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Found 10 papers, 6 papers with code
Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding
Therefore, they cannot be directly applied to cope with image change understanding (ICU), which requires models to capture actual changes between multiple images and describe them in language.
RSGPT: A Remote Sensing Vision Language Model and Benchmark
This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
UniNeXt: Exploring A Unified Architecture for Vision Recognition
Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone.
Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
In this work, we propose a novel and data-efficient framework for WILSS, named FMWISS.
Efficient Mask Correction for Click-Based Interactive Image Segmentation
To this end, we propose an efficient method to correct the mask with a lightweight mask correction network.
FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation
Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation.
Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
More specifically, we use the pseudo-labels generated by a mean teacher to supervise the student network to achieve a mutual knowledge distillation between the two branches.
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Further, we provide an in-depth analysis of the mechanism and rational behind our approach, which gives us a better understanding of why leverage logits in lieu of features can help domain generalization.
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
Previous works [3, 27] fail to employ strong augmentation in pseudo label learning efficiently, as the large distribution change caused by strong augmentation harms the batch normalisation statistics.
Multi Receptive Field Network for Semantic Segmentation
Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image.
Ranked #20 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
