Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation | Paper Citation Record

Observation 00d54f01-a89a-4299-9870-930f2858c8fe · outbound

This paper cites Fully convolu- tional networks for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Fully convolu- tional networks for semantic segmentation,

Reference 1

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Observation 961dedd0-0839-4c5e-a523-da7b54497b97 · outbound

This paper cites Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,

Reference 2

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Observation 89d74679-d60b-4598-9872-923cc3a31c11 · outbound

This paper cites Frozen is better than learning: A new design of prototype- based classifier for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Frozen is better than learning: A new design of prototype- based classifier for semantic segmentation,

Reference 3

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Observation b5f7e53f-996b-4234-8a3c-be9f6eec5ad6 · outbound

This paper cites Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation,

Reference 4

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Observation de2a37fa-ebb5-4d43-97be-c10566395a0a · outbound

This paper cites Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation

Reference 5

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Observation 77f8b6da-ac1a-4758-99f9-ec2215b9726f · outbound

This paper cites Distilling efficient vision transformers from cnns for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Distilling efficient vision transformers from cnns for semantic segmentation,

Reference 6

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Observation 8af2e439-8e9e-49c4-9667-56df0a6dfc9a · outbound

This paper cites Deep residual learn- ing for image recognition,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Deep residual learn- ing for image recognition,

Reference 7

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Observation a4341ad9-4d86-4ea2-bd4b-b712b0a5d976 · outbound

This paper cites Attention is all you need,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Attention is all you need,

Reference 8

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Observation 9b150596-a29b-4cd3-8e2e-b9ab181facb4 · outbound

This paper cites Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation,

Reference 9

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Observation 956fbbf4-33b9-44c3-ae56-1963cac9f7c9 · outbound

This paper cites Zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Zero-shot semantic segmentation,

Reference 10

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Observation 32d42c23-2a55-40a1-99af-0b4264627ab4 · outbound

This paper cites Context- aware feature generation for zero-shot semantic segmen- tation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Context- aware feature generation for zero-shot semantic segmen- tation,

Reference 11

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Observation d730324e-dd53-45ba-89f9-e9af4360fe88 · outbound

This paper cites Semantic projection network for zero-and few-label semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Semantic projection network for zero-and few-label semantic segmentation,

Reference 12

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Observation 7775a045-782e-46ec-ab36-22ebb42f7fae · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Learning transferable visual models from natural language supervision,

Reference 13

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Observation 1aeca0f8-6a16-4169-82b5-3257e73ee3c4 · outbound

This paper cites Segment anything,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Segment anything,

Reference 14

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Observation eb37aca6-1bbb-41b4-800b-550149e2de59 · outbound

This paper cites Language-driven semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Language-driven semantic segmentation,

Reference 15

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Observation bb0592bc-a8a3-4c15-b326-d9daf3a30a56 · outbound

This paper cites Scaling open- vocabulary image segmentation with image-level labels,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Scaling open- vocabulary image segmentation with image-level labels,

Reference 16

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Observation 3366cb06-d663-44a6-b62f-a5121e6cb74b · outbound

This paper cites Zegclip: Towards adapting clip for zero-shot semantic segmen- tation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Zegclip: Towards adapting clip for zero-shot semantic segmen- tation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:02.675875Z

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Observation aaaefa3a-2f47-4b3d-8420-298ec6f0472c · outbound

This paper cites Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:02.542189Z

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Observation e859b7fa-0759-4b11-beef-fe35dda0875f · outbound

This paper cites Global knowl- edge calibration for fast open-vocabulary segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Global knowl- edge calibration for fast open-vocabulary segmentation,

Reference 19

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Observation 7f2f0399-f6a6-4bac-9fc1-7ec4b8363b48 · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Open-vocabulary panoptic segmentation with text-to-image diffusion models,

Reference 20

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Observation 557f49bd-7402-4ca3-a8a2-3c03a9b7d31a · outbound

This paper cites CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation

Reference 21

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Observation 1640e186-f6a8-43df-8bd2-1406814eba3c · outbound

This paper cites Extract free dense labels from clip,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Extract free dense labels from clip,

Reference 22

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Observation 3cc6b57e-4926-4b35-a24d-af1ddf47d151 · outbound

This paper cites Sclip: Rethinking self- attention for dense vision-language inference,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Sclip: Rethinking self- attention for dense vision-language inference,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:01.862484Z

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Observation 686ba4bc-039a-4dfc-b692-9ec19c6bda68 · outbound

This paper cites Open- vocabulary semantic segmentation with decoupled one- pass network,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Open- vocabulary semantic segmentation with decoupled one- pass network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:01.732698Z

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Observation 303bffe8-50fe-49d8-9086-aa38a639c99c · outbound

This paper cites Learning Mask-aware CLIP Representations for Zero-Shot Segmentation.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Learning Mask-aware CLIP Representations for Zero-Shot Segmentation

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:17:54.765023Z

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Observation 6aecd422-fc1e-43b6-8844-fc4ee91feedf · outbound

This paper cites A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:01.539593Z

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Observation 7754982f-6748-4600-85ae-375026fbfe93 · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 27

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Observation 79adfb4f-d1c2-40a5-b273-fe012651ed8a · outbound

This paper cites Per-pixel classi- fication is not all you need for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Per-pixel classi- fication is not all you need for semantic segmentation,

Reference 28

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Observation 242f59d3-3e99-453e-a17b-c9b33bf80062 · outbound

This paper cites CLIP-to-seg distillation for inductive zero- shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation CLIP-to-seg distillation for inductive zero- shot semantic segmentation,

Reference 29

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Observation 86923bbf-ccf5-4c5b-a9e5-e96c9124305b · outbound

This paper cites Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation

Reference 30

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Observation c701391f-9fbd-45c0-a3dd-6e9987843ad0 · outbound

This paper cites Split matching for inductive zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Split matching for inductive zero-shot semantic segmentation,

Reference 31

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Observation 8e1c273b-7338-45da-88d4-416ccc4dbf91 · outbound

This paper cites Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:17:54.427094Z

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Observation e85a14e3-892f-46ec-826b-2a52df9c91ee · outbound

This paper cites 360sfuda++: Towards source-free uda for panoramic segmentation by learning reliable category prototypes,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation 360sfuda++: Towards source-free uda for panoramic segmentation by learning reliable category prototypes,

Reference 33

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Observation 415ab412-d058-461a-bf9d-3055f6c29e3b · outbound

This paper cites Adversarial co-training for semantic segmen- tation over medical images,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Adversarial co-training for semantic segmen- tation over medical images,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:00.751310Z

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Observation 6f6695e2-a76d-4efc-a73a-7046fd90f7e5 · outbound

This paper cites Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation,

Reference 35

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Observation ff455d1c-bf64-4572-be01-6faf5495432c · outbound

This paper cites Encoder-decoder with atrous separable convo- lution for semantic image segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Encoder-decoder with atrous separable convo- lution for semantic image segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:00.444048Z

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Observation 93c33dd1-5d6b-4b59-994f-809ae4ed2cff · outbound

This paper cites Semantic matters: A constrained approach for zero-shot video action recognition,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Semantic matters: A constrained approach for zero-shot video action recognition,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:00.316962Z

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Observation 8381aa8d-f5c9-4ac9-b872-06633abc560e · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:18:00.136005Z

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Observation cb78cee3-1aa4-4750-8b8a-681246f9bc37 · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

Reference 39

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Observation 6ec91391-e855-46b1-98c6-ee382036decd · outbound

This paper cites Unified perceptual parsing for scene understanding,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Unified perceptual parsing for scene understanding,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:59.692806Z

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Observation 2ba8f280-0d1a-4dbb-ba56-d30f218d391b · outbound

This paper cites Uncertainty teacher with dense focal loss for semi- supervised medical image segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Uncertainty teacher with dense focal loss for semi- supervised medical image segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:59.532301Z

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Observation d54dbfce-6363-4401-9ba9-bb641a10bb20 · outbound

This paper cites A good student is cooperative and reliable: Cnn-transformer collaborative learning for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation A good student is cooperative and reliable: Cnn-transformer collaborative learning for semantic segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:59.354123Z

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Observation 34d46e9a-aebe-4f2e-8b12-ebfd923f0a5f · outbound

This paper cites Semantics distortion and style matter: Towards source- free uda for panoramic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Semantics distortion and style matter: Towards source- free uda for panoramic segmentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:59.175122Z

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Observation 553203b0-aa43-46ea-bc7a-e430fc23c5d1 · outbound

This paper cites Transformer-cnn cohort: Semi-supervised semantic seg- mentation by the best of both students,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Transformer-cnn cohort: Semi-supervised semantic seg- mentation by the best of both students,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:59.010928Z

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Observation 9fbcb22c-a268-44e2-9192-ab65d2e6b950 · outbound

This paper cites Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:58.866201Z

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Observation 985dcbcf-4171-43ef-ab43-8c19d9be16cf · outbound

This paper cites GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation

Reference 46

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Observation 161b15ad-a21e-4522-b611-8a90091cb26e · outbound

This paper cites Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts

Reference 47

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Observation 0da8220a-4036-45b8-a42a-2760bd49ef5b · outbound

This paper cites Omnisam: Omnidirectional seg- ment anything model for uda in panoramic semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Omnisam: Omnidirectional seg- ment anything model for uda in panoramic semantic segmentation,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.856546Z

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Observation 17a961f8-717d-4a6a-b18e-501c475a9528 · outbound

This paper cites MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:17:54.229252Z

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Observation caa46ea0-98c8-4465-bb53-e0b51437e06c · outbound

This paper cites Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.866959Z

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Observation 0523980e-47e1-4ab9-847e-56598d3eed4c · outbound

This paper cites Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:17:54.171444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7366fbf-1f2c-4990-802e-5f6e0796ad7f · outbound

This paper cites Non-local neural networks,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Non-local neural networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:58.723246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87d42d82-5f6a-46e5-8560-55c6c7d39617 · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.887876Z

Source-reported events for the cited work

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Observation 25c80cad-d9a7-4fea-9149-df2aa4c4794b · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:58.533682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a8ca1de-e5d1-4b5f-9b45-fe1ef648d6a3 · outbound

This paper cites A good student is cooperative and reliable: Cnn-transformer JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 13 collaborative learning for semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation A good student is cooperative and reliable: Cnn-transformer JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 13 collaborative learning for semantic segmentation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:58.355430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ce1be30-8175-4d69-8c3f-bb7b692075aa · outbound

This paper cites Language-driven visual consensus for zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Language-driven visual consensus for zero-shot semantic segmentation,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.903092Z

Source-reported events for the cited work

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Observation 2ae4fef4-e8c3-4902-b837-645c105d896a · outbound

This paper cites Zero-shot learning with semantic output codes,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Zero-shot learning with semantic output codes,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:58.151041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82a177f0-1c2f-49df-86ee-15306df34a49 · outbound

This paper cites Decoupling zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Decoupling zero-shot semantic segmentation,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.912789Z

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Observation b0bbd6b2-9eec-4d19-b71a-a36868fa589a · outbound

This paper cites A closer look at self-training for zero- label semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation A closer look at self-training for zero- label semantic segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.951972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a4c8465f-c04a-4290-8c84-6767f117c72f · outbound

This paper cites Towards open vocabulary learning: A survey,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Towards open vocabulary learning: A survey,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.795801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fabba1e3-4d76-4977-9ccb-b88eb4caa266 · outbound

This paper cites Segment and recognize anything at any granularity,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Segment and recognize anything at any granularity,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.603422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 150c84fd-125c-4529-9ee8-7dd9a28e3eb9 · outbound

This paper cites Dst-det: Simple dynamic self-training for open- vocabulary object detection,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Dst-det: Simple dynamic self-training for open- vocabulary object detection,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.416368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af804dc5-09ef-42bc-b407-df8d7be170b1 · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Coco-stuff: Thing and stuff classes in context,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.265759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c991e381-ebb6-4530-bb64-b3dda450d881 · outbound

This paper cites Scene parsing through ade20k dataset,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Scene parsing through ade20k dataset,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:57.102462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1599898-7666-4247-bec5-d3691d1c624b · outbound

This paper cites Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.981164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03970648-f086-4acf-82f1-3cfb53a9b160 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Distilling the Knowledge in a Neural Network

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:53.956180Z

Source-reported events for the cited work

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Observation 25bc4745-3600-4bf8-8517-165df4d0d971 · outbound

This paper cites Mawkdn: A multimodal fusion wavelet knowledge distillation approach based on cross-view attention for action recognition,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Mawkdn: A multimodal fusion wavelet knowledge distillation approach based on cross-view attention for action recognition,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.863961Z

Source-reported events for the cited work

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Observation 3c6fe268-79ee-4d59-ac16-91e71d2c236f · outbound

This paper cites Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.749112Z

Source-reported events for the cited work

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Observation f720f9ee-c565-4229-b7d4-c6d086529dfc · outbound

This paper cites Distilling detr with visual-linguistic knowledge for open-vocabulary object detection,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Distilling detr with visual-linguistic knowledge for open-vocabulary object detection,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.654982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54ec7f5a-90c2-4170-ab9f-f3be00924fd6 · outbound

This paper cites Self pseudo entropy knowledge distillation for semi-supervised semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Self pseudo entropy knowledge distillation for semi-supervised semantic segmentation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.525070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbecdd89-5552-48a2-9d22-d07bfbf0c891 · outbound

This paper cites CLIPSelf: Vision transformer distills itself for open-vocabulary dense prediction,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation CLIPSelf: Vision transformer distills itself for open-vocabulary dense prediction,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.374021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a492142d-fd47-42f7-849a-3898ff3de535 · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Open-vocabulary object detection via vision and language knowledge distillation,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.273060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c56292a7-55b5-4542-8f2d-f7c6a66af207 · outbound

This paper cites Primitive generation and semantic-related alignment for universal zero-shot seg- mentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Primitive generation and semantic-related alignment for universal zero-shot seg- mentation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.167735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 99e6eb73-8a35-4b8e-910a-74ba337c1dab · outbound

This paper cites Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:56.055744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95999976-8674-49ba-8564-6efa08ed5aa3 · outbound

This paper cites FROSTER: Frozen CLIP is a strong teacher for open-vocabulary ac- tion recognition,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation FROSTER: Frozen CLIP is a strong teacher for open-vocabulary ac- tion recognition,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.935194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e68cb473-2de7-48e7-b6f3-962e468909a4 · outbound

This paper cites Focal loss for dense object detection,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Focal loss for dense object detection,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:54.015374Z

Source-reported events for the cited work

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Observation d0b88ed8-e1e5-48f0-863b-a69576cce9aa · outbound

This paper cites Exploiting a joint embedding space for generalized zero-shot semantic seg- mentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Exploiting a joint embedding space for generalized zero-shot semantic seg- mentation,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.831638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 90b94c64-b5b2-47d0-9311-3c466da49105 · outbound

This paper cites Ex- ploring regional clues in clip for zero-shot semantic seg- mentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Ex- ploring regional clues in clip for zero-shot semantic seg- mentation,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.749777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8329002-6ea4-4674-9470-9532cbbfc93e · outbound

This paper cites Otseg: Multi-prompt sinkhorn attention for zero-shot semantic segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Otseg: Multi-prompt sinkhorn attention for zero-shot semantic segmentation,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.623317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d8af515d-cc35-4c35-866f-7fafdf7f5f9f · outbound

This paper cites Freeseg: Unified, universal and open-vocabulary image segmentation,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Freeseg: Unified, universal and open-vocabulary image segmentation,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.494113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81d6b29c-529e-4fed-be35-c4583ff1a7d9 · outbound

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

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation The role of context for object detection and semantic segmentation in the wild,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.398287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 679ba8b4-2f31-4784-9786-2e73fedf49a5 · outbound

This paper cites Mmsegmentation: Openmmlab seman- tic segmentation toolbox and benchmark,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Mmsegmentation: Openmmlab seman- tic segmentation toolbox and benchmark,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.299943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25985df0-95d3-43de-9aa8-cc2391b00c03 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:54.053119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5f78d8d-b065-4a74-96b4-852807f8f5b3 · outbound

This paper cites Do vision transformers see like convolu- tional neural networks?.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Do vision transformers see like convolu- tional neural networks?

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.189488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e20c55d6-6255-4897-831d-9c5a8873a28f · outbound

This paper cites Similarity of neural network representations revisited,.

Partial CLIP is Enough: Chimera-Seg for Zero-shot Semantic Segmentation Similarity of neural network representations revisited,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:17:55.065498Z

Source-reported events for the cited work

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Outbound references

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