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Found 18 papers, 9 papers with code
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.
Modeling Continuous Motion for 3D Point Cloud Object Tracking
The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention
3D object detection with surround-view images is an essential task for autonomous driving.
Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors
Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors.
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics.
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes.
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.
Accelerating DETR Convergence via Semantic-Aligned Matching
First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics.
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.
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world.
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis.
Unbalanced Feature Transport for Exemplar-based Image Translation
In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully.
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion
DA-DETR introduces a novel CNN-Transformer Blender (CTBlender) that fuses the CNN features and Transformer features ingeniously for effective feature alignment and knowledge transfer across domains.
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.
Ranked #7 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses
Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
