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Found 10 papers, 7 papers with code
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding.
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets.
Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation
We first propose source student backbone and target student backbone to respectively extract the source-style and target-style feature for both source and target images.
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
LDC is built upon a parameterized calibration unit (PCU), which initializes biased distributions for all classes based on classifier vectors (memory-free) and a single covariance matrix.
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations.
A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval
To handle these two issues, we propose a feature consistency driven attention erasing network (FCAENet) for fine-grained image retrieval.
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation
To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task.
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Ranked #68 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization.
Harmonic Feature Activation for Few-Shot Semantic Segmentation
HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way.
