SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation | Paper Citation Record

Observation 0f65f269-66bc-4bb9-a50b-2534b81fe89c · outbound

This paper cites Xcit: Cross-covariance image transformers.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Xcit: Cross-covariance image transformers

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.432532Z

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Observation cf57f878-fd10-435d-aca7-88bc56fe3beb · outbound

This paper cites Medical image segmentation review: The suc- cess of u-net.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Medical image segmentation review: The suc- cess of u-net

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.446167Z

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Observation 609ada32-f649-4bab-b045-28e61b07d945 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Coco- stuff: Thing and stuff classes in context

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.410831Z

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Observation fbb210dc-14d9-45f0-ab98-e8873d7931e3 · outbound

This paper cites Sdpt: Semantic- aware dimension-pooling transformer for image segmenta- tion.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Sdpt: Semantic- aware dimension-pooling transformer for image segmenta- tion

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.418997Z

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Observation 806f7dc2-2ef4-4305-bdd2-4e14aa75eeeb · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Pem: Prototype-based efficient maskformer for image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.476069Z

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Observation c1c10957-3323-416e-a670-764ebc81395e · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-23T17:05:43.006210Z

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Observation 45655930-2eef-4c05-9724-7eebca2eb955 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.387494Z

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Observation cb3424a6-b177-4d88-9665-4a00d270bc84 · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.366393Z

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Observation 6b4117a1-5fb5-4d77-bc8b-6eceabf8bdb5 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.407925Z

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Observation cf146dd5-8f67-4d4e-9c81-9a512559632a · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.356022Z

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Observation 96ab9228-9061-4f1e-bfe9-3702829828bc · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.468249Z

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Observation de2bd12d-59fb-4228-b650-7597596e8832 · outbound

This paper cites Boundary-aware feature propa- gation for scene segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Boundary-aware feature propa- gation for scene segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.465543Z

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Observation 456dc8a2-a431-48da-b97e-bbabcdc1d239 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-23T17:05:43.009221Z

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Observation 75bf94d8-4a77-47c9-b2a8-3b2b0687c78a · outbound

This paper cites Dual attention network for scene segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Dual attention network for scene segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.457548Z

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Observation bbf2f8c8-711b-4f62-9694-2bfd8859f23f · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Cmt: Convolutional neural networks meet vision transformers

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.413568Z

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Observation 2db095d4-7a1a-4121-80ad-f9fd950ec0bd · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Segnext: Rethink- ing convolutional attention design for semantic segmenta- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.451849Z

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Observation df58de48-f16f-4f88-a5da-803088ebd763 · outbound

This paper cites Adaptive pyramid context network for semantic seg- mentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Adaptive pyramid context network for semantic seg- mentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.448954Z

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Observation e5de0e47-edc8-4dc8-81fa-0929a2ba7c1f · outbound

This paper cites Pas- cal voc 2008 challenge.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Pas- cal voc 2008 challenge

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.440808Z

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Observation ed727e5c-f5b6-4ef7-ab4e-74a866d6a747 · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Ccnet: Criss-cross attention for semantic segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.345025Z

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Observation d227df92-b2c5-49af-9a66-ccc3f3000a98 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Metaseg: Metaformer-based global contexts-aware network for efficient semantic segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.438136Z

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Observation d51841f3-7dba-4eac-8ca8-f00628e7be45 · outbound

This paper cites Segment any- thing.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Segment any- thing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.422691Z

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Observation ab5ebc58-75af-4d13-99d5-650ee3c9c746 · outbound

This paper cites Lisa: Reasoning segmenta- tion via large language model.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Lisa: Reasoning segmenta- tion via large language model

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.429198Z

Source-reported events for the cited work

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Observation a0bac698-a450-43cd-878d-9afc2e4259bd · outbound

This paper cites Semantic image segmenta- tion with deep convolutional nets and fully connected crfs.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Semantic image segmenta- tion with deep convolutional nets and fully connected crfs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.373542Z

Source-reported events for the cited work

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Observation af0007ea-a59a-4776-b8c5-9bb67134a75e · outbound

This paper cites Scale-aware modulation meet transformer.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Scale-aware modulation meet transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.353494Z

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Observation 48ef8945-1ec8-4cba-8ea5-675810be4fa9 · outbound

This paper cites Auto- deeplab: Hierarchical neural architecture search for semantic image segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Auto- deeplab: Hierarchical neural architecture search for semantic image segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.342331Z

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Observation d77ca3c6-67ba-4d20-83b2-c851e38a35c3 · outbound

This paper cites Bpkd: Boundary privileged knowledge distillation for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Bpkd: Boundary privileged knowledge distillation for semantic segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.335166Z

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Observation a8e58ef8-7f33-45a9-ac2d-28a47bc10a9c · outbound

This paper cites Fully convolutional networks for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.347566Z

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Observation c105fbd1-e9ae-44d3-8a93-3daa6ace204e · outbound

This paper cites Efficient Modulation for Vision Networks.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Efficient Modulation for Vision Networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:05:43.003487Z

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Observation 809b711c-b2e9-4625-b1c5-8843964c4cfb · outbound

This paper cites Large kernel matters–improve semantic segmenta- tion by global convolutional network.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Large kernel matters–improve semantic segmenta- tion by global convolutional network

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.358639Z

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Observation 7255b170-affe-4db3-8038-2a2dbddf2161 · outbound

This paper cites A transformer-based decoder for semantic segmentation with multi-level context mining.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation A transformer-based decoder for semantic segmentation with multi-level context mining

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.402756Z

Source-reported events for the cited work

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Observation ffdf8363-69aa-4dd7-8bcf-e1699c951729 · outbound

This paper cites Feedformer: Revisiting transformer decoder for ef- ficient semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Feedformer: Revisiting transformer decoder for ef- ficient semantic segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.416471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2d4f7953-2cf5-40cb-b404-7bf7bec8f6f5 · outbound

This paper cites Segmenter: Transformer for semantic segmenta- tion.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Segmenter: Transformer for semantic segmenta- tion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.483772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f48383f3-739a-4f8f-b502-d8623556146f · outbound

This paper cites Attention is all you need.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Attention is all you need

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.478496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d9799ec2-1339-4627-beb7-f30264457550 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.399087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d741b9e-9bec-43e6-b0a2-6b2e49c19256 · outbound

This paper cites Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.443350Z

Source-reported events for the cited work

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Observation 22fd5e27-d11a-4a1e-9fdb-7ca6420ccab9 · outbound

This paper cites Non-local neural networks.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Non-local neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.392336Z

Source-reported events for the cited work

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Observation 6378f7f3-06f2-4441-b26c-ec7a850d7c5e · outbound

This paper cites Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Vit-comer: Vision transformer with convolu- tional multi-scale feature interaction for dense predictions

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.396085Z

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Observation 92848695-736b-45cd-9fbf-76184154a9d6 · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.384792Z

Source-reported events for the cited work

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Observation ad1341c3-36ac-4ae1-8d4b-cd7560d83942 · outbound

This paper cites Lightweight real-time semantic seg- mentation network with efficient transformer and cnn.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Lightweight real-time semantic seg- mentation network with efficient transformer and cnn

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.454601Z

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Observation 4745f133-2fee-4cef-9723-ce48a3cc7b82 · outbound

This paper cites MacFormer: Semantic Segmentation with Fine Object Boundaries.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation MacFormer: Semantic Segmentation with Fine Object Boundaries

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:05:42.999998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 122e0b64-aeb2-43bd-8be7-4399662d41e5 · outbound

This paper cites Sctnet: Single-branch cnn with transformer semantic information for real-time segmen- tation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Sctnet: Single-branch cnn with transformer semantic information for real-time segmen- tation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.425411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2818bfc7-8b85-4812-a0ef-9412174bb156 · outbound

This paper cites Multi-scale rep- resentations by varing window attention for semantic seg- mentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Multi-scale rep- resentations by varing window attention for semantic seg- mentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.363713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 052bdfd9-351a-4401-84cb-d9fa538ee94e · outbound

This paper cites Multi-scale rep- resentations by varying window attention for semantic seg- mentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Multi-scale rep- resentations by varying window attention for semantic seg- mentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.470867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5975b19b-a3d4-4b21-8fed-f019e2dd853c · outbound

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

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:05:43.012381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fe63092a-a82f-4631-afe0-566f697dd19b · outbound

This paper cites Context prior for scene seg- mentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Context prior for scene seg- mentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.360970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 59bb1efb-a77a-4b93-99e8-a721d3202699 · outbound

This paper cites Metaformer is actually what you need for vision.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Metaformer is actually what you need for vision

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.473376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e5d63963-287d-445d-8922-97b21a15ba77 · outbound

This paper cites Object- contextual representations for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Object- contextual representations for semantic segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.481264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3469ab77-9dc6-4c0c-b2ed-5289fdf15725 · outbound

This paper cites Segfix: Model-agnostic boundary refinement for segmenta- tion.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Segfix: Model-agnostic boundary refinement for segmenta- tion

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.376339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 800a44ce-1fb6-4f8c-98ec-c5535e11f829 · outbound

This paper cites Con- text encoding for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Con- text encoding for semantic segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.369188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation efc034a0-9ec3-4ba8-875b-17b3a1fb0b2b · outbound

This paper cites Joint se- mantic segmentation and boundary detection using iterative pyramid contexts.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Joint se- mantic segmentation and boundary detection using iterative pyramid contexts

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.379416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f4d68d5a-b5f8-4bf0-9a0b-9f3a1a1e3c51 · outbound

This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.462975Z

Source-reported events for the cited work

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Observation 267f4a37-fbc5-4180-b694-96911472c8ed · outbound

This paper cites Squeeze-and-attention networks for semantic segmentation.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Squeeze-and-attention networks for semantic segmentation

Reference 52

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Observation a96b05ae-be7a-41dc-ad1a-b6b7f545c6a0 · outbound

This paper cites Scene parsing through ade20k dataset.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Scene parsing through ade20k dataset

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.460256Z

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Observation 4fa71a4e-9b16-4570-ad09-79a8cca97382 · outbound

This paper cites In this Supplementary, we il- lustrate the relationship of the proposed Cross-Layer Block (CLB) to other SOTA attention blocks, as shown in Fig.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation In this Supplementary, we il- lustrate the relationship of the proposed Cross-Layer Block (CLB) to other SOTA attention blocks, as shown in Fig

Reference 54

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Observation 4f71b014-61ee-4998-8b19-df2b460956db · outbound

This paper cites In this Sup- plementary, we present additional experimental comparison conducted with medium-weight and heavy-weight models on ADE20K and Cityscapes.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation In this Sup- plementary, we present additional experimental comparison conducted with medium-weight and heavy-weight models on ADE20K and Cityscapes

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.360443Z

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Observation aed08de0-54b4-4f96-9403-edd3239962f3 · outbound

This paper cites 9 shows additional visual comparison of the segmen- tation results obtained on the Cityscapes datasets using our SCASeg and SOTA methods.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation 9 shows additional visual comparison of the segmen- tation results obtained on the Cityscapes datasets using our SCASeg and SOTA methods

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:05:43.357698Z

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Observation 3afa462f-82f2-41ab-aed1-ef8d3958b288 · outbound

This paper cites an unresolved cited work.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation Unresolved cited work

Reference 57

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