Paper Citation Record · LEDGER

Towards Real-Time Open-Vocabulary Video Instance Segmentation

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2412.04434.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.04434 v1

Coverage vector

measured 61 of 61 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation 3e94c777-44a9-4294-8980-602f10de40f4 · outbound

This paper cites GPT-4 Technical Report.

Towards Real-Time Open-Vocabulary Video Instance Segmentation GPT-4 Technical Report

Reference 1

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Observation a29efce4-5d58-4c6f-989a-397d5e083c5f · outbound

This paper cites Tarvis: A unified approach for target-based video segmentation.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Tarvis: A unified approach for target-based video segmentation

Reference 2

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Observation c84a1983-93bf-4801-929e-80b38bbb6581 · outbound

This paper cites Burst: A benchmark for unifying object recognition, segmentation and tracking in video.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Burst: A benchmark for unifying object recognition, segmentation and tracking in video

Reference 3

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This paper cites Simple online and realtime tracking.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Simple online and realtime tracking

Reference 4

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This paper cites Efficientvit: Lightweight multi-scale attention for high- resolution dense prediction.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Efficientvit: Lightweight multi-scale attention for high- resolution dense prediction

Reference 5

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Observation 7c28fd84-81e1-4892-a348-4f2a5f99f6b9 · outbound

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation End-to- end object detection with transformers

Reference 6

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This paper cites Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model

Reference 7

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Observation f7fa137d-5f9a-4511-977b-38c42d7d3f10 · outbound

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation Yolo-world: Real-time open-vocabulary object detection

Reference 8

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This paper cites Xception: Deep learning with depthwise separable convolutions.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Xception: Deep learning with depthwise separable convolutions

Reference 9

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Observation a7cc4a43-ba9d-4841-95e5-ed1d385f2b77 · outbound

This paper cites Tao: A large-scale benchmark for tracking any object.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Tao: A large-scale benchmark for tracking any object

Reference 10

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Observation 2e78778b-0129-4ca9-a24f-9a61a6957124 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Towards Real-Time Open-Vocabulary Video Instance Segmentation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 11

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This paper cites EVA-02: A Visual Representation for Neon Genesis.

Towards Real-Time Open-Vocabulary Video Instance Segmentation EVA-02: A Visual Representation for Neon Genesis

Reference 12

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This paper cites Ultralyt- ics yolov8.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Ultralyt- ics yolov8

Reference 13

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Observation a1ff6b3e-ff4a-44e5-a23e-c0318f47520b · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Lvis: A dataset for large vocabulary instance segmentation

Reference 14

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This paper cites Masked autoencoders are scalable vision learners.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Masked autoencoders are scalable vision learners

Reference 15

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This paper cites Deep residual learning for image recognition.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Deep residual learning for image recognition

Reference 16

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This paper cites Scaling Laws for Neural Language Models.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Scaling Laws for Neural Language Models

Reference 17

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This paper cites Segment any- thing.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Segment any- thing

Reference 18

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Observation b75216db-7462-48ae-890b-2a3efddfe9a2 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 19

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This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

Towards Real-Time Open-Vocabulary Video Instance Segmentation The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 20

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This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 21

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This paper cites Grounded language-image pre-training.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Grounded language-image pre-training

Reference 22

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This paper cites Microsoft coco: Common objects in context.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Microsoft coco: Common objects in context

Reference 23

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Decoupled weight decay regularization

Reference 24

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation Hota: A higher order metric for evaluating multi-object tracking

Reference 25

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation Simple open-vocabulary object detection with vision transformers

Reference 26

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Observation a58e9310-84e4-4cba-bc00-795b1ce87eab · outbound

This paper cites Mod- eling context between objects for referring expression under- standing.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Mod- eling context between objects for referring expression under- standing

Reference 27

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Unresolved cited work

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This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 29

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This paper cites Occluded video instance segmentation: A bench- mark.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Occluded video instance segmentation: A bench- mark

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This paper cites Learning transferable visual models from natural language supervi- sion.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Learning transferable visual models from natural language supervi- sion

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation SAM 2: Segment Anything in Images and Videos

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

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This paper cites Urvos: Unified referring video object segmentation network with a large-scale benchmark.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Urvos: Unified referring video object segmentation network with a large-scale benchmark

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Objects365: A large-scale, high-quality dataset for object detection

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Aligning and prompting everything all at once for univer- sal visual perception

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Mobile- clip: Fast image-text models through multi-modal reinforced training

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Towards open-vocabulary video instance segmentation

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Unidentified video objects: A benchmark for dense, open- world segmentation

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Towards Real-Time Open-Vocabulary Video Instance Segmentation General object foundation model for images and videos at scale

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Tinyclip: Clip dis- tillation via affinity mimicking and weight inheritance

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Efficientsam: Leveraged masked image pretraining for efficient segment anything

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Unresolved cited work

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Towards grand unification of object tracking

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This paper cites Universal instance perception as object discovery and retrieval.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Universal instance perception as object discovery and retrieval

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Video instance seg- mentation

Reference 46

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Detclip: Dictionary-enriched visual-concept paralleled pre- training for open-world detection

Reference 47

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This paper cites Modeling context in referring expres- sions.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Modeling context in referring expres- sions

Reference 48

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This paper cites Open-vocabulary object detection using captions.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Open-vocabulary object detection using captions

Reference 49

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This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 50

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Towards Real-Time Open-Vocabulary Video Instance Segmentation Dino: Detr with improved denoising anchor boxes for end-to-end object detection

Reference 51

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This paper cites A simple framework for open-vocabulary segmentation and detection.

Towards Real-Time Open-Vocabulary Video Instance Segmentation A simple framework for open-vocabulary segmentation and detection

Reference 52

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This paper cites Mobileinst: Video in- stance segmentation on the mobile.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Mobileinst: Video in- stance segmentation on the mobile

Reference 53

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Observation fd06e973-41b9-4ee3-9f55-d8689cac4e7e · outbound

This paper cites Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:28:55.319777Z

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Observation 85b4331f-947e-4f74-84bc-55d042a08cd9 · outbound

This paper cites EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss.

Towards Real-Time Open-Vocabulary Video Instance Segmentation EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T21:28:55.016917Z

Source-reported events for the cited work

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Observation dc8f0053-2b15-458b-acad-3b21d14fae45 · outbound

This paper cites Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T21:28:55.021270Z

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Observation 437f8fc6-39ae-42a8-97d9-1023dfa6b882 · outbound

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

Towards Real-Time Open-Vocabulary Video Instance Segmentation Detrs beat yolos on real-time object detection

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T21:28:55.026043Z

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Observation f8db961c-cbfa-4413-90f4-f58632584898 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Detecting twenty-thousand classes using image-level supervision

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:28:55.294472Z

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Observation d1a1aa1c-08fb-4fac-a53f-8ace31c28fbc · outbound

This paper cites Deformable detr: Deformable transformers for end-to-end object detection.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Deformable detr: Deformable transformers for end-to-end object detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:28:55.263466Z

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Observation 987506cd-d1fd-4508-9785-c5d1b9180110 · outbound

This paper cites Generalized decoding for pixel, image, and lan- guage.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Generalized decoding for pixel, image, and lan- guage

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T21:28:55.043436Z

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Observation cac69651-d2c3-48db-83f1-c4c4b25cd614 · outbound

This paper cites an unresolved cited work.

Towards Real-Time Open-Vocabulary Video Instance Segmentation Unresolved cited work

Reference 368

Resolution: parse uncertain; raw_fallback, observed 2026-08-11T21:28:55.279097Z

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Pith citing papers

Observation ba96b6eb-c730-4041-940b-ce05dfa59553 · inbound

OpenFusion++: An Open-vocabulary Real-time Scene Understanding System cites this paper.

OpenFusion++: An Open-vocabulary Real-time Scene Understanding System Towards Real-Time Open-Vocabulary Video Instance Segmentation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-16T06:01:20.339228Z

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