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Tool learning with foundation models
Humans possess an extraordinary ability to create and utilize tools. With the advent of
foundation models, artificial intelligence systems have the potential to be equally adept in …
foundation models, artificial intelligence systems have the potential to be equally adept in …
A brief survey on semantic segmentation with deep learning
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification
Multiple instance learning (MIL) has been increasingly used in the classification of
histopathology whole slide images (WSIs). However, MIL approaches for this specific …
histopathology whole slide images (WSIs). However, MIL approaches for this specific …
Multi-class token transformer for weakly supervised semantic segmentation
This paper proposes a new transformer-based framework to learn class-specific object
localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS) …
Learning affinity from attention: End-to-end weakly-supervised semantic segmentation with transformers
Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …
Regional semantic contrast and aggregation for weakly supervised semantic segmentation
Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation
Abstract Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has attracted much attention due to low annotation costs. Existing methods often rely on …
has attracted much attention due to low annotation costs. Existing methods often rely on …
L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation
Mining precise class-aware attention maps, aka, class activation maps, is essential for
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
Class re-activation maps for weakly-supervised semantic segmentation
Extracting class activation maps (CAM) is arguably the most standard step of generating
pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …
pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …
Clims: Cross language image matching for weakly supervised semantic segmentation
It has been widely known that CAM (Class Activation Map) usually only activates
discriminative object regions and falsely includes lots of object-related backgrounds. As only …
discriminative object regions and falsely includes lots of object-related backgrounds. As only …