruction From Single Images
Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation.
Neural Monge Map estimation and its applications
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another.
Distilling Knowledge via Knowledge Review
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network.
Ranked #9 on
Knowledge Distillation
on CIFAR-100
Jigsaw Clustering for Unsupervised Visual Representation Learning
It is even comparable to the contrastive learning methods when only half of training batches are used.
Improving Calibration for Long-Tailed Recognition
Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning.
Ranked #11 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Video Instance Segmentation with a Propose-Reduce Paradigm
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos.
Ranked #1 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions.
ResLT: Residual Learning for Long-tailed Recognition
From this perspective, the trivial solution utilizes different branches for the head, medium, and tail classes respectively, and then sums their outputs as the final results is not feasible.
Ranked #16 on
Long-tail Learning
on iNaturalist 2018
Learnable Boundary Guided Adversarial Training
Previous adversarial training raises model robustness under the compromise of accuracy on natural data.
Ranked #1 on
Adversarial Defense
on CIFAR-100
Generalized Few-shot Semantic Segmentation
Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.
Generalized Few-Shot Semantic Segmentation
Semantic Segmentation
Dive Deeper Into Box for Object Detection
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
Instance segmentation is an important task for scene understanding.
Ranked #5 on
3D Instance Segmentation
on STPLS3D
3DSSD: Point-based 3D Single Stage Object Detector
Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
Learning Stochastic Behaviour from Aggregate Data
Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable.
GridMask Data Augmentation
Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and yet very effective.
DSGN: Deep Stereo Geometry Network for 3D Object Detection
Most state-of-the-art 3D object detectors heavily rely on LiDAR sensors because there is a large performance gap between image-based and LiDAR-based methods.
EmbedMask: Embedding Coupling for One-stage Instance Segmentation
The pixel-level clustering enables EmbedMask to generate high-resolution masks without missing details from repooling, and the existence of proposal embedding simplifies and strengthens the clustering procedure to achieve high speed with higher performance than segmentation-based methods.
Ranked #84 on
Instance Segmentation
on COCO test-dev
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process.
Ranked #25 on
Semantic Segmentation
on S3DIS Area5
Driver Identification via the Steering Wheel
Building upon existing work, we provide a novel approach for the design of the window length parameter that provides evidence that reliable driver identification can be achieved with data limited to the steering wheel only.
Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification
Person re-identification aims to identify whether pairs of images belong to the same person or not.
Fast Point R-CNN
We present a unified, efficient and effective framework for point-cloud based 3D object detection.
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD).
Associatively Segmenting Instances and Semantics in Point Clouds
A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed.
Ranked #15 on
3D Instance Segmentation
on S3DIS
(mRec metric)
Human Pose Estimation with Spatial Contextual Information
With this principle, we present two conceptually simple and yet computational efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural Network (PGNN), to exploit underlying contextual information.
Ranked #10 on
Pose Estimation
on MPII Human Pose
IPOD: Intensive Point-based Object Detector for Point Cloud
We present a novel 3D object detection framework, named IPOD, based on raw point cloud.
Ranked #1 on
3D Object Detection
on KITTI Pedestrians Easy
Sequential Context Encoding for Duplicate Removal
Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset.
PSANet: Point-wise Spatial Attention Network for Scene Parsing
We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes.
Ranked #47 on
Semantic Segmentation
on Cityscapes test
Path Aggregation Network for Instance Segmentation
The way that information propagates in neural networks is of great importance.
Ranked #3 on
Object Detection
on iSAID
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.
SGN: Sequential Grouping Networks for Instance Segmentation
By exploiting two-directional information, the second network groups horizontal and vertical lines into connected components.
Label distribution based facial attractiveness computation by deep residual learning
In order to address the first challenge, this paper recasts facial attractiveness computation as a label distribution learning (LDL) problem rather than a traditional single-label supervised learning task.
Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation
Aiming at simultaneous detection and segmentation (SDS), we propose a proposal-free framework, which detect and segment object instances via mid-level patches.
Semantic Segmentation With Object Clique Potential
In this paper, we propose an object clique potential for semantic segmentation.
Box Aggregation for Proposal Decimation: Last Mile of Object Detection
Regions-with-convolutional-neural-network (RCNN) is now a commonly employed object detection pipeline.
Contour Box: Rejecting Object Proposals Without Explicit Closed Contours
Closed contour is an important objectness indicator.
