ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations | Paper Citation Record

Observation 8bf2686a-5832-47cb-a992-2274bc9d6496 · outbound

This paper cites an unresolved cited work.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Unresolved cited work

Reference 1

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Observation fcb24603-5534-4fa9-8df5-341368e12b3e · outbound

This paper cites Prototype as query for few shot semantic segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Prototype as query for few shot semantic segmentation

Reference 2

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This paper cites A closer look at few-shot classification.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations A closer look at few-shot classification

Reference 3

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This paper cites Shot in the dark: Few-shot learning with no base-class labels.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Shot in the dark: Few-shot learning with no base-class labels

Reference 4

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This paper cites Yolo-world: Real-time open-vocabulary object detec- tion.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Yolo-world: Real-time open-vocabulary object detec- tion

Reference 5

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This paper cites The cityscapes dataset for semantic urban scene understanding.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations The cityscapes dataset for semantic urban scene understanding

Reference 6

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This paper cites Maximum likelihood from incomplete data via the em algorithm.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Maximum likelihood from incomplete data via the em algorithm

Reference 7

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 8

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This paper cites The pascal visual object classes (voc) challenge.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations The pascal visual object classes (voc) challenge

Reference 9

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This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Model-agnostic meta-learning for fast adaptation of deep networks

Reference 10

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This paper cites Learning few-shot segmentation from bounding box annotations.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Learning few-shot segmentation from bounding box annotations

Reference 11

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Observation 2290c85e-b5f8-4874-83e7-2e1b368552ce · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 12

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This paper cites Deep residual learning for image recognition.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Deep residual learning for image recognition

Reference 13

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This paper cites Semantic segmentation of underwater imagery: Dataset and bench- mark.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Semantic segmentation of underwater imagery: Dataset and bench- mark

Reference 14

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This paper cites Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation

Reference 15

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This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B

Reference 16

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This paper cites Imagenet classification with deep convolutional neural networks.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Imagenet classification with deep convolutional neural networks

Reference 17

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This paper cites Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

Reference 18

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This paper cites Mi- crosoft coco: Common objects in context.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Mi- crosoft coco: Common objects in context

Reference 19

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Observation ab317082-49ef-49b5-93a5-5e96ec255c6b · outbound

This paper cites Dynamic prototype convolution network for few- shot semantic segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Dynamic prototype convolution network for few- shot semantic segmentation

Reference 20

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This paper cites Prototype rectification for few-shot learning.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Prototype rectification for few-shot learning

Reference 21

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This paper cites Learning non-target knowledge for few-shot semantic seg- mentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Learning non-target knowledge for few-shot semantic seg- mentation

Reference 22

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This paper cites Intermediate prototype mining transformer for few-shot semantic segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Intermediate prototype mining transformer for few-shot semantic segmentation

Reference 23

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This paper cites Least squares quantization in pcm.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Least squares quantization in pcm

Reference 24

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This paper cites Uavid: A semantic segmentation dataset for uav imagery.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Uavid: A semantic segmentation dataset for uav imagery

Reference 25

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This paper cites Some methods for classification and analysis of multivariate observations.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Some methods for classification and analysis of multivariate observations

Reference 26

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This paper cites On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning

Reference 27

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ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Unresolved cited work

Reference 28

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This paper cites Interclass prototype relation for few-shot segmen- tation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Interclass prototype relation for few-shot segmen- tation

Reference 29

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This paper cites an unresolved cited work.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Unresolved cited work

Reference 30

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This paper cites In- telligent robotic perception systems.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations In- telligent robotic perception systems

Reference 31

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This paper cites Weakly supervised one shot segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Weakly supervised one shot segmentation

Reference 32

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This paper cites You only look once: Unified, real-time object detection.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations You only look once: Unified, real-time object detection

Reference 33

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This paper cites Girshick, and Jian Sun.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Girshick, and Jian Sun

Reference 34

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This paper cites Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings

Reference 35

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 36

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ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Prototypical networks for few-shot learning

Reference 37

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This paper cites Learning to compare: Relation network for few-shot learning.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Learning to compare: Relation network for few-shot learning

Reference 38

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ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Unresolved cited work

Reference 39

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This paper cites Spin: Simultaneous perception interaction and naviga- tion.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Spin: Simultaneous perception interaction and naviga- tion

Reference 40

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ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Attention is all you need

Reference 41

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This paper cites Panet: Few-shot image semantic segmentation with prototype alignment.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Panet: Few-shot image semantic segmentation with prototype alignment

Reference 42

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This paper cites SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Reference 43

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This paper cites Adaptive agent transformer for few-shot segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Adaptive agent transformer for few-shot segmentation

Reference 44

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This paper cites Prototype mixture models for few-shot semantic segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Prototype mixture models for few-shot semantic segmentation

Reference 45

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Observation ed16f92c-aab3-4eaf-8352-ce4d64f1520a · outbound

This paper cites Self-guided and cross- guided learning for few-shot segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Self-guided and cross- guided learning for few-shot segmentation

Reference 46

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This paper cites Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning

Reference 47

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This paper cites Mask matching transformer for few-shot segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Mask matching transformer for few-shot segmentation

Reference 48

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This paper cites Feature- proxy transformer for few-shot segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Feature- proxy transformer for few-shot segmentation

Reference 49

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This paper cites Sg- one: Similarity guidance network for one-shot semantic segmentation.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Sg- one: Similarity guidance network for one-shot semantic segmentation

Reference 50

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Observation 7eaac1b2-9ade-45af-8589-9e2e660169ce · outbound

This paper cites Pyramid scene parsing network.

ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations Pyramid scene parsing network

Reference 51

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