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Found 26 papers, 15 papers with code
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion
The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection.
Contextual Text Block Detection towards Scene Text Understanding
We formulate the new setup by a dual detection task which first detects integral text units and then groups them into a CTB.
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
Specifically, we design cross-frame pseudo labelling to provide pseudo supervision from previous video frames while learning from the augmented current video frames.
Hierarchical Mask Calibration for Unified Domain Adaptive Panoptic Segmentation
Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains.
Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation
We build the balanced subclass distributions by clustering pixels of each original class into multiple subclasses of similar sizes, which provide class-balanced pseudo supervision to regularize the class-biased segmentation.
Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA.
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training.
Contextual Text Detection
This paper presents Contextual Text Detection, a new setup that detects contextual text blocks for better understanding of texts in scenes.
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos.
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis.
Spectral Unsupervised Domain Adaptation for Visual Recognition
Second, it introduces multi-view spectral learning that learns useful unsupervised representations by maximizing mutual information among multiple ST-generated spectral views of each target sample.
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
We position the few labeled target samples as references that gauge the similarity between source and target features and guide adaptive inter-domain alignment for learning more similar source features.
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
With FAA-generated samples, the training can continue the 'random walk' and drift into an area with a flat loss landscape, leading to more robust domain adaptation.
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks.
I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition
The first task focuses on image-to-character (I2C) mapping which detects a set of character candidates from images based on different alignments of visual features in an non-sequential way.
DA-DETR: Domain Adaptive Detection Transformer by Hybrid Attention
In this work, we adopt a one-stage detector and design DA-DETR, a simple yet effective domain adaptive object detection network that performs inter-domain alignment with a single discriminator.
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation
Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation.
FSDR: Frequency Space Domain Randomization for Domain Generalization
It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features.
Cross-View Regularization for Domain Adaptive Panoptic Segmentation
The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains.
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation
Specifically, we design a residual dense block with multiple receptive fields as a building block in the encoder which preserves detailed information in each modality and learns hierarchical modality-specific and fused features effectively.
Ranked #15 on
3D Semantic Segmentation
on SemanticKITTI
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively.
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations.
Hierarchy Composition GAN for High-fidelity Image Synthesis
Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts.
