HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback | Paper Citation Record

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This paper cites XMem++: Production-level video segmenta- tion from few annotated frames.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback XMem++: Production-level video segmenta- tion from few annotated frames

Reference 1

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Observation ffc90e0c-f9ca-4d50-99cc-c79a12a78eb8 · outbound

This paper cites Object segmentation by long term analysis of point trajectories.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Object segmentation by long term analysis of point trajectories

Reference 2

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This paper cites Emerg- ing properties in self-supervised vision transformers.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Emerg- ing properties in self-supervised vision transformers

Reference 3

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This paper cites XMem: long-term video object segmentation with an Atkinson-Shiffrin mem- ory model.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback XMem: long-term video object segmentation with an Atkinson-Shiffrin mem- ory model

Reference 4

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Observation 9aeacfc9-6c40-48b8-a3d1-1b28f23e3eeb · outbound

This paper cites Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion

Reference 5

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Observation 6fc6d2a2-c4ac-4c5d-b836-56d7bf5fde51 · outbound

This paper cites Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation

Reference 6

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Observation 0786f55a-ec9a-4b86-99bd-81cca337d3b3 · outbound

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Putting the object back into video object segmentation

Reference 7

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Observation 267d0047-749b-40ad-979f-c581f8c44d02 · outbound

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 234e5f86-78f8-45c8-9987-b865754d9182 · outbound

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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This paper cites Mask r-cnn.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Mask r-cnn

Reference 10

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Observation 5022a0da-0986-4cec-a4f7-1550058d69f9 · outbound

This paper cites Lvos: A benchmark for long-term video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Lvos: A benchmark for long-term video object segmentation

Reference 11

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This paper cites Determining opti- cal flow.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Determining opti- cal flow

Reference 12

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This paper cites Segment anything in high quality.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Segment anything in high quality

Reference 13

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This paper cites Segment any- thing.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Segment any- thing

Reference 14

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Observation cee4ab2c-36f7-4bba-b1b1-f86087428fd9 · outbound

This paper cites The first visual object tracking segmentation vots2023 chal- lenge results.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback The first visual object tracking segmentation vots2023 chal- lenge results

Reference 15

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Observation 27048a54-5fa7-416e-a5d1-af9ab8ed3505 · outbound

This paper cites The second visual object tracking seg- mentation vots2024 challenge results, 2024.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback The second visual object tracking seg- mentation vots2024 challenge results, 2024

Reference 16

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This paper cites an unresolved cited work.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Unresolved cited work

Reference 17

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This paper cites Video object segmentation with adaptive feature bank and uncertain-region refinement.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Video object segmentation with adaptive feature bank and uncertain-region refinement

Reference 18

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This paper cites Follow anything: Open- set detection, tracking, and following in real-time.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Follow anything: Open- set detection, tracking, and following in real-time

Reference 19

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This paper cites Background sub- traction in highly dynamic scenes.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Background sub- traction in highly dynamic scenes

Reference 20

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This paper cites The cell tracking challenge: 10 years of objective benchmarking.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback The cell tracking challenge: 10 years of objective benchmarking

Reference 21

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Observation 7ba2a628-8d3b-474b-ae08-fb3f38f8be5c · outbound

This paper cites Video object segmentation using space-time memory networks.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Video object segmentation using space-time memory networks

Reference 22

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This paper cites Perazzi, J.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Perazzi, J

Reference 23

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This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback The 2017 DAVIS Challenge on Video Object Segmentation

Reference 25

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Learning transferable visual models from natural language supervi- sion

Reference 26

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This paper cites Deepftsg: Multi-stream asymmetric use-net trellis en- coders with shared decoder feature fusion architecture for video motion segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Deepftsg: Multi-stream asymmetric use-net trellis en- coders with shared decoder feature fusion architecture for video motion segmentation

Reference 27

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback SAM 2: Segment Anything in Images and Videos

Reference 28

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This paper cites Learning fast and robust target models for video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Learning fast and robust target models for video object segmentation

Reference 29

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

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback U- net: Convolutional networks for biomedical image segmen- tation

Reference 30

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This paper cites A practical adap- tive approach for dynamic background subtraction using an invariant colour model and object tracking.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback A practical adap- tive approach for dynamic background subtraction using an invariant colour model and object tracking

Reference 31

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This paper cites Video class agnostic segmentation benchmark for au- tonomous driving.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Video class agnostic segmentation benchmark for au- tonomous driving

Reference 32

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This paper cites Breaking the” object” in video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Breaking the” object” in video object segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:10:41.087274Z

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This paper cites Ensemble deep learning object detection fusion for cell tracking, mitosis, and lineage.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Ensemble deep learning object detection fusion for cell tracking, mitosis, and lineage

Reference 34

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HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Dino-tracker: Taming dino for self-supervised point track- ing in a single video

Reference 35

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This paper cites Attention is all you need.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Attention is all you need

Reference 36

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This paper cites A Comprehensive Review of Modern Object Segmenta- tion Approaches.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback A Comprehensive Review of Modern Object Segmenta- tion Approaches

Reference 37

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This paper cites A learning algorithm for continually running fully recurrent neural networks.Neu- ral computation, 1(2):270–280, 1989.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback A learning algorithm for continually running fully recurrent neural networks.Neu- ral computation, 1(2):270–280, 1989

Reference 38

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HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Unresolved cited work

Reference 39

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This paper cites Youtube-vos: Sequence-to-sequence video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Youtube-vos: Sequence-to-sequence video object segmentation

Reference 40

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HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 41

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This paper cites Learning spatio-temporal transformer for vi- sual tracking.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Learning spatio-temporal transformer for vi- sual tracking

Reference 42

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Observation d6c1bb3b-1986-4f67-9709-dce2cd0b0cbf · outbound

This paper cites Decoupling features in hierar- chical propagation for video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Decoupling features in hierar- chical propagation for video object segmentation

Reference 43

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This paper cites Collaborative video object segmentation by foreground-background inte- gration.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Collaborative video object segmentation by foreground-background inte- gration

Reference 44

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Observation 91bceb3e-a575-4ec4-8fd0-c15aadc82f6c · outbound

This paper cites Associating ob- jects with transformers for video object segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Associating ob- jects with transformers for video object segmentation

Reference 45

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Observation ecf0b8cc-4b54-4ac0-96fe-c5284f412b73 · outbound

This paper cites Collabora- tive video object segmentation by multi-scale foreground- background integration.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Collabora- tive video object segmentation by multi-scale foreground- background integration

Reference 46

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This paper cites Scalable video object segmen- tation with identification mechanism.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Scalable video object segmen- tation with identification mechanism

Reference 47

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Observation 6360d43b-1d38-4ec4-9ec9-42dab251595d · outbound

This paper cites Joint feature learning and relation modeling for tracking: A one-stream framework.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Joint feature learning and relation modeling for tracking: A one-stream framework

Reference 48

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Observation ab1a807d-fc92-45f3-ba16-dad2d30e2154 · outbound

This paper cites Wide Residual Networks.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Wide Residual Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T18:10:40.789908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd846c8-a482-4324-adc5-e97be01a5907 · outbound

This paper cites RMem: Re- stricted Memory Banks Improve Video Object Segmentation.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback RMem: Re- stricted Memory Banks Improve Video Object Segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:10:40.900556Z

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 bf46cf98-5acc-43b6-ad38-1ba39913a79d · outbound

This paper cites Tracking anything in high quality, 2023.

HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback Tracking anything in high quality, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:10:40.888660Z

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