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Found 18 papers, 6 papers with code
Single-temporal Supervised Remote Change Detection for Domain Generalization
In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization.
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage.
HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands.
Tuning-Free Image Customization with Image and Text Guidance
To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
By pulling the learned features to these semantic anchors, several advantages can be attained: 1) the intra-class compactness and naturally inter-class separability, 2) induced bias or errors from feature learning can be avoided, and 3) robustness to the long-tailed problem.
Can the Query-based Object Detector Be Designed with Fewer Stages?
Query-based object detectors have made significant advancements since the publication of DETR.
IIDM: Inter and Intra-domain Mixing for Semi-supervised Domain Adaptation in Semantic Segmentation
In this paper, we highlight the significance of exploiting the intra-domain information between the labeled target data and unlabeled target data.
DISCO: Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Large-scale well-annotated datasets are of great importance for training an effective object detector.
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer.
HopFIR: Hop-wise GraphFormer with Intragroup Joint Refinement for 3D Human Pose Estimation
The HGF module groups the joints by k-hop neighbors and applies a hopwise transformer-like attention mechanism to these groups to discover latent joint synergies.
Ranked #141 on
3D Human Pose Estimation
on Human3.6M
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF).
Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept
Inspired by this, we propose a new framework that leverages the labeled 3D human poses to learn a 3D concept of the human body to reduce the ambiguity.
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
Learning a good 3D human pose representation is important for human pose related tasks, e. g. human 3D pose estimation and action recognition.
