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Found 21 papers, 18 papers with code
Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free
The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image.
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.
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 #23 on
3D Semantic Segmentation
on SemanticKITTI
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.
Ranked #2 on
Domain Adaptation
on Panoptic SYNTHIA-to-Mapillary
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.
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.
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.
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.
Bi-level Feature Alignment for Versatile Image Translation and Manipulation
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
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.
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.
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.
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.
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.
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.
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively.
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the progress achieved in this area.
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
Conditional Tuning Network for Few-Shot Adaptation of Segmentation Anything Model
CAT-SAM freezes the entire SAM and adapts its mask decoder and image encoder simultaneously with a small number of learnable parameters.
