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Found 17 papers, 8 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Exploring the Intersection of Complex Aesthetics and Generative AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic Era
This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact.
$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
Entropy Neural Estimation for Graph Contrastive Learning
In this paper, we theoretically illustrate that the entropy of a dataset can be approximated by maximizing the lower bound of the mutual information across different views of a graph, \ie, entropy is estimated by a neural network.
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples.
Mutual Exclusive Modulator for Long-Tailed Recognition
In this paper, we try to look into the root cause of the LTR task, i. e., training samples for each class are greatly imbalanced, and propose a straightforward solution.
Pyramid Fusion Transformer for Semantic Segmentation
The recently proposed MaskFormer gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level classification method.
Inter-Image Communication for Weakly Supervised Localization
We learn a feature center for each category and realize the global feature consistency by forcing the object features to approach class-specific centers.
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization
We propose a novel 3D point cloud segmentation framework named SASO, which jointly performs semantic and instance segmentation tasks.
Ranked #2 on
3D Instance Segmentation
on S3DIS
(mIoU metric)
3D Instance Segmentation
3D Semantic Instance Segmentation
+4
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
To fulfill the direct evaluation, we annotate pixel-level object masks on the ILSVRC validation set.
3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition
In addition, such a fixed adjacency matrix used in all layers leads to the network failing to extract multi-level semantic features.
Ranked #58 on
Skeleton Based Action Recognition
on NTU RGB+D
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
In this way, the possibilities embedded in the produced similarity maps can be adapted to guide the process of segmenting objects.
Ranked #89 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation
Semantic segmentation of 3D unstructured point clouds remains an open research problem.
Self-produced Guidance for Weakly-supervised Object Localization
A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks.
Ranked #1 on
Weakly-Supervised Object Localization
on ILSVRC 2015
Adversarial Complementary Learning for Weakly Supervised Object Localization
With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.
Ranked #2 on
Weakly-Supervised Object Localization
on ILSVRC 2016
Non-flat Ground Detection Based on A Local Descriptor
The main contribution of this paper is a newly proposed descriptor which is implemented in the disparity image to obtain a disparity texture image.
