ContextFormer: Redefining Efficiency in Semantic Segmentation | Paper Citation Record

Observation 46bc04ef-8ae8-4e0b-abc0-a2871bff84d8 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.641274Z

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Observation 4fe85ad3-5741-45a3-81c9-4308415c83de · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

ContextFormer: Redefining Efficiency in Semantic Segmentation Coco- stuff: Thing and stuff classes in context

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.631173Z

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Observation 4ff7c5b3-6978-437d-9ebe-f8a686e8f838 · outbound

This paper cites Pem: Prototype-based efficient maskformer for image segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Pem: Prototype-based efficient maskformer for image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.622056Z

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Observation 1c27b214-fdc9-4332-bb1c-921e427884b0 · outbound

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

ContextFormer: Redefining Efficiency in Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.613195Z

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Observation 5e7045ff-eefd-4580-8bdb-99efbdbcacfe · outbound

This paper cites Deeplabv3+: Encoderde- coder with atrous separable convolution for semantic image segmentation [m].

ContextFormer: Redefining Efficiency in Semantic Segmentation Deeplabv3+: Encoderde- coder with atrous separable convolution for semantic image segmentation [m]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.604749Z

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Observation 6a322c93-3e18-4cf5-a638-626f4c0d7b88 · outbound

This paper cites Mobile- former: Bridging mobilenet and transformer.

ContextFormer: Redefining Efficiency in Semantic Segmentation Mobile- former: Bridging mobilenet and transformer

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.596427Z

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Observation 825c5236-3783-4afa-9f63-404a63e69b86 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.718917Z

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Observation 615ac641-80b9-47f8-a7a4-4f7b0d790056 · outbound

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

ContextFormer: Redefining Efficiency in Semantic Segmentation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.721723Z

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Observation 85636867-fa18-41cd-9192-e5030b6f9ff3 · outbound

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

ContextFormer: Redefining Efficiency in Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.577459Z

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Observation 2cf2f549-a2a4-42e8-88a0-ca83605cf44a · outbound

This paper cites Coatnet: Marrying convolution and attention for all data sizes.

ContextFormer: Redefining Efficiency in Semantic Segmentation Coatnet: Marrying convolution and attention for all data sizes

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.567484Z

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Observation 231c4c8e-9711-4ef8-b2f5-7bff1cddd518 · outbound

This paper cites Scaling vision transformers to 22 billion pa- rameters.

ContextFormer: Redefining Efficiency in Semantic Segmentation Scaling vision transformers to 22 billion pa- rameters

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.558525Z

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Observation 4da06ea8-3730-4686-b3ab-3a0a0c9d4e6a · outbound

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

ContextFormer: Redefining Efficiency in Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.734512Z

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Observation c0fba700-be10-44d4-b50d-e5ead070ea7e · outbound

This paper cites Hr-nas: Searching ef- ficient high-resolution neural architectures with lightweight transformers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Hr-nas: Searching ef- ficient high-resolution neural architectures with lightweight transformers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.544132Z

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Observation 848658a5-a1a2-434d-992f-28e6737b2585 · outbound

This paper cites Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks.

ContextFormer: Redefining Efficiency in Semantic Segmentation Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.534698Z

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Observation 7f6eec5f-adeb-4e7c-8064-741b79ba1d15 · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

ContextFormer: Redefining Efficiency in Semantic Segmentation Repvgg: Making vgg-style convnets great again

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.743465Z

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Observation 0bca0bc5-5df0-43ad-989e-75508ac07a05 · outbound

This paper cites Tinynet: A lightweight, modular, and unified network architecture for the internet of things.

ContextFormer: Redefining Efficiency in Semantic Segmentation Tinynet: A lightweight, modular, and unified network architecture for the internet of things

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.520540Z

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Observation bc9f165c-3414-41b4-a70d-e0a987aebadc · outbound

This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows.

ContextFormer: Redefining Efficiency in Semantic Segmentation Cswin transformer: A general vision transformer backbone with cross-shaped windows

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.511327Z

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Observation efca6f2d-080c-4633-ace5-dd53b6d48e46 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

ContextFormer: Redefining Efficiency in Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.751426Z

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Observation 7e7a4f3c-d09f-4a73-a015-d162154b4540 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

ContextFormer: Redefining Efficiency in Semantic Segmentation Taming transformers for high-resolution image synthesis

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.754750Z

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Observation b3e15c0b-b66b-49a8-96a5-c7e185bdb926 · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference.

ContextFormer: Redefining Efficiency in Semantic Segmentation Levit: a vision transformer in convnet’s clothing for faster inference

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.757396Z

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Observation dc7ab93f-1a70-4d0a-b1a2-795f2114d699 · outbound

This paper cites Cmt: Convolutional neural networks meet vision transformers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Cmt: Convolutional neural networks meet vision transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.489877Z

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Observation 8776d04f-d8c7-43bd-ab6e-167828123b6f · outbound

This paper cites Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion.

ContextFormer: Redefining Efficiency in Semantic Segmentation Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.481708Z

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Observation ab3bd6c5-5c66-434f-aeab-9c377d6f594a · outbound

This paper cites Ghostnet: More features from cheap 9 operations.

ContextFormer: Redefining Efficiency in Semantic Segmentation Ghostnet: More features from cheap 9 operations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.473722Z

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Observation 3b7b280f-49b7-4af3-997b-20d693b9c901 · outbound

This paper cites mask r-cnn,.

ContextFormer: Redefining Efficiency in Semantic Segmentation mask r-cnn,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.464823Z

Source-reported events for the cited work

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Observation e52a5f55-c775-4445-ab24-147b229e090f · outbound

This paper cites Deep residual learning for image recognition.

ContextFormer: Redefining Efficiency in Semantic Segmentation Deep residual learning for image recognition

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.455890Z

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Observation 0d32745e-aac7-485b-8a8e-bb912ff5aa32 · outbound

This paper cites Axial Attention in Multidimensional Transformers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Axial Attention in Multidimensional Transformers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.775330Z

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Observation 2cb7b0da-1c71-4f88-8b7c-1882fe37b1a2 · outbound

This paper cites Searching for mo- bilenetv3.

ContextFormer: Redefining Efficiency in Semantic Segmentation Searching for mo- bilenetv3

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.446354Z

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Observation d523e870-252e-45c3-a07b-506ec5b6c7d1 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

ContextFormer: Redefining Efficiency in Semantic Segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 28

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Observation c3572d09-d25e-4ce1-bfb4-f72d1f199eb5 · outbound

This paper cites Trseg: Trans- former for semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Trseg: Trans- former for semantic segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.438262Z

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Observation 665a89cf-4cb0-427e-a6db-e3d802a277e6 · outbound

This paper cites Metaseg: Metaformer-based global contexts-aware network for efficient semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Metaseg: Metaformer-based global contexts-aware network for efficient semantic segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.429979Z

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Observation e8074a48-93f7-458b-8412-903eaf28b859 · outbound

This paper cites Panoptic feature pyramid networks.

ContextFormer: Redefining Efficiency in Semantic Segmentation Panoptic feature pyramid networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.420827Z

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Observation b8b01e1d-9c46-4995-b3c5-4045d31fde6b · outbound

This paper cites Dfanet: Deep feature aggregation for real-time semantic seg- mentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Dfanet: Deep feature aggregation for real-time semantic seg- mentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.411389Z

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Observation cd32b43d-7eb3-4acb-9a2f-d053a6af07ef · outbound

This paper cites Convmlp: Hierarchical convolutional mlps for vision.

ContextFormer: Redefining Efficiency in Semantic Segmentation Convmlp: Hierarchical convolutional mlps for vision

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.402512Z

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Observation b2cef2dc-d41a-484e-9e51-6acfa915825b · outbound

This paper cites Partial order pruning: for best speed/accuracy trade-off in neural architecture search.

ContextFormer: Redefining Efficiency in Semantic Segmentation Partial order pruning: for best speed/accuracy trade-off in neural architecture search

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.393261Z

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Observation b79a9334-2a91-4125-bc89-c863149dda0d · outbound

This paper cites Efficientformer: Vision transformers at mobilenet speed.

ContextFormer: Redefining Efficiency in Semantic Segmentation Efficientformer: Vision transformers at mobilenet speed

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.383473Z

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Observation 33658867-d8d3-481c-9dea-76df88573169 · outbound

This paper cites Re- thinking vision transformers for mobilenet size and speed.

ContextFormer: Redefining Efficiency in Semantic Segmentation Re- thinking vision transformers for mobilenet size and speed

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.372913Z

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Observation 156377b7-1f9d-47e9-bf0c-e3f4f058ce2f · outbound

This paper cites Microsoft coco: Common objects in context.

ContextFormer: Redefining Efficiency in Semantic Segmentation Microsoft coco: Common objects in context

Reference 37

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Observation bfcedb53-c866-4ec8-942c-78b115cfda25 · outbound

This paper cites Focal loss for dense object detection.

ContextFormer: Redefining Efficiency in Semantic Segmentation Focal loss for dense object detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.358355Z

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Observation 1713214f-3da7-4ccf-8765-75a69b099ab6 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

ContextFormer: Redefining Efficiency in Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.816104Z

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Observation 13162c7e-d830-401a-90b3-8c6c4de78fdd · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

ContextFormer: Redefining Efficiency in Semantic Segmentation Swin transformer v2: Scaling up capacity and resolution

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.342464Z

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Observation bec53f12-8408-48c2-9dac-e9386183eccf · outbound

This paper cites Fully convolutional networks for semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.334409Z

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Observation 9c9d3138-29a9-4bf8-a9ab-72f68038c8a6 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.

ContextFormer: Redefining Efficiency in Semantic Segmentation Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.324654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a370ae29-0aca-488d-bce3-00d361354e5d · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

ContextFormer: Redefining Efficiency in Semantic Segmentation MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.827838Z

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Observation 4b7d7983-24ca-4ce7-8afd-52914d7b8201 · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Separable Self-attention for Mobile Vision Transformers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.830789Z

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Observation 8ccc05f8-59b2-403c-8380-32d020e31f15 · outbound

This paper cites Espnetv2: A light-weight, power ef- ficient, and general purpose convolutional neural network.

ContextFormer: Redefining Efficiency in Semantic Segmentation Espnetv2: A light-weight, power ef- ficient, and general purpose convolutional neural network

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.314779Z

Source-reported events for the cited work

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Observation 94d100e6-5b03-4a10-8f23-71e2cf688850 · outbound

This paper cites Review the state-of-the-art technologies of semantic segmentation based on deep learning.

ContextFormer: Redefining Efficiency in Semantic Segmentation Review the state-of-the-art technologies of semantic segmentation based on deep learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.305639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5784797-4126-4dea-acf3-08006712bc4d · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

ContextFormer: Redefining Efficiency in Semantic Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.296528Z

Source-reported events for the cited work

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Observation 2b66a459-d47c-46cc-af2a-a3a12e957a06 · outbound

This paper cites Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-09T20:52:32.986482Z

Source-reported events for the cited work

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Observation ae8435f5-3ffa-4877-ae14-b3bd08d9eef5 · outbound

This paper cites Edgevits: Competing light-weight cnns on mobile devices with vision transformers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Edgevits: Competing light-weight cnns on mobile devices with vision transformers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.287047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce56c2e4-445b-4475-83ce-97a66d0a7874 · outbound

This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.849312Z

Source-reported events for the cited work

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Observation f7aadef9-b9bc-4b88-91c3-6d23bd5192e7 · outbound

This paper cites Erfnet: Efficient residual factorized convnet for real-time semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Erfnet: Efficient residual factorized convnet for real-time semantic segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.277427Z

Source-reported events for the cited work

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Observation 06649997-c4b0-4e5e-a0ef-2a43fad4a9d0 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

ContextFormer: Redefining Efficiency in Semantic Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.855519Z

Source-reported events for the cited work

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Observation 054c9d28-826f-43a6-8f0e-f4750dd357f8 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

ContextFormer: Redefining Efficiency in Semantic Segmentation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.261541Z

Source-reported events for the cited work

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Observation 7b5fcc8f-2133-42c7-81f3-aeb874178e9c · outbound

This paper cites Ssformer: A lightweight transformer for semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Ssformer: A lightweight transformer for semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.251897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 718222f4-dbca-4b9e-ac6f-84d3867bd560 · outbound

This paper cites Feedformer: Revisiting transformer decoder for efficient semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Feedformer: Revisiting transformer decoder for efficient semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.241864Z

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Observation 1b9f968b-62b6-45a6-b006-2149b73ae7bf · outbound

This paper cites Deep high-resolution representation learning for human pose es- timation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Deep high-resolution representation learning for human pose es- timation

Reference 56

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Observation 235b48a2-7a87-4d04-9365-9b1c6a7a9ff9 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

ContextFormer: Redefining Efficiency in Semantic Segmentation Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 57

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Observation 45d2624c-477e-4665-b4c2-f3c266b55dc6 · outbound

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

ContextFormer: Redefining Efficiency in Semantic Segmentation Training data-efficient image transformers & distillation through at- tention

Reference 58

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Observation f81f2574-65fb-4812-beb1-ee8c110d5651 · outbound

This paper cites Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.219458Z

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Observation 12c09c6b-79bf-4688-889f-4d622fde9f9f · outbound

This paper cites Deep high-resolution repre- sentation learning for visual recognition.

ContextFormer: Redefining Efficiency in Semantic Segmentation Deep high-resolution repre- sentation learning for visual recognition

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.208942Z

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Observation 3e59b35b-e694-427b-88b3-2e92ad738f9b · outbound

This paper cites Rtformer: Effi- cient design for real-time semantic segmentation with trans- former.

ContextFormer: Redefining Efficiency in Semantic Segmentation Rtformer: Effi- cient design for real-time semantic segmentation with trans- former

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.198173Z

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Observation c8750d39-a361-4944-8ae6-0f1a4304b173 · outbound

This paper cites A novel transformer based se- mantic segmentation scheme for fine-resolution remote sens- ing images.

ContextFormer: Redefining Efficiency in Semantic Segmentation A novel transformer based se- mantic segmentation scheme for fine-resolution remote sens- ing images

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.188566Z

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Observation 7d061a8d-d6ea-45d0-b3d1-b12e4dda31a7 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.

ContextFormer: Redefining Efficiency in Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.176991Z

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Observation 145e7115-397a-458b-bf8e-b3a49ea6c189 · outbound

This paper cites U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation

Reference 64

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Observation f578d2d7-f011-4083-96ef-5efc25434e7b · outbound

This paper cites Bisenet: Bilateral segmentation network for real-time semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Bisenet: Bilateral segmentation network for real-time semantic segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.167741Z

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Observation 2c0d11ed-68e2-41b3-88f5-086003eb2737 · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet.

ContextFormer: Redefining Efficiency in Semantic Segmentation Tokens-to-token vit: Training vision transformers from scratch on imagenet

Reference 66

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Observation e47590dc-1e5e-4351-aa7e-b91a9acf238a · outbound

This paper cites Object- contextual representations for semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Object- contextual representations for semantic segmentation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T20:52:32.901187Z

Source-reported events for the cited work

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Observation 919964a3-c2df-4faa-9c1a-10ce36d4511e · outbound

This paper cites In- terleaved group convolutions.

ContextFormer: Redefining Efficiency in Semantic Segmentation In- terleaved group convolutions

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.150319Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 628af007-c6d2-4ddb-a215-1d953079d6ef · outbound

This paper cites Topformer: Token pyramid transformer for mobile semantic segmentation.

ContextFormer: Redefining Efficiency in Semantic Segmentation Topformer: Token pyramid transformer for mobile semantic segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.140101Z

Source-reported events for the cited work

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Observation 3d0a632a-46bd-44a2-86c6-d5f570ceb92f · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

ContextFormer: Redefining Efficiency in Semantic Segmentation Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.130112Z

Source-reported events for the cited work

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Observation 0a7e57bd-7fec-4b6e-a19d-3a113d338f1c · outbound

This paper cites Pyramid scene parsing network.

ContextFormer: Redefining Efficiency in Semantic Segmentation Pyramid scene parsing network

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.120137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9901eadf-3dee-4d49-a39c-523d574c95fa · outbound

This paper cites Icnet for real-time semantic segmenta- tion on high-resolution images.

ContextFormer: Redefining Efficiency in Semantic Segmentation Icnet for real-time semantic segmenta- tion on high-resolution images

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.110841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f857141-cf12-43fe-ac04-959070bd3759 · outbound

This paper cites Scene parsing through 11 ade20k dataset.

ContextFormer: Redefining Efficiency in Semantic Segmentation Scene parsing through 11 ade20k dataset

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.101926Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d4092ebf-24c0-46e5-8e64-b25cdb7fc626 · outbound

This paper cites Rethinking bottleneck structure for efficient mobile network design.

ContextFormer: Redefining Efficiency in Semantic Segmentation Rethinking bottleneck structure for efficient mobile network design

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.091402Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 031b5f8e-6552-482c-84e1-fe9a8113364d · outbound

This paper cites Biformer: Vision transformer with bi-level routing attention.

ContextFormer: Redefining Efficiency in Semantic Segmentation Biformer: Vision transformer with bi-level routing attention

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.082105Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed5fcef9-0212-4e08-8f91-d284450b4582 · outbound

This paper cites As demonstrated in Fig.

ContextFormer: Redefining Efficiency in Semantic Segmentation As demonstrated in Fig

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.072516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ceb2eb99-eba3-4fb5-92e3-54be1156681d · outbound

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ContextFormer: Redefining Efficiency in Semantic Segmentation Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-09T20:52:33.063986Z

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Observation 020a4226-ce03-4011-96bf-bc481f1d8df5 · outbound

This paper cites an unresolved cited work.

ContextFormer: Redefining Efficiency in Semantic Segmentation Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-09T20:52:33.055506Z

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Observation 56c93e3c-1744-4918-88a6-7f260f951c62 · outbound

This paper cites C shows additional visual results of the proposed Con- textFormer model with original images, ground-truth, Top- Former, and ContextFormer (GM E).

ContextFormer: Redefining Efficiency in Semantic Segmentation C shows additional visual results of the proposed Con- textFormer model with original images, ground-truth, Top- Former, and ContextFormer (GM E)

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.045891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89c31889-6707-4458-aa9e-78349f4e6bd8 · outbound

This paper cites However, cer- tain limitations warrant further investigation.

ContextFormer: Redefining Efficiency in Semantic Segmentation However, cer- tain limitations warrant further investigation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:52:33.036062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Outbound references

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