OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference | Paper Citation Record

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This paper cites Clip- graphs: Multimodal graph networks to infer object-room affinities.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Clip- graphs: Multimodal graph networks to infer object-room affinities

Reference 1

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Observation 80f40780-dd37-4fc7-bac2-7b22e4389f2d · outbound

This paper cites Estimating kullback-leibler divergence using kernel machines.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Estimating kullback-leibler divergence using kernel machines

Reference 2

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This paper cites A Theoretical Analysis of Contrastive Unsupervised Representation Learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Reference 3

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This paper cites Investigating the Role of Negatives in Contrastive Representation Learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Investigating the Role of Negatives in Contrastive Representation Learning

Reference 4

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This paper cites Do more negative samples necessarily hurt in contrastive learn- ing? InInternational conference on machine learning, pages 1101–1116.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Do more negative samples necessarily hurt in contrastive learn- ing? InInternational conference on machine learning, pages 1101–1116

Reference 5

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This paper cites Video pretraining (vpt): Learning to act by watching unlabeled online videos.Advances in Neural Information Processing Systems, 35:24639–24654, 2022.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Video pretraining (vpt): Learning to act by watching unlabeled online videos.Advances in Neural Information Processing Systems, 35:24639–24654, 2022

Reference 6

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This paper cites Place recognition survey: An update on deep learning approaches.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Place recognition survey: An update on deep learning approaches

Reference 7

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This paper cites Data vi- sualization with multidimensional scaling.Journal of com- putational and graphical statistics, 17(2):444–472, 2008.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Data vi- sualization with multidimensional scaling.Journal of com- putational and graphical statistics, 17(2):444–472, 2008

Reference 8

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This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.Advances in neural informa- tion processing systems, 32, 2019.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Learning imbalanced datasets with label- distribution-aware margin loss.Advances in neural informa- tion processing systems, 32, 2019

Reference 9

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This paper cites Emerg- ing properties in self-supervised vision transformers.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Emerg- ing properties in self-supervised vision transformers

Reference 10

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Observation 49bf9791-f730-4bae-b346-f14bcc95de0f · outbound

This paper cites Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Reference 11

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This paper cites $A^2$Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference $A^2$Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models

Reference 12

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This paper cites A simple framework for contrastive learning of visual representations.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference A simple framework for contrastive learning of visual representations

Reference 13

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This paper cites Improved Baselines with Momentum Contrastive Learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Improved Baselines with Momentum Contrastive Learning

Reference 14

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This paper cites An empirical study of training self-supervised vision transformers.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference An empirical study of training self-supervised vision transformers

Reference 15

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This paper cites Duel: Dupli- cate elimination on active memory for self-supervised class- imbalanced learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Duel: Dupli- cate elimination on active memory for self-supervised class- imbalanced learning

Reference 16

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This paper cites Imagenet: A large-scale hierarchical image database.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Imagenet: A large-scale hierarchical image database

Reference 17

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This paper cites Routledge, 2017.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Routledge, 2017

Reference 18

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This paper cites CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation

Reference 19

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This paper cites Rethinking The Uniformity Metric in Self-Supervised Learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Rethinking The Uniformity Metric in Self-Supervised Learning

Reference 20

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This paper cites A review of environmental context detection for navigation based on multiple sensors.Sensors, 20(16), 2020.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference A review of environmental context detection for navigation based on multiple sensors.Sensors, 20(16), 2020

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:30.405319Z

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This paper cites Reliable estimation of kl divergence using a discriminator in repro- ducing kernel hilbert space.Advances in Neural Information Processing Systems, 34:10221–10233, 2021.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Reliable estimation of kl divergence using a discriminator in repro- ducing kernel hilbert space.Advances in Neural Information Processing Systems, 34:10221–10233, 2021

Reference 22

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This paper cites Classification using kernel density estimates: Multiscale analysis and visualization.Technometrics, 48(1):120–132,.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Classification using kernel density estimates: Multiscale analysis and visualization.Technometrics, 48(1):120–132,

Reference 23

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This paper cites Deep residual learning for image recognition.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Deep residual learning for image recognition

Reference 24

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This paper cites Towards open world object de- tection.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Towards open world object de- tection

Reference 25

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This paper cites Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,

Reference 26

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This paper cites Segment any- thing.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Segment any- thing

Reference 27

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This paper cites Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization

Reference 28

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This paper cites Auto mc-reward: Automated dense reward de- sign with large language models for minecraft.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Auto mc-reward: Automated dense reward de- sign with large language models for minecraft

Reference 29

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This paper cites Understanding and Improving Transfer Learning of Deep Models via Neural Collapse.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Understanding and Improving Transfer Learning of Deep Models via Neural Collapse

Reference 30

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This paper cites Self-supervised Learning is More Robust to Dataset Imbalance.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Self-supervised Learning is More Robust to Dataset Imbalance

Reference 31

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This paper cites Sphereface: Deep hypersphere embedding for face recognition.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Sphereface: Deep hypersphere embedding for face recognition

Reference 32

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This paper cites The MIT Press, 1999.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference The MIT Press, 1999

Reference 33

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This paper cites Representation Learning with Contrastive Predictive Coding.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Representation Learning with Contrastive Predictive Coding

Reference 34

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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This paper cites Matching multiple perspectives for efficient representation learning.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Matching multiple perspectives for efficient representation learning

Reference 36

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This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020

Reference 37

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This paper cites Mp5: A multi-modal open-ended embodied system in minecraft via active perception.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Mp5: A multi-modal open-ended embodied system in minecraft via active perception

Reference 38

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This paper cites Learning transferable visual models from natural language supervi- sion.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Learning transferable visual models from natural language supervi- sion

Reference 39

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This paper cites Self- supervised learning through efference copies.Advances in Neural Information Processing Systems, 35:4543–4557,.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Self- supervised learning through efference copies.Advances in Neural Information Processing Systems, 35:4543–4557,

Reference 40

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This paper cites ViNT: A Foundation Model for Visual Navigation.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference ViNT: A Foundation Model for Visual Navigation

Reference 41

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This paper cites Improved deep metric learning with multi- class n-pair loss objective.Advances in neural information processing systems, 29, 2016.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Improved deep metric learning with multi- class n-pair loss objective.Advances in neural information processing systems, 29, 2016

Reference 42

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This paper cites Nomad: Goal masked diffusion policies for nav- igation and exploration.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Nomad: Goal masked diffusion policies for nav- igation and exploration

Reference 43

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This paper cites The Replica Dataset: A Digital Replica of Indoor Spaces.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference The Replica Dataset: A Digital Replica of Indoor Spaces

Reference 44

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This paper cites Un- derstanding self-supervised learning dynamics without con- trastive pairs.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Un- derstanding self-supervised learning dynamics without con- trastive pairs

Reference 45

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This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 46

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Observation 0fb09a35-0e18-4832-a629-579cfcec9ae1 · outbound

This paper cites Graph based environment representation for vision-and- language navigation in continuous environments, 2023.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Graph based environment representation for vision-and- language navigation in continuous environments, 2023

Reference 47

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Observation 3965416a-3efd-4fc4-bb1b-b62a5697c2cf · outbound

This paper cites Vlfm: Vision-language frontier maps for zero-shot semantic navigation, 2023.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Vlfm: Vision-language frontier maps for zero-shot semantic navigation, 2023

Reference 48

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This paper cites Kernel mixture model for probability density estimation in bayesian classifiers.Data Mining and Knowl- edge Discovery, 32:675–707, 2018.

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference Kernel mixture model for probability density estimation in bayesian classifiers.Data Mining and Knowl- edge Discovery, 32:675–707, 2018

Reference 49

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