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Paper Citation Record · LEDGER

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2505.19434.

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

pith.paper-citation-record.v1
2505.19434 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:19:17.664872Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T17:53:49.388949Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:16:14.230121Z

Reference resolution

25 of 25 outbound references displayed

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  • verified fuzzy10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43685550-9833-41ae-8ee2-2fe4cc98429c · outbound

This paper cites an unresolved cited work.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Unresolved cited work

Reference 1

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Observation c74a04aa-75e6-4a5d-9ff3-3f2d50b3ec60 · outbound

This paper cites Enhancing vision-language tracking by effectively converting textual cues into visual cues.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Enhancing vision-language tracking by effectively converting textual cues into visual cues

Reference 4

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Observation ad141a57-a1ca-4a3a-87b2-8c54f3186f26 · outbound

This paper cites Dal: A deep depth-aware long-term tracker.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Dal: A deep depth-aware long-term tracker

Reference 10

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Observation 295a6cf8-7faf-4f9e-8b0e-d7e096edc8c1 · outbound

This paper cites Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

Reference 12

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Source-reported events for the cited work

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Observation 0a2a0068-36c4-4ecf-9584-5211f173596c · outbound

This paper cites Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5eb4434e-ca3b-48c6-981e-c21030d14ef3 · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2bf7e2e9-09ec-4a52-9024-4bfb272e4ffb · outbound

This paper cites Removal and selection: Improving rgb-infrared ob- ject detection via coarse-to-fine fusion.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Removal and selection: Improving rgb-infrared ob- ject detection via coarse-to-fine fusion

Reference 16

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Unavailable: canonical work link unavailable.

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Observation e632ef26-3bea-4fb1-b815-585deea2e221 · outbound

This paper cites ODTrack: Online Dense Temporal Token Learning for Visual Tracking.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features ODTrack: Online Dense Temporal Token Learning for Visual Tracking

Reference 17

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Source-reported events for the cited work

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Observation 9c5ac1bc-8aba-44a2-9ff4-9c950b79c468 · outbound

This paper cites More Details on the RGB-X Benchmarks As discussed in Sec.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features More Details on the RGB-X Benchmarks As discussed in Sec

Reference 18

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Observation d09bc597-1631-4be6-a031-009cb30f238b · outbound

This paper cites In this section, we provide an overview of these benchmarks and their respective evaluation metrics.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features In this section, we provide an overview of these benchmarks and their respective evaluation metrics

Reference 19

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Observation 5d87c688-6f27-4a63-99dd-244769f886a6 · outbound

This paper cites This dataset employs an anchor-based short-term evaluation protocol (Kristan et al., 2020), which requires trackers to restart multiple times from different initialization points.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features This dataset employs an anchor-based short-term evaluation protocol (Kristan et al., 2020), which requires trackers to restart multiple times from different initialization points

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d9208d1e-7e0a-4c0c-a420-2323cc5e8a92 · outbound

This paper cites As shown in Tab.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features As shown in Tab

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation cda9b8e8-12e7-44fb-bfaa-14b1c76c3f3f · outbound

This paper cites Our compact spatial modeling method, through the pro- posed Spatial Compact Module, integrates the features of both RGB and X modalities into a compact feature space.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Our compact spatial modeling method, through the pro- posed Spatial Compact Module, integrates the features of both RGB and X modalities into a compact feature space

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 30de5c83-5124-4ab7-b42a-4b553006c5db · outbound

This paper cites These methods compress the temporal interaction between search and cue features into a small set of temporal queries, providing temporal guidance for the tracker.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features These methods compress the temporal interaction between search and cue features into a small set of temporal queries, providing temporal guidance for the tracker

Reference 24

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raw_fallback, observed 2026-08-07T14:19:19.951245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2fff0499-ee89-42a2-9654-97b32bc1a484 · outbound

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

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2009

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Observation ceee9695-3fe0-4a7d-916d-d9743f227eda · outbound

This paper cites VMBench: A Benchmark for Perception-Aligned Video Motion Generation.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features VMBench: A Benchmark for Perception-Aligned Video Motion Generation

Reference 2014

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Observation 01ec521d-a63a-40d8-aea5-2a6b6f77a7c5 · outbound

This paper cites This approach has been widely adopted in the tracking field, such as in TrDiMP (Wang et al., 2021a) and JointNLT (Zhou et al., 2023).

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features This approach has been widely adopted in the tracking field, such as in TrDiMP (Wang et al., 2021a) and JointNLT (Zhou et al., 2023)

Reference 2015

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 80575576-bc44-48bc-9f00-62ecbb9437d4 · outbound

This paper cites VastTrack: Vast Category Visual Object Tracking.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features VastTrack: Vast Category Visual Object Tracking

Reference 2016

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local_arxiv, observed 2026-08-07T14:19:19.104070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation bcd44cdb-919c-45dd-ad8d-e0f07484b930 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 2018

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Observation 9b9895d9-6601-4598-a1f0-7bc615ec7ec4 · outbound

This paper cites Explicit Visual Prompts for Visual Object Tracking.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Explicit Visual Prompts for Visual Object Tracking

Reference 2019

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local_arxiv, observed 2026-08-07T14:19:18.830409Z

Source-reported events for the cited work

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Observation 4b6fb4c5-ed9a-4eb3-93b3-4e39d6747ff6 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Imagenet: A large-scale hierarchical image database

Reference 2020

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Observation 5d19dfa8-d9ab-49ac-a3c5-a0096dfa298b · outbound

This paper cites DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features DTVLT: A Multi-modal Diverse Text Benchmark for Visual Language Tracking Based on LLM

Reference 2021

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Unavailable: canonical work link unavailable.

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Observation ee6c68ce-e826-40e8-a5aa-73c4e8275bb6 · outbound

This paper cites Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers

Reference 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation fab4ea75-533a-4afc-b9e7-f9d87e3b1128 · outbound

This paper cites ˇC., Lukeˇziˇc, A., Drbohlav, O., et al.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features ˇC., Lukeˇziˇc, A., Drbohlav, O., et al

Reference 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2291bfdd-35a7-4d92-8c62-a5ffdea1d3ed · outbound

This paper cites Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness.

CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness

Reference 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Pith citing papers

Observation d93218e6-6d02-432f-843f-0e7a18ab5ded · inbound

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts cites this paper.

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features

Reference 3

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arxiv_id, observed 2026-05-11T23:16:14.235460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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