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Found 66 papers, 28 papers with code
Compositional 3D Scene Synthesis with Scene Graph Guided Layout-Shape Generation
Recent progresses have been made in shape generation with powerful generative models, such as diffusion models, which increases the shape fidelity.
Robust Shape Fitting for 3D Scene Abstraction
A RANSAC estimator guided by a neural network fits these primitives to a depth map.
Convincing Rationales for Visual Question Answering Reasoning
Visual Question Answering (VQA) is a challenging task of predicting the answer to a question about the content of an image.
Transformer-based Multimodal Change Detection with Multitask Consistency Constraints
We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks.
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models
3D building generation with low data acquisition costs, such as single image-to-3D, becomes increasingly important.
Interactive Image Segmentation with Cross-Modality Vision Transformers
Interactive image segmentation aims to segment the target from the background with the manual guidance, which takes as input multimodal data such as images, clicks, scribbles, and bounding boxes.
Learning Similarity between Scene Graphs and Images with Transformers
Scene graph generation is conventionally evaluated by (mean) Recall@K, which measures the ratio of correctly predicted triplets that appear in the ground truth.
LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints
Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints.
Generating Evidential BEV Maps in Continuous Driving Space
Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown.
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images
Visual question answering (VQA) is an important and challenging multimodal task in computer vision.
SSGVS: Semantic Scene Graph-to-Video Synthesis
A semantic scene graph-to-video synthesis framework (SSGVS), based on the pre-trained VSG encoder, VQ-VAE, and auto-regressive Transformer, is proposed to synthesize a video given an initial scene image and a non-fixed number of semantic scene graphs.
Flow-based GAN for 3D Point Cloud Generation from a Single Image
Generating a 3D point cloud from a single 2D image is of great importance for 3D scene understanding applications.
GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model
This paper proposes an attention-based graph model, named GATraj, which achieves a good balance of prediction accuracy and inference speed.
RelTR: Relation Transformer for Scene Graph Generation
Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Disentangled Lifespan Face Synthesis
The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture, while being identity preserving.
Spatial-Temporal Transformer for Dynamic Scene Graph Generation
Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation.
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
A RANSAC estimator guided by a neural network fits these primitives to 3D features, such as a depth map.
Text to Image Generation with Semantic-Spatial Aware GAN
Text-to-image synthesis (T2I) aims to generate photo-realistic images which are semantically consistent with the text descriptions.
Context-Aware Layout to Image Generation with Enhanced Object Appearance
We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators.
Ranked #1 on
Layout-to-Image Generation
on COCO-Stuff 128x128
Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery
Semantic segmentation for aerial platforms has been one of the fundamental scene understanding task for the earth observation.
Self-supervised monocular depth estimation from oblique UAV videos
Monocular video frames are used for training the deep learning model which learns depth and pose information jointly through two different networks, one each for depth and pose.
LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds
Interpretation of Airborne Laser Scanning (ALS) point clouds is a critical procedure for producing various geo-information products like 3D city models, digital terrain models and land use maps.
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors
Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications.
Real-time Semantic Segmentation with Context Aggregation Network
With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications.
Exploring Dynamic Context for Multi-path Trajectory Prediction
In our framework, first, the spatial context between agents is explored by using self-attention architectures.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.
AMENet: Attentive Maps Encoder Network for Trajectory Prediction
Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic.
LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation.
Plug & Play Convolutional Regression Tracker for Video Object Detection
As the tracker reuses the features from the detector, it is a very light-weighted increment to the detection network.
MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future.
NODIS: Neural Ordinary Differential Scene Understanding
Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image.
Ranked #8 on
Scene Graph Generation
on Visual Genome
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.
LIP: Learning Instance Propagation for Video Object Segmentation
In recent years, the task of segmenting foreground objects from background in a video, i. e. video object segmentation (VOS), has received considerable attention.
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision.
Ranked #1 on
Horizon Line Estimation
on KITTI Horizon
A deep-learning-based approach for fast and robust steel surface defects classification
Automatic visual recognition of steel surface defects provides critical functionality to facilitate quality control of steel strip production.
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain.
Target-Tailored Source-Transformation for Scene Graph Generation
We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation.
Robust object extraction from remote sensing data
The delineation approach to which the evaluation framework is applied, was previously introduced and is substantially improved in this study.
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).
Security Event Recognition for Visual Surveillance
The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
UAVid: A Semantic Segmentation Dataset for UAV Imagery
There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the driving car.
Patch-based Evaluation of Dense Image Matching Quality
Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data.
Change Detection between Multimodal Remote Sensing Data Using Siamese CNN
Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring.
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy.
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models
In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions.
Triplet-based Deep Similarity Learning for Person Re-Identification
Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one.
Temporally Object-based Video Co-Segmentation
Therefore, we formulate a graphical model to select a proposal stream for each object in which the pairwise potentials consist of the appearance dissimilarity between different streams in the same video and also the similarity between the streams in different videos.
Unsupervised Deep Domain Adaptation for Pedestrian Detection
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.
Slice Sampling Particle Belief Propagation
We propose to avoid dependence on a proposal distribution by introducing a slice sampling based PBP algorithm.
Vehicle Detection in Aerial Images
Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier.
Natural Language Guided Visual Relationship Detection
Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features.
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples.
Towards Automated Cadastral Boundary Delineation from UAV Data
This study proposes (i) a workflow that automatically extracts candidate cadastral boundaries from UAV orthoimages and (ii) a tool for their semi-automatic processing to delineate final cadastral boundaries.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated monocular images.
Ranked #3 on
Horizon Line Estimation
on York Urban Dataset
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling.
Motion Segmentation via Global and Local Sparse Subspace Optimization
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model.
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort.
Real-Time RGB-D based Template Matching Pedestrian Detection
We show that our method outperforms the state-of-the-art approaches.
On Support Relations and Semantic Scene Graphs
In contrast to previous methods for extracting support relations, the proposed approach generates more accurate results, and does not require a pixel-wise semantic labeling of the scene.
Unbiased Sparse Subspace Clustering By Selective Pursuit
If these unknown subspaces are well-separated this algorithm is guaranteed to succeed.
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.
Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation
A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function.
