I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation | Paper Citation Record

Observation bca65d8d-3328-4df8-9289-4d8cc2b356a8 · outbound

This paper cites Rethinking semantic segmentation from a sequence-to- sequence perspective with transformers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Rethinking semantic segmentation from a sequence-to- sequence perspective with transformers,

Reference 1

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Observation 366ddbdf-0ecb-44f5-906d-8855dd2ed4af · outbound

This paper cites Vision transformers: From semantic segmentation to dense prediction,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Vision transformers: From semantic segmentation to dense prediction,

Reference 2

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Observation 8a5553f1-ef26-4b0f-ab5e-dcfaeb1ddd78 · outbound

This paper cites Vision trans- formers for dense prediction,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Vision trans- formers for dense prediction,

Reference 3

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Observation a8c0c54f-ad2d-4e90-a7b9-c50d01d22a0e · outbound

This paper cites Seg- menter: Transformer for semantic segmentation,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Seg- menter: Transformer for semantic segmentation,

Reference 4

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Observation 6e450a04-d1de-42d9-a674-50bf933c0d42 · outbound

This paper cites SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 5

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Observation f1ea6648-0331-44f4-b927-67b6b67ac29c · outbound

This paper cites SegViT: Semantic Segmentation with Plain Vision Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation SegViT: Semantic Segmentation with Plain Vision Transformers

Reference 6

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Observation 46443d01-994b-4665-bee5-47ed2f6415df · outbound

This paper cites Segvitv2: Exploring efficient and continual semantic segmentation with plain vision transformers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Segvitv2: Exploring efficient and continual semantic segmentation with plain vision transformers,

Reference 7

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Observation 85615bb3-a5d1-4681-b6c2-ec9904d1ce13 · outbound

This paper cites Structtoken: Rethinking semantic segmentation with structural prior,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Structtoken: Rethinking semantic segmentation with structural prior,

Reference 8

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Observation 8d083fc1-7fdf-4214-8155-d6f61a0b1ec2 · outbound

This paper cites Per-Pixel Classification is Not All You Need for Semantic Segmentation.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Per-Pixel Classification is Not All You Need for Semantic Segmentation

Reference 9

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Observation 661f9cd1-0667-4ac0-a82c-f93a18a88e5b · outbound

This paper cites Masked-attention mask transformer for uni- versal image segmentation,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Masked-attention mask transformer for uni- versal image segmentation,

Reference 10

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Observation 4134b2d9-36ca-4885-8922-e954a56006ac · outbound

This paper cites Your vit is secretly an image segmentation model,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Your vit is secretly an image segmentation model,

Reference 11

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Observation 8c364d4c-a014-4a95-b6ff-2a88b74432e0 · outbound

This paper cites Attention is all you need,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Attention is all you need,

Reference 12

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Observation 49ff8c98-eafd-4f81-bb6b-602d93bc7237 · outbound

This paper cites Understanding and Overcoming the Challenges of Efficient Transformer Quantization.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 13

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Observation c6e42522-044d-42ec-bef6-a2631fbcf3c0 · outbound

This paper cites A Survey of Techniques for Optimizing Transformer Inference.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation A Survey of Techniques for Optimizing Transformer Inference

Reference 14

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Observation b1b5a77d-3847-47b9-8cf6-a7b776fe5586 · outbound

This paper cites A comprehensive survey on recent model compression and acceleration approaches for deep neural networks and transformers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation A comprehensive survey on recent model compression and acceleration approaches for deep neural networks and transformers,

Reference 15

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Observation 93b4e442-9a15-4853-8e6e-45d3de1d9ea3 · outbound

This paper cites Vision transformers on the edge: A comprehensive survey of model compression and acceler- ation strategies,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Vision transformers on the edge: A comprehensive survey of model compression and acceler- ation strategies,

Reference 16

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Observation 59c41a3c-97cb-426f-95b9-1b160536d12c · outbound

This paper cites Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,

Reference 17

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Observation 34ac7d7a-da21-40d6-a7d2-a131903b1db4 · outbound

This paper cites Rethinking Vision Transformers for MobileNet Size and Speed.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Rethinking Vision Transformers for MobileNet Size and Speed

Reference 18

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Observation a1d01125-70c7-4720-b9ca-122626902fdf · outbound

This paper cites EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers

Reference 19

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Observation 6cc3cdb1-3bcd-4c46-9331-cbdca0ad296b · outbound

This paper cites Mobile-Former: Bridging MobileNet and Transformer.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Mobile-Former: Bridging MobileNet and Transformer

Reference 20

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Observation c75883f9-ab80-4636-b27c-9ae6549f2d87 · outbound

This paper cites Microvit: A vision transformer with low complexity self attention for edge device,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Microvit: A vision transformer with low complexity self attention for edge device,

Reference 21

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Observation 3a6f81df-447a-4c96-b060-d6b431146d83 · outbound

This paper cites A lightweight vision transformer with weighted global average pooling: implications for iomt applications,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation A lightweight vision transformer with weighted global average pooling: implications for iomt applications,

Reference 22

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Observation 28cd8d07-ac30-48ee-b75a-4bea8181733f · outbound

This paper cites Algm: Adaptive local-then-global token merging for efficient semantic segmentation with plain vision trans- formers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Algm: Adaptive local-then-global token merging for efficient semantic segmentation with plain vision trans- formers,

Reference 23

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Observation d9863595-ec6d-4596-9a4d-2769e2eb9426 · outbound

This paper cites Step: Supertoken and early-pruning for efficient semantic segmentation,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Step: Supertoken and early-pruning for efficient semantic segmentation,

Reference 24

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Observation dc96aead-f529-4879-b018-f22110fa75ed · outbound

This paper cites Prune and merge: Efficient token compression for vision transformer with spatial information preserved,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Prune and merge: Efficient token compression for vision transformer with spatial information preserved,

Reference 25

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Observation 155d8e47-60cf-43a3-b0e4-c2603ea84960 · outbound

This paper cites Ucc: A unified cascade compression framework for vision transformer models,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Ucc: A unified cascade compression framework for vision transformer models,

Reference 26

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Observation 204274bb-80e0-4d19-98df-8be9ce76c1a6 · outbound

This paper cites Atom: Adaptive token merging for efficient acceleration of vision transformer,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Atom: Adaptive token merging for efficient acceleration of vision transformer,

Reference 27

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Observation 0b2b802d-b1ab-4165-bb13-74b5bbc91504 · outbound

This paper cites Efficient token pruning in vision transformers using an attention-based multilayer network,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Efficient token pruning in vision transformers using an attention-based multilayer network,

Reference 28

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Observation e8ebb47f-896a-4fbc-ae48-1d388e5faffb · outbound

This paper cites Revisiting token pruning for object detection and instance segmentation,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Revisiting token pruning for object detection and instance segmentation,

Reference 29

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Observation a2a4a448-7408-4c44-a4bc-16fe9b5a6a01 · outbound

This paper cites Adalog: Post-training quantization for vision transform- ers with adaptive logarithm quantizer,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Adalog: Post-training quantization for vision transform- ers with adaptive logarithm quantizer,

Reference 30

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Observation 5c75c491-e397-48f7-a030-d580290d4900 · outbound

This paper cites EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs

Reference 31

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Observation 03acd65d-065d-4318-8e39-baff85d5c491 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 32

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Observation 74fc0b49-8b28-47aa-a47e-c51cc212a414 · outbound

This paper cites PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization

Reference 33

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Observation e341feb0-8e81-4560-8179-b71006a3ddaf · outbound

This paper cites Mixed Non-linear Quantization for Vision Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Mixed Non-linear Quantization for Vision Transformers

Reference 34

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Observation e8e3c47d-9407-45bf-9465-437cb3178034 · outbound

This paper cites Fima- q: Post-training quantization for vision transformers by fisher information matrix approximation,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Fima- q: Post-training quantization for vision transformers by fisher information matrix approximation,

Reference 35

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Observation 947a557e-42ed-4e87-a53a-d3466ac5eb8c · outbound

This paper cites Orq-vit: Outlier resilient post training quantization for vision transformers via outlier decomposition,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Orq-vit: Outlier resilient post training quantization for vision transformers via outlier decomposition,

Reference 36

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Observation 7bd38294-4c1e-47df-8a9e-73f282841ba6 · outbound

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

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Training data-efficient image transformers & distillation through attention

Reference 37

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Observation bc32194d-9a3e-4bbb-8b5d-d43a2c79d265 · outbound

This paper cites Knowledge distillation in vision transformers: A critical review,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Knowledge distillation in vision transformers: A critical review,

Reference 38

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Observation ccb557f0-1837-4cf6-baa2-ba371722d5d2 · outbound

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

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Imagenet: A large-scale hierarchical image database,

Reference 39

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Observation 2c1cd232-c151-43ce-9282-9c934eadebe9 · outbound

This paper cites Available: https://www.sciencedirect.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Available: https://www.sciencedirect

Reference 40

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Observation c6c8a256-2fe0-4be9-b577-01d46755e36f · outbound

This paper cites Nvidia tensorrt,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Nvidia tensorrt,

Reference 41

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Observation 19e377c4-5b63-4357-8f60-f2a1e15bcb40 · outbound

This paper cites TVM: An Automated End-to-End Optimizing Compiler for Deep Learning.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Reference 42

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Observation 912a3476-1fc0-45fb-ac47-cdada0f6cc1b · outbound

This paper cites Onnx runtime,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Onnx runtime,

Reference 43

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Observation 8545f374-c481-42b3-8014-98a2df74e674 · outbound

This paper cites Fq- vit: Post-training quantization for fully quantized vision transformer,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Fq- vit: Post-training quantization for fully quantized vision transformer,

Reference 44

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Observation 8cc0b7d4-9edc-48c8-9208-70f0894644b3 · outbound

This paper cites I-vit: Integer-only quantization for efficient vision transformer inference,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation I-vit: Integer-only quantization for efficient vision transformer inference,

Reference 45

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Observation bec1b3fd-72b0-460a-81dd-9a0531806c6c · outbound

This paper cites RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers

Reference 46

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Observation 488c121c-17ab-4bd2-ab31-93c3915a5958 · outbound

This paper cites Aphq-vit: Post-training quantization with aver- age perturbation hessian based reconstruction for vision transformers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Aphq-vit: Post-training quantization with aver- age perturbation hessian based reconstruction for vision transformers,

Reference 47

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Observation e6d42115-9932-4ba7-8750-1761b9a812e0 · outbound

This paper cites ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers

Reference 48

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Observation 3e268da4-fbde-4aec-8a6a-025ba9e4c7f1 · outbound

This paper cites Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction ,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction ,

Reference 49

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Observation 5934a7f5-7c60-491d-be60-7363b2ae4332 · outbound

This paper cites Towards accurate post-training quantization for vision transformer,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Towards accurate post-training quantization for vision transformer,

Reference 50

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Observation 4104d820-19ee-4000-9a68-1e18a7495156 · outbound

This paper cites Bivit: Extremely compressed binary vision transformers,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Bivit: Extremely compressed binary vision transformers,

Reference 51

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Observation 1ccac3b3-8330-409d-a69d-eab32d4485f9 · outbound

This paper cites Packqvit: faster sub-8-bit vision transformers via full and packed quantization on the mobile,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Packqvit: faster sub-8-bit vision transformers via full and packed quantization on the mobile,

Reference 52

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Observation 613fe485-67c0-420d-b1c0-e1aef3c6c337 · outbound

This paper cites Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity

Reference 53

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Observation 85350947-d16d-4dd9-a58b-1778ccd67bb6 · outbound

This paper cites Model quantization and hardware acceleration for vision transformers: A comprehensive survey,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Model quantization and hardware acceleration for vision transformers: A comprehensive survey,

Reference 54

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Observation d6663b39-84ae-4380-8eaa-95ad5300b5ca · outbound

This paper cites Q-vit: accurate and fully quantized low-bit vision trans- former,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Q-vit: accurate and fully quantized low-bit vision trans- former,

Reference 55

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Observation c742a339-c820-4f68-8e68-54d58c1bab26 · outbound

This paper cites Quantiza- tion and training of neural networks for efficient integer- arithmetic-only inference,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Quantiza- tion and training of neural networks for efficient integer- arithmetic-only inference,

Reference 56

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Observation 52e9cba3-a219-4b94-8c15-d093c81d75c0 · outbound

This paper cites Scene parsing through ade20k dataset,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Scene parsing through ade20k dataset,

Reference 57

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Observation 8b9f6f41-c5c4-47a2-ac77-0375625ae7cd · outbound

This paper cites The cityscapes dataset for semantic urban scene under- standing,.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation The cityscapes dataset for semantic urban scene under- standing,

Reference 58

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I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Cyclical learning rates for training neural networks,

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I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation End-to-end object detection with transformers,

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I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation MMSegmentation: Openmmlab se- mantic segmentation toolbox and benchmark,

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