SAM2Auto: Auto Annotation Using FLASH | Paper Citation Record

Observation 141eb0cc-e367-4eac-8545-d8bc078a515d · outbound

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

SAM2Auto: Auto Annotation Using FLASH An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 1

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Observation ca464e71-aefd-46a6-b77a-c4c58ac0942f · outbound

This paper cites Language models are few-shot learners,.

SAM2Auto: Auto Annotation Using FLASH Language models are few-shot learners,

Reference 2

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Observation 4493ab42-bbf1-461d-8b01-d4edd12ec425 · outbound

This paper cites Learning transferable visual models from natural language super- vision,.

SAM2Auto: Auto Annotation Using FLASH Learning transferable visual models from natural language super- vision,

Reference 3

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Observation 269a7ba3-1575-4f2e-be58-7cdc5c292fc0 · outbound

This paper cites Llama: Open and efficient foundation language models,.

SAM2Auto: Auto Annotation Using FLASH Llama: Open and efficient foundation language models,

Reference 4

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Observation 07d54d86-0fcb-4d21-973f-df87082f29c8 · outbound

This paper cites On the opportunities and risks of foundation models,.

SAM2Auto: Auto Annotation Using FLASH On the opportunities and risks of foundation models,

Reference 5

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Observation 36205894-e44e-45fb-ba09-ae733d85476b · outbound

This paper cites Gpt-4 technical report,.

SAM2Auto: Auto Annotation Using FLASH Gpt-4 technical report,

Reference 6

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Observation 7a05e676-81b4-40f4-8f65-b523e842a170 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models,.

SAM2Auto: Auto Annotation Using FLASH Coca: Contrastive captioners are image-text foundation models,

Reference 7

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Observation c4e4e5a6-adbe-4db5-ab6d-7c2189296ae8 · outbound

This paper cites Attention is all you need,.

SAM2Auto: Auto Annotation Using FLASH Attention is all you need,

Reference 8

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Observation cbf1327a-3005-4230-b259-3309f126ff39 · outbound

This paper cites Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,.

SAM2Auto: Auto Annotation Using FLASH Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,

Reference 9

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Observation d3f8c4f1-8d2a-4382-81ee-be0c6736c1c6 · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

SAM2Auto: Auto Annotation Using FLASH Emerging properties in self-supervised vision trans- formers,

Reference 10

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Observation b1d3a19a-b7e5-4862-95d7-2d3a71f2afee · outbound

This paper cites I was surprised by the great response to my recent post on small object detection—thank you for all the dms and shares.

SAM2Auto: Auto Annotation Using FLASH I was surprised by the great response to my recent post on small object detection—thank you for all the dms and shares

Reference 11

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Observation c3517a28-e329-48fd-9ae6-63169e72ea86 · outbound

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

SAM2Auto: Auto Annotation Using FLASH Training data-efficient image transformers & distillation through attention,

Reference 12

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Observation 5e33a63b-3166-47c1-840f-6a5f2c5172e8 · outbound

This paper cites Review of accident detection methods using dashcam videos for autonomous driving vehicles,.

SAM2Auto: Auto Annotation Using FLASH Review of accident detection methods using dashcam videos for autonomous driving vehicles,

Reference 13

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Observation cb8b6d95-f05e-45a7-9732-065599a72f91 · outbound

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

SAM2Auto: Auto Annotation Using FLASH The pascal visual object classes (voc) challenge,

Reference 14

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Observation a9a116e5-7655-4e8f-a4df-64427648107f · outbound

This paper cites Fast r-cnn,.

SAM2Auto: Auto Annotation Using FLASH Fast r-cnn,

Reference 15

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Observation 3eb8f8f1-09a9-4de4-8237-c9b540a3aab6 · outbound

This paper cites Mobilenets: Efficient convolutional neural networks for mobile vision applications,.

SAM2Auto: Auto Annotation Using FLASH Mobilenets: Efficient convolutional neural networks for mobile vision applications,

Reference 16

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Observation e2e4675c-7a3e-4f3c-ad70-33b83dd4d98c · outbound

This paper cites Vision meets robotics: The kitti dataset,.

SAM2Auto: Auto Annotation Using FLASH Vision meets robotics: The kitti dataset,

Reference 17

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Observation 4b663b4c-4a0e-4ff2-8e05-730f09cc1705 · outbound

This paper cites Masked generative distillation,.

SAM2Auto: Auto Annotation Using FLASH Masked generative distillation,

Reference 18

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Observation 2d2772ad-d95d-4746-b2ad-e380bb296ab7 · outbound

This paper cites How to train your vit? data, augmentation, and regular- ization in vision transformers,.

SAM2Auto: Auto Annotation Using FLASH How to train your vit? data, augmentation, and regular- ization in vision transformers,

Reference 19

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Observation 049b3453-746b-4aac-bb7e-30d867ecb36f · outbound

This paper cites Afreeca: Annotation-free counting for all,.

SAM2Auto: Auto Annotation Using FLASH Afreeca: Annotation-free counting for all,

Reference 20

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Observation edc39b9c-1ae5-44b7-914d-7939eed7d367 · outbound

This paper cites Open-vocabulary point-cloud object detection without 3d annota- tion,.

SAM2Auto: Auto Annotation Using FLASH Open-vocabulary point-cloud object detection without 3d annota- tion,

Reference 21

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Observation 33041f99-8c1f-42a0-b604-4abe7663b2c6 · outbound

This paper cites Emernerf: Emergent spatial- temporal scene decomposition via self-supervision,.

SAM2Auto: Auto Annotation Using FLASH Emernerf: Emergent spatial- temporal scene decomposition via self-supervision,

Reference 22

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Observation 233047e2-9342-4cbc-a3b6-f642a7179426 · outbound

This paper cites Stereo4d: Learning how things move in 3d from internet stereo videos,.

SAM2Auto: Auto Annotation Using FLASH Stereo4d: Learning how things move in 3d from internet stereo videos,

Reference 23

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Observation 490b2d99-cafd-4c36-9281-bb337f0a8503 · outbound

This paper cites Open World Object Detection in the Era of Foundation Models.

SAM2Auto: Auto Annotation Using FLASH Open World Object Detection in the Era of Foundation Models

Reference 24

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Observation 0cb95db0-1b79-4fc6-8402-8dd10f385721 · outbound

This paper cites Yolo-world: Real-time open-vocabulary object detection,.

SAM2Auto: Auto Annotation Using FLASH Yolo-world: Real-time open-vocabulary object detection,

Reference 25

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Observation 331f31a7-8fa9-489e-b121-312e93e292af · outbound

This paper cites Sapiens: Foundation for human vision models,.

SAM2Auto: Auto Annotation Using FLASH Sapiens: Foundation for human vision models,

Reference 26

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Observation fc34242f-3b7a-4128-82b8-2e2d46869cbc · outbound

This paper cites Detrs beat yolos on real-time object detection,.

SAM2Auto: Auto Annotation Using FLASH Detrs beat yolos on real-time object detection,

Reference 27

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Observation 88ef4513-b520-4d05-a8aa-59e62f80199d · outbound

This paper cites Dino-x: A unified vision model for open-world object detection and understanding,.

SAM2Auto: Auto Annotation Using FLASH Dino-x: A unified vision model for open-world object detection and understanding,

Reference 28

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Observation bc88f3d9-4476-49d7-985d-b15702220d42 · outbound

This paper cites Prompt-guided detr with roi-pruned masked attention for open-vocabulary object detection,.

SAM2Auto: Auto Annotation Using FLASH Prompt-guided detr with roi-pruned masked attention for open-vocabulary object detection,

Reference 29

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Observation eb7f98fd-a0ce-47ec-b9e4-4834e90f355a · outbound

This paper cites Detclip: Dictionary-enriched visual-concept paralleled pre- training for open-world detection,.

SAM2Auto: Auto Annotation Using FLASH Detclip: Dictionary-enriched visual-concept paralleled pre- training for open-world detection,

Reference 30

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Observation 78bbcda0-b63c-4cd2-b6d2-24c874619e65 · outbound

This paper cites Aligning bag of regions for open-vocabulary object detection,.

SAM2Auto: Auto Annotation Using FLASH Aligning bag of regions for open-vocabulary object detection,

Reference 31

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Observation 53d379cd-3e7a-4d36-bfb5-3fa3c0f89319 · outbound

This paper cites Detclipv2: Scalable open-vocabulary object detection pre-training via word- region alignment,.

SAM2Auto: Auto Annotation Using FLASH Detclipv2: Scalable open-vocabulary object detection pre-training via word- region alignment,

Reference 32

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Observation 53d136b4-1885-484d-88de-d2b98b1ec9d3 · outbound

This paper cites Fmgs: Founda- tion model embedded 3d gaussian splatting for holistic 3d scene understanding,.

SAM2Auto: Auto Annotation Using FLASH Fmgs: Founda- tion model embedded 3d gaussian splatting for holistic 3d scene understanding,

Reference 33

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Observation c79b529f-c13e-4f40-b47a-23f04eb04430 · outbound

This paper cites General object foundation model for images and videos at scale,.

SAM2Auto: Auto Annotation Using FLASH General object foundation model for images and videos at scale,

Reference 34

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Observation 1b1457d4-00fc-4708-8b80-127a2b498948 · outbound

This paper cites Region-aware pretraining for open-vocabulary object detection with vision transformers,.

SAM2Auto: Auto Annotation Using FLASH Region-aware pretraining for open-vocabulary object detection with vision transformers,

Reference 35

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SAM2Auto: Auto Annotation Using FLASH Open- emma: Open-source multimodal model for end-to-end autonomous driving,

Reference 36

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This paper cites Simple open-vocabulary object detection with vision transformers,.

SAM2Auto: Auto Annotation Using FLASH Simple open-vocabulary object detection with vision transformers,

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SAM2Auto: Auto Annotation Using FLASH Exploiting unlabeled data with vision and language models for object detection,

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SAM2Auto: Auto Annotation Using FLASH Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 39

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Observation c693f3e2-d783-4d90-961f-fea6cee403a5 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SAM2Auto: Auto Annotation Using FLASH SAM 2: Segment Anything in Images and Videos

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SAM2Auto: Auto Annotation Using FLASH Slicing aided hyper inference and fine-tuning for small object detection,

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Observation 3afdfa69-558e-4277-b600-d6a4b2d14988 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning,.

SAM2Auto: Auto Annotation Using FLASH Bdd100k: A diverse driving dataset for heterogeneous multitask learning,

Reference 42

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Observation 1146a43c-65ad-4757-a020-b0e33dfc88d0 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

SAM2Auto: Auto Annotation Using FLASH nuscenes: A multimodal dataset for autonomous driving,

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Observation 1ef8f871-3560-4280-9e42-f9e57f6d9558 · outbound

This paper cites Multiple adverse weather conditions adaptation for object detection via causal intervention,.

SAM2Auto: Auto Annotation Using FLASH Multiple adverse weather conditions adaptation for object detection via causal intervention,

Reference 44

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SAM2Auto: Auto Annotation Using FLASH Learning enriched features for real image restoration and enhancement,

Reference 45

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Observation 9916782c-d57d-44f9-adad-5ff847df7855 · outbound

This paper cites You only need 90k parameters to adapt light: A light weight transformer for image enhancement and exposure correction,.

SAM2Auto: Auto Annotation Using FLASH You only need 90k parameters to adapt light: A light weight transformer for image enhancement and exposure correction,

Reference 46

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Observation 45fd805f-bd6b-4f8d-9b54-b05872d043e2 · outbound

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SAM2Auto: Auto Annotation Using FLASH Learning multi-scale photo exposure correction,

Reference 47

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Observation fdd5693c-134c-467b-b7a3-ec5fd9e4025c · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks,.

SAM2Auto: Auto Annotation Using FLASH Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 48

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Observation 8bfb6082-1d7a-4106-a8af-8a277fe8ec47 · outbound

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SAM2Auto: Auto Annotation Using FLASH Track- former: Multi-object tracking with transformers,

Reference 49

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Observation 58c620b3-1d1f-4e50-afe7-66437e7f8221 · outbound

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SAM2Auto: Auto Annotation Using FLASH Boosttrack: boosting the similarity measure and detection confidence for improved multiple object tracking,

Reference 50

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Observation 91e1de73-6aee-4ce5-a655-fda1cc12759d · outbound

This paper cites Bytetrack: Multi-object tracking by associating every de- tection box,.

SAM2Auto: Auto Annotation Using FLASH Bytetrack: Multi-object tracking by associating every de- tection box,

Reference 51

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Observation 0563de1b-60ef-4f71-9e84-9b81bc888e60 · outbound

This paper cites Diffmot: A real-time diffusion-based multiple object tracker with non-linear prediction,.

SAM2Auto: Auto Annotation Using FLASH Diffmot: A real-time diffusion-based multiple object tracker with non-linear prediction,

Reference 52

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Observation 47c2dce8-73e9-482a-a01e-36c85d962dc9 · outbound

This paper cites Simple cues lead to a strong multi-object tracker,.

SAM2Auto: Auto Annotation Using FLASH Simple cues lead to a strong multi-object tracker,

Reference 53

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Observation ca4413ff-5f36-4ade-b6a9-f9d02fe52f41 · outbound

This paper cites Romot: Referring-expression-comprehension open-set multi-object track- ing,.

SAM2Auto: Auto Annotation Using FLASH Romot: Referring-expression-comprehension open-set multi-object track- ing,

Reference 54

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Observation 264d7d5e-3788-4926-8fdc-08ec25309c6e · outbound

This paper cites Samba: Synchronized set-of-sequences modeling for multiple object tracking,.

SAM2Auto: Auto Annotation Using FLASH Samba: Synchronized set-of-sequences modeling for multiple object tracking,

Reference 55

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Observation 66bdfd4a-406c-4fd4-a4de-3e72b105b8b5 · outbound

This paper cites Sparsetrack: Multi-object tracking by performing scene decomposition based on pseudo-depth,.

SAM2Auto: Auto Annotation Using FLASH Sparsetrack: Multi-object tracking by performing scene decomposition based on pseudo-depth,

Reference 56

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Observation b3f76947-01ba-465b-a0e7-da6b5a99dda0 · outbound

This paper cites Unifying short and long- term tracking with graph hierarchies,.

SAM2Auto: Auto Annotation Using FLASH Unifying short and long- term tracking with graph hierarchies,

Reference 57

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Observation 8519e4d7-16f9-4e3b-8c82-fa7895b6775d · outbound

This paper cites Matching anything by segmenting anything,.

SAM2Auto: Auto Annotation Using FLASH Matching anything by segmenting anything,

Reference 58

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Observation 64e0327a-f25e-46ef-8aa2-89728e1e1bcc · outbound

This paper cites Joint modeling of feature, correspondence, and a compressed memory for video object seg- mentation,.

SAM2Auto: Auto Annotation Using FLASH Joint modeling of feature, correspondence, and a compressed memory for video object seg- mentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.811130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef3b0cf6-6090-4e67-9a6e-eb290f128804 · outbound

This paper cites Putting the object back into video object segmentation,.

SAM2Auto: Auto Annotation Using FLASH Putting the object back into video object segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.796553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84caad5f-208f-44c5-958f-9092c957c86e · outbound

This paper cites Tam-vt: Transformation- aware multi-scale video transformer for segmentation and tracking,.

SAM2Auto: Auto Annotation Using FLASH Tam-vt: Transformation- aware multi-scale video transformer for segmentation and tracking,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.780135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 816f34d1-afeb-46a7-a8c0-fa4172a15fde · outbound

This paper cites Videoclick: Video object segmentation with a single click,.

SAM2Auto: Auto Annotation Using FLASH Videoclick: Video object segmentation with a single click,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.764851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e3a3064-84ac-49fb-a252-499cb01b38bf · outbound

This paper cites Tracking anything with decoupled video segmentation,.

SAM2Auto: Auto Annotation Using FLASH Tracking anything with decoupled video segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.749321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c88fc593-8467-45cc-9ecf-6ab7c2b6f38e · outbound

This paper cites Segment anything meets point tracking,.

SAM2Auto: Auto Annotation Using FLASH Segment anything meets point tracking,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.734308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf9cb5f8-cc62-4cdd-a3c7-cb383613eda1 · outbound

This paper cites Occluded video instance segmentation: A benchmark,.

SAM2Auto: Auto Annotation Using FLASH Occluded video instance segmentation: A benchmark,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.719088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89cb77e5-ea89-4f34-88cd-f40f0399b2ec · outbound

This paper cites Towards open-vocabulary video instance segmentation,.

SAM2Auto: Auto Annotation Using FLASH Towards open-vocabulary video instance segmentation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.704843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c50198bd-0d88-499d-89a3-e4185fbb5892 · outbound

This paper cites Dynomo: Online point tracking by dynamic online monoc- ular gaussian reconstruction,.

SAM2Auto: Auto Annotation Using FLASH Dynomo: Online point tracking by dynamic online monoc- ular gaussian reconstruction,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.690240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c606cdf6-5506-483b-bea3-7e55aeb607f5 · outbound

This paper cites Spatialtracker: Tracking any 2d pixels in 3d space,.

SAM2Auto: Auto Annotation Using FLASH Spatialtracker: Tracking any 2d pixels in 3d space,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.675448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f62c37d-8d34-48d2-80e6-c16433b9d558 · outbound

This paper cites Track4gen: Teaching video diffusion models to track points im- proves video generation,.

SAM2Auto: Auto Annotation Using FLASH Track4gen: Teaching video diffusion models to track points im- proves video generation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.661481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4f83091-7b1f-419d-882a-8043b18292de · outbound

This paper cites CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos.

SAM2Auto: Auto Annotation Using FLASH CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:29:57.579631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df8190a-0ce3-423d-aee6-54daa121903b · outbound

This paper cites Omnitracker: Unifying visual object tracking by tracking- with-detection,.

SAM2Auto: Auto Annotation Using FLASH Omnitracker: Unifying visual object tracking by tracking- with-detection,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.647312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 517e82b4-1b50-4f2f-86c8-b954cd74284a · outbound

This paper cites Spam- ming labels: Efficient annotations for the trackers of tomorrow,.

SAM2Auto: Auto Annotation Using FLASH Spam- ming labels: Efficient annotations for the trackers of tomorrow,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.632365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2c475c12-6a97-40d8-ad89-b0752cc4567f · outbound

This paper cites Better call sal: Towards learning to segment anything in lidar,.

SAM2Auto: Auto Annotation Using FLASH Better call sal: Towards learning to segment anything in lidar,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.618924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f3a1dd1-010a-47c2-9401-55bfe408522f · outbound

This paper cites Efficient Track Anything.

SAM2Auto: Auto Annotation Using FLASH Efficient Track Anything

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:29:57.597591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bee51378-5c63-4c9b-aac5-3b0cfdd1a4fc · outbound

This paper cites Ref- ereverything: Towards segmenting everything we can speak of in videos,.

SAM2Auto: Auto Annotation Using FLASH Ref- ereverything: Towards segmenting everything we can speak of in videos,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.605410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69110601-d667-4a93-811f-41e58ed8cffc · outbound

This paper cites Samurai: Adapting segment anything model for zero-shot visual tracking with motion-aware memory,.

SAM2Auto: Auto Annotation Using FLASH Samurai: Adapting segment anything model for zero-shot visual tracking with motion-aware memory,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.590896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f90fa2b-bfb3-4a23-9757-35defb0efd6e · outbound

This paper cites Smite: Segment me in time,.

SAM2Auto: Auto Annotation Using FLASH Smite: Segment me in time,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.575993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69dc21d7-24c2-497e-85ce-b4b2e874e0fb · outbound

This paper cites Deep multiple instance learning for image classification and auto-annotation,.

SAM2Auto: Auto Annotation Using FLASH Deep multiple instance learning for image classification and auto-annotation,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.560826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a447e4e-c188-4eab-8fef-dacf1cb6f137 · outbound

This paper cites Not all labels are equal: Rationalizing the labeling costs for training object detection,.

SAM2Auto: Auto Annotation Using FLASH Not all labels are equal: Rationalizing the labeling costs for training object detection,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.546476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d28c945-c974-453e-a1d6-7f0d3215232d · outbound

This paper cites Scaling open- vocabulary object detection,.

SAM2Auto: Auto Annotation Using FLASH Scaling open- vocabulary object detection,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.531148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3e9f6fd-d633-4cec-a05f-a10bd7794271 · outbound

This paper cites Detclipv3: Towards versatile generative open-vocabulary object detection,.

SAM2Auto: Auto Annotation Using FLASH Detclipv3: Towards versatile generative open-vocabulary object detection,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.516510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89fb9a6d-966e-413f-9ccc-3646726d2494 · outbound

This paper cites Apovis: Automated pixel-level open-vocabulary instance segmenta- tion through integration of pre-trained vision-language models and foundational segmentation models,.

SAM2Auto: Auto Annotation Using FLASH Apovis: Automated pixel-level open-vocabulary instance segmenta- tion through integration of pre-trained vision-language models and foundational segmentation models,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.502773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c25acfe3-242d-4ef9-8b96-4372f40c7895 · outbound

This paper cites Towards real-time open-vocabulary video instance segmentation,.

SAM2Auto: Auto Annotation Using FLASH Towards real-time open-vocabulary video instance segmentation,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.488488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7ae07312-23b3-4818-9881-1d6d7ea6018b · outbound

This paper cites Aide: An automatic data engine for object detection in autonomous driving,.

SAM2Auto: Auto Annotation Using FLASH Aide: An automatic data engine for object detection in autonomous driving,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.473757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79e9f0b0-c5f0-4d54-991f-6e9e8b3ac182 · outbound

This paper cites Automatic labeling of objects from lidar point clouds via trajectory-level re- finement,.

SAM2Auto: Auto Annotation Using FLASH Automatic labeling of objects from lidar point clouds via trajectory-level re- finement,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.458559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a94816a4-697a-4e44-8524-57782251bd82 · outbound

This paper cites Cosmos world foundation model platform for physical ai,.

SAM2Auto: Auto Annotation Using FLASH Cosmos world foundation model platform for physical ai,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.444531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef4443e8-3f9c-43f4-ab77-8e4e6d4a821b · outbound

This paper cites Launch: Auto label images with roboflow,.

SAM2Auto: Auto Annotation Using FLASH Launch: Auto label images with roboflow,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.430444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f0378ed-e1e9-49b9-aebd-ff2086420736 · outbound

This paper cites Semi-supervised open-world object detection,.

SAM2Auto: Auto Annotation Using FLASH Semi-supervised open-world object detection,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.416237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 88c6090c-43b8-40d2-9100-1b30af70dab5 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise,.

SAM2Auto: Auto Annotation Using FLASH A density-based algorithm for discovering clusters in large spatial databases with noise,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.402802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f5e39e2-95ae-4a2b-a62d-8b00f75fe8b7 · outbound

This paper cites Mot16: A benchmark for multi-object tracking,.

SAM2Auto: Auto Annotation Using FLASH Mot16: A benchmark for multi-object tracking,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.388362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 23ae04ab-125f-4eec-8f95-e237a5e09244 · outbound

This paper cites Simple Unsupervised Multi-Object Tracking.

SAM2Auto: Auto Annotation Using FLASH Simple Unsupervised Multi-Object Tracking

Reference 91

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:29:57.900009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d21bd25a-5978-40b3-993f-d519487cd308 · outbound

This paper cites Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking.

SAM2Auto: Auto Annotation Using FLASH Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Reference 92

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:29:57.877145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95a3436c-593d-4113-a857-a8fb5397f545 · outbound

This paper cites Tracking objects as points,.

SAM2Auto: Auto Annotation Using FLASH Tracking objects as points,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.374585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5080f07-314c-4f2f-94e7-b41a25a63ee7 · outbound

This paper cites Mot20: A benchmark for multi object tracking in crowded scenes,.

SAM2Auto: Auto Annotation Using FLASH Mot20: A benchmark for multi object tracking in crowded scenes,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.359120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b524e032-ae8c-4426-bb30-6ebeef504211 · outbound

This paper cites Dance- track: Multi-object tracking in uniform appearance and diverse motion,.

SAM2Auto: Auto Annotation Using FLASH Dance- track: Multi-object tracking in uniform appearance and diverse motion,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.344860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6509b91-f2a5-4c15-b883-ad66031681f0 · outbound

This paper cites Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol,.

SAM2Auto: Auto Annotation Using FLASH Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.330228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d47f9c7-04f0-40d8-ba6c-53a797016299 · outbound

This paper cites Per- formance measures and a data set for multi-target, multi-camera tracking,.

SAM2Auto: Auto Annotation Using FLASH Per- formance measures and a data set for multi-target, multi-camera tracking,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.315467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 245d57dc-c72c-4f0a-88bc-49cf1deabe72 · outbound

This paper cites Hota: A higher order metric for evaluating multi- object tracking,.

SAM2Auto: Auto Annotation Using FLASH Hota: A higher order metric for evaluating multi- object tracking,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.300986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80075588-6396-4742-9571-bf0f4c39c8e0 · outbound

This paper cites Tracking without bells and whistles,.

SAM2Auto: Auto Annotation Using FLASH Tracking without bells and whistles,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.285525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 586c19e9-1e65-48ba-a8c9-8c2a8c2ff125 · outbound

This paper cites Learning a neural solver for multiple object tracking,.

SAM2Auto: Auto Annotation Using FLASH Learning a neural solver for multiple object tracking,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:29:58.270336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers