UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation | Paper Citation Record

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This paper cites URL http://adas.cvc.uab.es/elektra/enigma-portfolio/cvc-14-visible-fir-day-night-pedestrian-sequen\ -dataset/.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation URL http://adas.cvc.uab.es/elektra/enigma-portfolio/cvc-14-visible-fir-day-night-pedestrian-sequen\ -dataset/

Reference 1

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This paper cites URL http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation URL http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/

Reference 2

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Observation 99ab05ae-3cf1-485e-a047-dab78a864d71 · outbound

This paper cites Iris thermal/visible face database.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Iris thermal/visible face database

Reference 3

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Observation 5ab870f1-877c-49ca-85b3-6ff56d90588d · outbound

This paper cites Bahnsen and Thomas B.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Bahnsen and Thomas B

Reference 4

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This paper cites Yuille, Yuyin Zhou, and Cihang Xie.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Yuille, Yuyin Zhou, and Cihang Xie

Reference 5

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Observation 8e593292-2475-44d6-a490-5fba96b7b9e2 · outbound

This paper cites BeiT : BERT pre-training of image transformers.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation BeiT : BERT pre-training of image transformers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:47.295171Z

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This paper cites BIRDSAI : A dataset for detection and tracking in aerial thermal infrared videos.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation BIRDSAI : A dataset for detection and tracking in aerial thermal infrared videos

Reference 7

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Observation e75fe407-9d07-4bcd-bccb-74634afa588f · outbound

This paper cites End-to-end object detection with transformers.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation End-to-end object detection with transformers

Reference 8

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Observation 767aa3da-77b0-49c8-a00a-6d2b46303dad · outbound

This paper cites Emerging properties in self-supervised vision transformers.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Emerging properties in self-supervised vision transformers

Reference 9

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This paper cites Atmospheric transmission and thermal inertia induced blind road segmentation with a large-scale dataset tbrsd.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Atmospheric transmission and thermal inertia induced blind road segmentation with a large-scale dataset tbrsd

Reference 10

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Observation 875dc59f-dbca-4569-aaa5-8ebd5dcb29c9 · outbound

This paper cites Infrared city database, 2021 a.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Infrared city database, 2021 a

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:47.226010Z

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Observation 1b8c89c4-3248-4230-b607-d1f9b130b217 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 12

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

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation An empirical study of training self-supervised vision transformers

Reference 13

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Observation 3a17b849-1223-4d60-a729-cffd2155c35c · outbound

This paper cites Vision transformer adapter for dense predictions.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Vision transformer adapter for dense predictions

Reference 14

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This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 15

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Observation e2aecf06-a01f-421f-bde8-4d26a9e0d4f2 · outbound

This paper cites MMSegmentation : Openmmlab semantic segmentation toolbox and benchmark, 2020.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation MMSegmentation : Openmmlab semantic segmentation toolbox and benchmark, 2020

Reference 16

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Observation 3f57a3f1-7b94-4e6e-8c94-d964b8109b52 · outbound

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

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation ImageNet : A large-scale hierarchical image database

Reference 17

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

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 18

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This paper cites EVA : Exploring the limits of masked visual representation learning at scale.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation EVA : Exploring the limits of masked visual representation learning at scale

Reference 19

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Observation bdbf581d-597c-452a-b522-e5701929ffab · outbound

This paper cites Rethinking Patch Dependence for Masked Autoencoders.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Rethinking Patch Dependence for Masked Autoencoders

Reference 20

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This paper cites MFNet : Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation MFNet : Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes

Reference 21

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Observation d92efad2-dd4e-4a5d-8c52-a0e29d1b083c · outbound

This paper cites Deep residual learning for image recognition.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Deep residual learning for image recognition

Reference 22

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This paper cites Masked autoencoders are scalable vision learners.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Masked autoencoders are scalable vision learners

Reference 23

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This paper cites Distilling the Knowledge in a Neural Network.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Distilling the Knowledge in a Neural Network

Reference 24

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This paper cites MILAN: Masked Image Pretraining on Language Assisted Representation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation MILAN: Masked Image Pretraining on Language Assisted Representation

Reference 25

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This paper cites Multispectral pedestrian detection: Benchmark dataset and baselines.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Multispectral pedestrian detection: Benchmark dataset and baselines

Reference 26

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This paper cites LLVIP : A visible-infrared paired dataset for low-light vision.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation LLVIP : A visible-infrared paired dataset for low-light vision

Reference 27

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This paper cites Billion-scale similarity search with gpus.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Billion-scale similarity search with gpus

Reference 28

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Observation b8ae9f8a-63fb-4911-bd4f-afcada548726 · outbound

This paper cites Similarity of neural network representations revisited.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Similarity of neural network representations revisited

Reference 29

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This paper cites RGB-T object tracking: Benchmark and baseline.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation RGB-T object tracking: Benchmark and baseline

Reference 30

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This paper cites Segmenting objects in day and night: Edge-conditioned cnn for thermal image semantic segmentation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Segmenting objects in day and night: Edge-conditioned cnn for thermal image semantic segmentation

Reference 31

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This paper cites LasHeR : A large-scale high-diversity benchmark for rgbt tracking.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation LasHeR : A large-scale high-diversity benchmark for rgbt tracking

Reference 32

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This paper cites Infrared ship database, 2021.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Infrared ship database, 2021

Reference 33

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This paper cites Ni, and Heung-Yeung Shum.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Ni, and Heung-Yeung Shum

Reference 34

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This paper cites On-vehicle visible and infrared object detection database, 2021 b.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation On-vehicle visible and infrared object detection database, 2021 b

Reference 35

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This paper cites Exploring plain vision transformer backbones for object detection.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Exploring plain vision transformer backbones for object detection

Reference 36

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This paper cites Lawrence Zitnick.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Lawrence Zitnick

Reference 37

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This paper cites InfMAE: A Foundation Model in the Infrared Modality.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation InfMAE: A Foundation Model in the Infrared Modality

Reference 38

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This paper cites Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection

Reference 39

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This paper cites Cross-modal collaborative representation learning and a large-scale rgbt benchmark for crowd counting.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Cross-modal collaborative representation learning and a large-scale rgbt benchmark for crowd counting

Reference 40

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This paper cites LSOTB-TIR : A large-scale high-diversity thermal infrared object tracking benchmark.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation LSOTB-TIR : A large-scale high-diversity thermal infrared object tracking benchmark

Reference 41

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This paper cites General-purpose dual-sensor (infrared/visible) video database, 2021 b.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation General-purpose dual-sensor (infrared/visible) video database, 2021 b

Reference 42

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This paper cites Infrared aerial photography database, 2021 c.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Infrared aerial photography database, 2021 c

Reference 43

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This paper cites Exploring target representations for masked autoencoders.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Exploring target representations for masked autoencoders

Reference 44

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This paper cites Fully convolutional networks for semantic segmentation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 45

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This paper cites Seasons in Drift : A long term thermal imaging dataset for studying concept drift.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Seasons in Drift : A long term thermal imaging dataset for studying concept drift

Reference 46

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UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Unresolved cited work

Reference 47

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This paper cites Multi-modal rgb--depth--thermal human body segmentation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Multi-modal rgb--depth--thermal human body segmentation

Reference 48

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This paper cites What do self-supervised vision transformers learn? In ICLR, 2023.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation What do self-supervised vision transformers learn? In ICLR, 2023

Reference 49

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This paper cites PyTorch : An imperative style, high-performance deep learning library.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation PyTorch : An imperative style, high-performance deep learning library

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This paper cites Mathematical contributions to the theory of evolution.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Mathematical contributions to the theory of evolution

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This paper cites TinyMIM : An empirical study of distilling mim pre-trained models.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation TinyMIM : An empirical study of distilling mim pre-trained models

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Observation b8557b70-1bbe-418f-a7c4-79c7c4e04d46 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Indoor segmentation and support inference from rgbd images

Reference 53

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Observation 5585b6d4-ffa2-4b9d-91ed-c0bfd6b32d69 · outbound

This paper cites Lichtenberg, and Jianxiong Xiao.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Lichtenberg, and Jianxiong Xiao

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.671574Z

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Observation b62560d3-77f0-4d2f-9e89-b495d7156ad0 · outbound

This paper cites Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.656359Z

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Observation dc57d9a0-fef2-4b26-8e1b-d29728e2490f · outbound

This paper cites Multispectral object detection for autonomous vehicles.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Multispectral object detection for autonomous vehicles

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.640858Z

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Observation cdf34d89-04f0-419b-a641-9621b90a5d1a · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Training data-efficient image transformers & distillation through attention

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.625993Z

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Observation 24dcf3c8-9232-42ed-975f-8f10c53f0e52 · outbound

This paper cites DeiT III : Revenge of the vit.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation DeiT III : Revenge of the vit

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.610742Z

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Observation fb3886ae-5a9e-4381-a228-be8d5c22a7a3 · outbound

This paper cites RGBT Salient Object Detection : A large-scale dataset and benchmark.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation RGBT Salient Object Detection : A large-scale dataset and benchmark

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.594841Z

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Observation 4d10340c-c2fa-4b81-88fb-7270734eccf2 · outbound

This paper cites A closer look at self-supervised lightweight vision transformers.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation A closer look at self-supervised lightweight vision transformers

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.579099Z

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Observation aa3a6241-430d-4488-a2b2-4a3df26092c4 · outbound

This paper cites Unified perceptual parsing for scene understanding.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Unified perceptual parsing for scene understanding

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.562273Z

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Observation 4fe5c991-830b-4b76-8fec-23fb65f2bb6c · outbound

This paper cites Alvarez, and Ping Luo.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Alvarez, and Ping Luo

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.545373Z

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Observation 45ebcea5-3e28-4ab5-867a-09243e22b37f · outbound

This paper cites MCNet : Multi-level correction network for thermal image semantic segmentation of nighttime driving scene.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation MCNet : Multi-level correction network for thermal image semantic segmentation of nighttime driving scene

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.529980Z

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Observation 384da174-4137-4ee1-ad21-b38b3b16f49d · outbound

This paper cites EfficientSAM : Leveraged masked image pretraining for efficient segment anything.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation EfficientSAM : Leveraged masked image pretraining for efficient segment anything

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.513988Z

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Observation 18ec3f43-03d7-4af1-88f5-8cdff0974f56 · outbound

This paper cites ROMA : Cross-domain region similarity matching for unpaired nighttime infrared to daytime visible video translation.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation ROMA : Cross-domain region similarity matching for unpaired nighttime infrared to daytime visible video translation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.497385Z

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Observation 360d50f3-6327-4913-a6af-309e279a7c35 · outbound

This paper cites Visible-Thermal UAV Tracking : A large-scale benchmark and new baseline.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Visible-Thermal UAV Tracking : A large-scale benchmark and new baseline

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.481219Z

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Observation 1973faa9-78e6-407e-8d78-26e34bf1277b · outbound

This paper cites PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images

Reference 67

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Observation 83b43470-89aa-424c-ad84-e236e4f08cd3 · outbound

This paper cites Pyramid scene parsing network.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Pyramid scene parsing network

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:45.964216Z

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Observation c916ab5f-7fcb-487d-85ce-1029413e4ba8 · outbound

This paper cites Scene parsing through ade20k dataset.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Scene parsing through ade20k dataset

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:45.969335Z

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Observation e04844d9-d443-4890-a4da-8f33c53c49b0 · outbound

This paper cites Image BERT pre-training with online tokenizer.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Image BERT pre-training with online tokenizer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:53:46.445323Z

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Observation da568c67-33a8-4a31-8ca5-c3e4b8cfd93a · outbound

This paper cites write newline.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation write newline

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:45.978460Z

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Observation 7da20a57-80d2-4d86-be75-07a34ab68012 · outbound

This paper cites @esa (Ref.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation @esa (Ref

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:45.983743Z

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Observation a2877dbe-e442-48f4-b834-81ba023d7042 · outbound

This paper cites an unresolved cited work.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:45.988839Z

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Observation 986f4085-e807-449d-89f8-17c94dba445d · outbound

This paper cites Therefore, we focus on enhancing small pre-trained models by introducing a comprehensive framework, UNIP, and validating its effectiveness through extensive experiments.

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation Therefore, we focus on enhancing small pre-trained models by introducing a comprehensive framework, UNIP, and validating its effectiveness through extensive experiments

Reference 74

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