Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection | Paper Citation Record

Observation b8c88ce7-a4e2-4245-9c1a-473ab0eb6fe4 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 1

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Observation 99648745-d955-487b-bd77-ad787169ea21 · outbound

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

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection End-to- end object detection with transformers

Reference 2

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Observation d248a2b5-7328-43c8-b73a-f03f1f156166 · outbound

This paper cites Harmonizing transferability and discriminability for adapting object detectors.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Harmonizing transferability and discriminability for adapting object detectors

Reference 3

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Observation 052f189d-f34b-477d-8b4c-c1ee970597d2 · outbound

This paper cites an unresolved cited work.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Unresolved cited work

Reference 4

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Observation d0c6e4f3-e1fa-43f2-b7ca-04c46fae9f9e · outbound

This paper cites Meta-causal learning for single domain generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Meta-causal learning for single domain generalization

Reference 5

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Observation 3abf5e8d-fd3b-4f51-b21e-9cfa3b7f1bc5 · outbound

This paper cites Learning domain adaptive object detection with probabilistic teacher.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Learning domain adaptive object detection with probabilistic teacher

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.279425Z

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Observation d908cd39-6214-411b-bb8e-e0132743d168 · outbound

This paper cites Center-aware adversarial augmentation for single domain generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Center-aware adversarial augmentation for single domain generalization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.267091Z

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Observation 8fe51a52-2aaf-4e59-9fbd-dcc63febac21 · outbound

This paper cites Domain adaptive faster R-CNN for object detection in the wild.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Domain adaptive faster R-CNN for object detection in the wild

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.254178Z

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Observation 42e29962-9886-41ab-aa0e-1626ef80d52b · outbound

This paper cites RobustNet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection RobustNet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.242102Z

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Observation fab819eb-8fe6-4590-9139-aeaccfafb916 · outbound

This paper cites Attention consistency on visual corruptions for single-source domain generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Attention consistency on visual corruptions for single-source domain generalization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.231335Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1abd49f7-1f84-4d0a-a40b-f50d9ac0b082 · outbound

This paper cites Saquib Sarfraz, and Mohsen Ali.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Saquib Sarfraz, and Mohsen Ali

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.220119Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2fdf022f-dc76-4b70-a475-bbc4a10c09fe · outbound

This paper cites Un- biased mean teacher for cross-domain object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Un- biased mean teacher for cross-domain object detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.208662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 85f7c956-d9c9-4e5c-bd1f-3dfef41f0d7b · outbound

This paper cites D3T: Distinctive dual-domain teacher zigzagging across RGB- thermal gap for domain-adaptive object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection D3T: Distinctive dual-domain teacher zigzagging across RGB- thermal gap for domain-adaptive object detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.197372Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 10945c96-edbe-4ea2-96ef-9284aff44b80 · outbound

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

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.307228Z

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Observation fd83cf6c-d91d-4c71-8d78-5d3afe879950 · outbound

This paper cites The pascal visual object classes (VOC) challenge.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection The pascal visual object classes (VOC) challenge

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.177934Z

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Observation 0f50987d-0b88-4298-9204-f87d2d615bf9 · outbound

This paper cites PØDA: Prompt-driven zero- shot domain adaptation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection PØDA: Prompt-driven zero- shot domain adaptation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.166117Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02bdb2c4-09b0-456a-8ba3-466413931578 · outbound

This paper cites Towards robust ob- ject detection invariant to real-world domain shifts.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Towards robust ob- ject detection invariant to real-world domain shifts

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.154431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 14f5b248-992c-49a3-99c7-ce6129c669b5 · outbound

This paper cites Adversarially adaptive normalization for single domain generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Adversarially adaptive normalization for single domain generalization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.142642Z

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Observation 00742690-15fb-4571-abd3-fd7ac9952e9d · outbound

This paper cites AcroFOD: An adaptive method for cross-domain few-shot object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection AcroFOD: An adaptive method for cross-domain few-shot object detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.130133Z

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Observation a510d9f6-bb37-4ee6-b195-4d541819ebcf · outbound

This paper cites AsyFOD: An asymmetric adaptation paradigm for few-shot domain adaptive object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection AsyFOD: An asymmetric adaptation paradigm for few-shot domain adaptive object detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.118622Z

Source-reported events for the cited work

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Observation befacc59-8c32-45ed-8865-f7b741f791d1 · outbound

This paper cites Gatys, Alexander S.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Gatys, Alexander S

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.106909Z

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Observation 843b96e7-c9ba-475d-8aef-468fc08b2340 · outbound

This paper cites Deep residual learning for image recognition.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Deep residual learning for image recognition

Reference 22

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Observation 45665488-042f-433c-b635-95d5995d5570 · outbound

This paper cites Mask R-CNN.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Mask R-CNN

Reference 23

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Observation 592b1844-9776-424b-a02d-b58fb5de5b99 · outbound

This paper cites Multi-adversarial faster-rcnn for unrestricted object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Multi-adversarial faster-rcnn for unrestricted object detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.074188Z

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Observation ce21655a-da3a-4dcf-a7c7-70e46e154a98 · outbound

This paper cites StyleMix: Sep- arating content and style for enhanced data augmentation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection StyleMix: Sep- arating content and style for enhanced data augmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.062243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation caa864ed-f361-4a9b-b382-8039bc393798 · outbound

This paper cites Mixed samples as probes for unsu- pervised model selection in domain adaptation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Mixed samples as probes for unsu- pervised model selection in domain adaptation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.050211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df7a2b1c-7d15-4f6e-a84a-89417a09b322 · outbound

This paper cites Itera- tive normalization: Beyond standardization towards efficient whitening.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Itera- tive normalization: Beyond standardization towards efficient whitening

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.038433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9220391b-c25e-4e0a-8a64-5105223843e9 · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Arbitrary style transfer in real-time with adaptive instance normalization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.360567Z

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Observation 647836db-447d-420d-af2b-d5451d7b505a · outbound

This paper cites Cross-domain weakly-supervised object de- tection through progressive domain adaptation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Cross-domain weakly-supervised object de- tection through progressive domain adaptation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.364796Z

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Observation b8147039-78c9-43a1-8445-b9b553cfa105 · outbound

This paper cites Diffusemix: Label- preserving data augmentation with diffusion models.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Diffusemix: Label- preserving data augmentation with diffusion models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.368826Z

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Unavailable: canonical work link unavailable.

Observation 4570bbc3-8ef7-49de-91b8-f10ebb5933d2 · outbound

This paper cites Ultralytics YOLOv8, 2023.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Ultralytics YOLOv8, 2023

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:17.003199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e195477-85a2-40d9-b280-c27a4f304fb3 · outbound

This paper cites Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.376395Z

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Observation 679bf784-ab74-409e-bfab-af58f0be8ac0 · outbound

This paper cites an unresolved cited work.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Unresolved cited work

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4fbba91e-927a-4e74-9626-d14fd7918a83 · outbound

This paper cites Puz- zle mix: Exploiting saliency and local statistics for optimal mixup.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Puz- zle mix: Exploiting saliency and local statistics for optimal mixup

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.978929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6211c985-c9c5-47cf-91d3-b74b4894c81c · outbound

This paper cites CLIPstyler: Image style transfer with a single text condition.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection CLIPstyler: Image style transfer with a single text condition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.966572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e3d3167e-1f37-4440-a50a-8579fdfb3d58 · outbound

This paper cites Reed, Cheng-Yang Fu, and Alexander C.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Reed, Cheng-Yang Fu, and Alexander C

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.954449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c7cf43dd-87fa-4de4-a848-93a17f808ba1 · outbound

This paper cites ConfMix: Unsupervised domain adaptation for ob- ject detection via confidence-based mixing.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection ConfMix: Unsupervised domain adaptation for ob- ject detection via confidence-based mixing

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.941775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a2824d2-2701-4f4c-a5cb-54ae23d57191 · outbound

This paper cites Nussbaumer and Henri J.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Nussbaumer and Henri J

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.929372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9400c4f8-f24e-4402-926b-f7aefb46641f · outbound

This paper cites DoubleAUG: Single-domain generalized object detector in urban via color perturbation and dual-style memory.TOMM,.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection DoubleAUG: Single-domain generalized object detector in urban via color perturbation and dual-style memory.TOMM,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.917726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ed18ca7d-7ce9-4759-bb39-7314bb55b89e · outbound

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

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Learning transferable visual models from natural language supervision

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.406793Z

Source-reported events for the cited work

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Observation d5ebf62c-659d-4447-9432-82648adcc833 · outbound

This paper cites YOLO9000: Better, faster, stronger.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection YOLO9000: Better, faster, stronger

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.897529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 42b702c9-1d03-4ba6-853b-e99716bc0ca6 · outbound

This paper cites You only look once: Unified, real-time object de- tection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection You only look once: Unified, real-time object de- tection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.884947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c2ad5ee-36df-4ab2-b9a5-52d8bf1119dd · outbound

This paper cites Faster R-CNN: Towards real-time object detection with re- gion proposal networks.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Faster R-CNN: Towards real-time object detection with re- gion proposal networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.872173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 78258e9d-bd22-4776-911d-0b275525e317 · outbound

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

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection U- Net: Convolutional networks for biomedical image segmen- tation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.860575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ce75ebb4-a7df-4768-b7ba-df3d7c421ea1 · outbound

This paper cites Se- mantic foggy scene understanding with synthetic data.IJCV,.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Se- mantic foggy scene understanding with synthetic data.IJCV,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:16.424989Z

Source-reported events for the cited work

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Observation 4cbb12ee-20bc-45be-a312-28f5e00c3b17 · outbound

This paper cites FCOS: A simple and strong anchor-free object detector.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection FCOS: A simple and strong anchor-free object detector

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.841868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76b2bd66-8982-4c01-bf15-16a7eb9a7920 · outbound

This paper cites DACS: Domain adaptation via cross- domain mixed sampling.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection DACS: Domain adaptation via cross- domain mixed sampling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.830068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bce93beb-fd90-4965-995a-cae321bf4a0b · outbound

This paper cites Manifold mixup: Better representations by interpolating hidden states.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Manifold mixup: Better representations by interpolating hidden states

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.819039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c2365ce2-e075-4584-8e5f-22b6631d642f · outbound

This paper cites CLIP the gap: A single domain generalization approach for object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection CLIP the gap: A single domain generalization approach for object detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.808151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 859ce872-8c17-4159-b52c-3ddc12c499be · outbound

This paper cites Few- shot adaptive faster R-CNN.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Few- shot adaptive faster R-CNN

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.796250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 52dc9472-bd25-4cca-9dc5-59d02fb0939b · outbound

This paper cites Learning to diversify for single do- main generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Learning to diversify for single do- main generalization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.784595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26f6ee15-1374-4d52-b798-1e1e0c55fb63 · outbound

This paper cites Single-domain generalized object detection in urban scene via cyclic-disentangled self- distillation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Single-domain generalized object detection in urban scene via cyclic-disentangled self- distillation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.772897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 96ba6420-e49e-4b9e-99c3-784abf0d37d9 · outbound

This paper cites Vector-decomposed disentanglement for domain- invariant object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Vector-decomposed disentanglement for domain- invariant object detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.761798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 27700269-7703-47f9-b0bc-fdda17333092 · outbound

This paper cites A fourier-based framework for domain generaliza- tion.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection A fourier-based framework for domain generaliza- tion

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.750624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d50aa558-3fe6-451a-a262-e33737dbeae4 · outbound

This paper cites SimDE: A simple domain expan- sion approach for single-source domain generalization.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection SimDE: A simple domain expan- sion approach for single-source domain generalization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.738342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 95022a60-ae36-4a24-8f60-e95a909a4df6 · outbound

This paper cites FDA: Fourier domain adaptation for semantic segmentation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection FDA: Fourier domain adaptation for semantic segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.727538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b862bef-8379-42b1-b8a9-4644dd2bc684 · outbound

This paper cites Zou, and Chelsea Finn.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Zou, and Chelsea Finn

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.717117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2707df5-c01a-4bf3-b72f-87cdad15262a · outbound

This paper cites BDD100K: A diverse driving dataset for heterogeneous multitask learning.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection BDD100K: A diverse driving dataset for heterogeneous multitask learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.705916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 06f389dc-ed49-4d16-a634-58d2cf08be1c · outbound

This paper cites CutMix: Regu- larization strategy to train strong classifiers with localizable features.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection CutMix: Regu- larization strategy to train strong classifiers with localizable features

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.693847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51ae7295-c621-4d5d-a7d6-9c8d9b830f96 · outbound

This paper cites Few-shot cross-domain object detection with instance- level prototype-based meta-learning.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Few-shot cross-domain object detection with instance- level prototype-based meta-learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.682326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2831cca-74e2-4bce-9811-5d928f1c832d · outbound

This paper cites DETRs beat YOLOs on real-time object detection.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection DETRs beat YOLOs on real-time object detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.669453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 15a4753c-0a2a-4314-8800-08c8c73496cc · outbound

This paper cites PICA: Point-wise instance and cen- troid alignment based few-shot domain adaptive object de- tection with loose annotations.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection PICA: Point-wise instance and cen- troid alignment based few-shot domain adaptive object de- tection with loose annotations

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.658021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aa7a660c-23c2-49bf-9ab7-b7a367f0a071 · outbound

This paper cites Mixstyle neural networks for domain generalization and adaptation.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Mixstyle neural networks for domain generalization and adaptation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.645543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 565a7813-614d-41f8-a463-d81796779a6e · outbound

This paper cites Patch-mix trans- former for unsupervised domain adaptation: A game per- spective.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Patch-mix trans- former for unsupervised domain adaptation: A game per- spective

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.629891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4a876b1e-9a85-4d3e-8989-0cf4cbaa5303 · outbound

This paper cites 6, we analyze the sensitivity of the style mixing parameters in the proposed LDDS and pro- vide quantitative results on the daytime-sunny to night- rainy task.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection 6, we analyze the sensitivity of the style mixing parameters in the proposed LDDS and pro- vide quantitative results on the daytime-sunny to night- rainy task

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:25:16.617037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a1bbe978-167c-49ad-bc28-e6f3685f0b12 · outbound

This paper cites Implementation Details In the experiments based on one-stage, two-stage, and transformer-based detectors, the style generation compo- nent adhered to a consistent setup.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Implementation Details In the experiments based on one-stage, two-stage, and transformer-based detectors, the style generation compo- nent adhered to a consistent setup

Reference 66

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:25:16.602527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c626e7fa-de9f-43d3-96a0-1f43b4a6b8d7 · outbound

This paper cites an unresolved cited work.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:25:16.587463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cbd207e9-0ede-4971-a612-87fc4845580a · outbound

This paper cites Structure of the backbone The backbone network serving as the feature extraction module is a critical component of the detector.

Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection Structure of the backbone The backbone network serving as the feature extraction module is a critical component of the detector

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:25:16.574106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers