Search Results for author:
Found 74 papers, 38 papers with code
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot
Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design.
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
Ranked #6 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling
The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans.
AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach
Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.
Towards Balanced Learning for Instance Recognition
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
Ranked #5 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Enhanced Boundary Learning for Glass-like Object Segmentation
We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours.
Ranked #20 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields.
Ranked #2 on
3D Object Detection
on KITTI Cars Easy val
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems.
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks
To suit our network to self-supervised learning, we design several novel loss functions that utilize the inherent properties of LiDAR point clouds.
Channel-wise Alignment for Adaptive Object Detection
Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information.
Understanding the wiring evolution in differentiable neural architecture search
To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
TSIT: A Simple and Versatile Framework for Image-to-Image Translation
We introduce a simple and versatile framework for image-to-image translation.
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image.
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations.
TPNet: Trajectory Proposal Network for Motion Prediction
However, it remains difficult for these methods to provide multimodal predictions as well as integrate physical constraints such as traffic rules and movable areas.
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner.
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching
Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline.
Temporal Pyramid Network for Action Recognition
Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle.
Ranked #105 on
Action Recognition
on Something-Something V2
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds.
DSNAS: Direct Neural Architecture Search without Parameter Retraining
We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy.
Ranked #18 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information.
Ranked #17 on
Vehicle Pose Estimation
on KITTI Cars Hard
Side-Aware Boundary Localization for More Precise Object Detection
To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket.
Learning a Decision Module by Imitating Driver's Control Behaviors
In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning.
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference.
Dense Fusion Classmate Network for Land Cover Classification
Recently, FCNs based methods have made great progress in semantic segmentation.
Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints
Most of existing methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises.
Graph-guided Architecture Search for Real-time Semantic Segmentation
Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint.
Robust Multi-Modality Multi-Object Tracking
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects.
Ranked #17 on
Multiple Object Tracking
on KITTI Test (Online Methods)
(MOTA metric)
GDRQ: Group-based Distribution Reshaping for Quantization
Low-bit quantization is challenging to maintain high performance with limited model capacity (e. g., 4-bit for both weights and activations).
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object regions across consecutive frames.
Towards Instance-level Image-to-Image Translation
The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation.
Switchable Whitening for Deep Representation Learning
Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods.
Ranked #7 on
Robust Object Detection
on DWD
Libra R-CNN: Towards Balanced Learning for Object Detection
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
Ranked #162 on
Object Detection
on COCO test-dev
(using extra training data)
R$^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\mathcal{R}^2$-CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector.
Hybrid Task Cascade for Instance Segmentation
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
Ranked #32 on
Object Detection
on COCO-O
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e. g., image-level, region-level, and pixel-level are diverging.
Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation
Due to the expensive and time-consuming annotations (e. g., segmentation) for real-world images, recent works in computer vision resort to synthetic data.
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 #52 on
Semantic Segmentation
on Cityscapes test
Generative Adversarial Frontal View to Bird View Synthesis
Due to the large gap and severe deformation between the frontal view and bird view, generating a bird view image from a single frontal view is challenging.
SegStereo: Exploiting Semantic Information for Disparity Estimation
Disparity estimation for binocular stereo images finds a wide range of applications.
Ranked #6 on
Semantic Segmentation
on KITTI Semantic Segmentation
Pose Guided Human Video Generation
Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance.
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
Ranked #3 on
All-day Semantic Segmentation
on All-day CityScapes
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Generating scene graph to describe all the relations inside an image gains increasing interests these years.
Ranked #1 on
Scene Graph Generation
on VRD
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning.
Eliminating Background-Bias for Robust Person Re-Identification
State-of-the-art methods mainly utilize deep learning based approaches for learning visual features for describing person appearances.
Mask-aware Photorealistic Face Attribute Manipulation
The task of face attribute manipulation has found increasing applications, but still remains challeng- ing with the requirement of editing the attributes of a face image while preserving its unique details.
Low-Latency Video Semantic Segmentation
Recent years have seen remarkable progress in semantic segmentation.
Ranked #6 on
Video Semantic Segmentation
on Cityscapes val
Context Encoding for Semantic Segmentation
In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.
Ranked #1 on
Pose Estimation
on KITTI 2015
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
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
Ranked #52 on
Lane Detection
on CULane
(using extra training data)
Face Parsing via Recurrent Propagation
Face parsing is an important problem in computer vision that finds numerous applications including recognition and editing.
Video Object Segmentation with Re-identification
Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module.
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this paper.
Ranked #11 on
Dichotomous Image Segmentation
on DIS-TE4
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
+3
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
Ranked #4 on
Video Semantic Segmentation
on CamVid
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.
Revisiting Batch Normalization For Practical Domain Adaptation
However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain.
Semantic Segmentation With Object Clique Potential
In this paper, we propose an object clique potential for semantic segmentation.
Just Noticeable Defocus Blur Detection and Estimation
We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images.
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering.
Understanding and Diagnosing Visual Tracking Systems
Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community.
Hierarchical Saliency Detection on Extended CSSD
Complex structures commonly exist in natural images.
Discriminative Blur Detection Features
Ubiquitous image blur brings out a practically important question what are effective features to differentiate between blurred and unblurred image regions.
Hierarchical Saliency Detection
When dealing with objects with complex structures, saliency detection confronts a critical problem namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns.
