Drift-Resilient Temporal Priors for Visual Tracking | Paper Citation Record

Observation 9464dd4a-b48b-4bb5-bba6-a9008f2aa61e · outbound

This paper cites Ar- trackv2: Prompting autoregressive tracker where to look and how to describe.

Drift-Resilient Temporal Priors for Visual Tracking Ar- trackv2: Prompting autoregressive tracker where to look and how to describe

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.219876Z

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Observation c543b16f-b803-4d23-bba0-e712b0aa5a16 · outbound

This paper cites Learning discriminative model prediction for track- ing.

Drift-Resilient Temporal Priors for Visual Tracking Learning discriminative model prediction for track- ing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.211788Z

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Observation 7edf3b67-ff4e-4798-b278-dfde5fdf5679 · outbound

This paper cites Hiptrack: Vi- sual tracking with historical prompts.

Drift-Resilient Temporal Priors for Visual Tracking Hiptrack: Vi- sual tracking with historical prompts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.194942Z

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Observation bc56ce25-c695-4dd0-bf40-0647f376dea6 · outbound

This paper cites Spmtrack: Spatio-temporal parameter-efficient fine-tuning with mixture of experts for scalable visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Spmtrack: Spatio-temporal parameter-efficient fine-tuning with mixture of experts for scalable visual tracking

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.199104Z

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Observation c9d22377-3032-4410-8665-8deacb74db90 · outbound

This paper cites Robust object modeling for visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Robust object modeling for visual tracking

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.190733Z

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Observation 4d217ec9-d563-42ec-9e21-6342f9aa49cb · outbound

This paper cites Backbone is all your need: A simplified architecture for visual object tracking.

Drift-Resilient Temporal Priors for Visual Tracking Backbone is all your need: A simplified architecture for visual object tracking

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.203381Z

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Observation 6f50cf80-2c63-476d-8025-01db68a5d257 · outbound

This paper cites Transformer tracking.

Drift-Resilient Temporal Priors for Visual Tracking Transformer tracking

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.223969Z

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Observation a855dad8-821a-4a76-bda6-5c0c3e89a841 · outbound

This paper cites High-performance transformer tracking.

Drift-Resilient Temporal Priors for Visual Tracking High-performance transformer tracking

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.207354Z

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Observation 00e88067-28d2-45dd-9bed-b45657bf2154 · outbound

This paper cites SeqTrack: Sequence to sequence learning for visual ob- ject tracking.

Drift-Resilient Temporal Priors for Visual Tracking SeqTrack: Sequence to sequence learning for visual ob- ject tracking

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.215692Z

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Observation c8dd3f42-7fb9-4157-be8e-c9ebaa27a304 · outbound

This paper cites MixFormer: End-to-end tracking with iterative mixed atten- tion.

Drift-Resilient Temporal Priors for Visual Tracking MixFormer: End-to-end tracking with iterative mixed atten- tion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.182563Z

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Observation ec90b417-59f3-4353-9906-59cf0b2c06f4 · outbound

This paper cites MixFormer: End-to-end tracking with iterative mixed atten- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 4129 – 4146.

Drift-Resilient Temporal Priors for Visual Tracking MixFormer: End-to-end tracking with iterative mixed atten- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 4129 – 4146

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.187287Z

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Observation 395fcb69-c833-4f6c-bac0-6912156e52f9 · outbound

This paper cites Proba- bilistic regression for visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Proba- bilistic regression for visual tracking

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.292661Z

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Observation 3c198bb5-54e9-492f-8301-e3d30041f78a · outbound

This paper cites FlashAttention-2: Faster attention with better par- allelism and work partitioning.

Drift-Resilient Temporal Priors for Visual Tracking FlashAttention-2: Faster attention with better par- allelism and work partitioning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.296979Z

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Observation 886c0c3a-67a7-466e-b504-861d3a605253 · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher R´e.

Drift-Resilient Temporal Priors for Visual Tracking Fu, Stefano Ermon, Atri Rudra, and Christopher R´e

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.312727Z

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Observation 4fa3eb63-600f-4e95-b198-8bcf09fd30f9 · outbound

This paper cites Vision transformers need registers.

Drift-Resilient Temporal Priors for Visual Tracking Vision transformers need registers

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.240153Z

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Observation 6eaa3475-a74d-404f-b609-bbd78030feea · outbound

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

Drift-Resilient Temporal Priors for Visual Tracking An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.340134Z

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Observation c61eeae9-dfbe-43b6-a61e-f10db6ae65d0 · outbound

This paper cites LaSOT: A high-quality benchmark for large-scale single ob- ject tracking.

Drift-Resilient Temporal Priors for Visual Tracking LaSOT: A high-quality benchmark for large-scale single ob- ject tracking

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.257177Z

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Observation b5f8c625-c199-412e-9120-0b8e2ec571f6 · outbound

This paper cites AiATrack: Attention in attention for transformer visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking AiATrack: Attention in attention for transformer visual tracking

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.304426Z

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Observation b258765e-c6b6-4720-9036-fca0d24b8a6e · outbound

This paper cites Generalized relation modeling for transformer tracking.

Drift-Resilient Temporal Priors for Visual Tracking Generalized relation modeling for transformer tracking

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.228386Z

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Observation b5bbedf3-beae-466d-bd0e-6bfb31ce6e04 · outbound

This paper cites Dreamtrack: Dreaming the future for mul- timodal visual object tracking.

Drift-Resilient Temporal Priors for Visual Tracking Dreamtrack: Dreaming the future for mul- timodal visual object tracking

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.358502Z

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Observation 0d5180fc-d2a9-4cdb-bcc0-5d91dd83f2fe · outbound

This paper cites Target-aware tracking with long-term context attention.

Drift-Resilient Temporal Priors for Visual Tracking Target-aware tracking with long-term context attention

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.232523Z

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Observation fb605278-c7ef-42ba-a120-99a21fadb0e4 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Drift-Resilient Temporal Priors for Visual Tracking LoRA: Low-rank adaptation of large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.403812Z

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Observation bf90354e-e21a-4f95-b810-181a4dda4ec5 · outbound

This paper cites Got-10k: A large high-diversity benchmark for generic object tracking in the wild.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 43(5):1562–1577.

Drift-Resilient Temporal Priors for Visual Tracking Got-10k: A large high-diversity benchmark for generic object tracking in the wild.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 43(5):1562–1577

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.382386Z

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Observation 265b7444-bbfa-448d-8ec4-36f4a1869ab0 · outbound

This paper cites Rtracker: Recoverable tracking via pn tree structured memory.

Drift-Resilient Temporal Priors for Visual Tracking Rtracker: Recoverable tracking via pn tree structured memory

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.370197Z

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Observation 877d8181-51a9-46c4-9fe6-af1927f9d594 · outbound

This paper cites Exploring enhanced contextual information for video-level object tracking.

Drift-Resilient Temporal Priors for Visual Tracking Exploring enhanced contextual information for video-level object tracking

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.330262Z

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Observation 47fb7faf-40ec-4c3e-8fa2-c682bfaa156b · outbound

This paper cites The tenth visual object tracking vot2022 challenge re- sults.

Drift-Resilient Temporal Priors for Visual Tracking The tenth visual object tracking vot2022 challenge re- sults

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.365955Z

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Observation 07727eaf-ece9-48c8-aafa-3d097ea46d1a · outbound

This paper cites The second visual object tracking segmentation vots2024 challenge results.

Drift-Resilient Temporal Priors for Visual Tracking The second visual object tracking segmentation vots2024 challenge results

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.326257Z

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Observation e2e304c5-d7ed-4cae-959a-10c605eb1431 · outbound

This paper cites FractalNet: Ultra-deep neural networks without residuals.

Drift-Resilient Temporal Priors for Visual Tracking FractalNet: Ultra-deep neural networks without residuals

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.334331Z

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Observation 89367ef9-458f-4eac-9fd7-717ba634f346 · outbound

This paper cites CiteTracker: Correlating image and text for visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking CiteTracker: Correlating image and text for visual tracking

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.344272Z

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Observation f374a86b-fa5e-4c50-80b9-b32fa7a5a32c · outbound

This paper cites SwinTrack: A simple and strong baseline for trans- former tracking.

Drift-Resilient Temporal Priors for Visual Tracking SwinTrack: A simple and strong baseline for trans- former tracking

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.271335Z

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Observation 265e2ddc-95d7-4ff5-afaf-be11ac3efb96 · outbound

This paper cites Tracking meets lora: Faster training, larger model, stronger performance.

Drift-Resilient Temporal Priors for Visual Tracking Tracking meets lora: Faster training, larger model, stronger performance

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.244198Z

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Observation 69838809-6161-4f5c-aef5-2b20654c0100 · outbound

This paper cites Loratv2: En- abling low-cost temporal modeling in one-stream trackers.

Drift-Resilient Temporal Priors for Visual Tracking Loratv2: En- abling low-cost temporal modeling in one-stream trackers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.252637Z

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Observation c6b8ed0b-e8fc-4052-b3eb-4fb7ca072209 · outbound

This paper cites Microsoft COCO: Common objects in context.

Drift-Resilient Temporal Priors for Visual Tracking Microsoft COCO: Common objects in context

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.279877Z

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Observation 8681e6c7-a00f-4785-8f0f-f8412750b53e · outbound

This paper cites Decoupled weight decay regularization.

Drift-Resilient Temporal Priors for Visual Tracking Decoupled weight decay regularization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.275740Z

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Observation 967371ae-95b9-40df-904d-3063e3125b0f · outbound

This paper cites A benchmark and simulator for uav tracking.

Drift-Resilient Temporal Priors for Visual Tracking A benchmark and simulator for uav tracking

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.374158Z

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Observation fe152e29-618b-449a-b01c-2a0a5ed97b21 · outbound

This paper cites TrackingNet: A large-scale dataset and benchmark for object tracking in the wild.

Drift-Resilient Temporal Priors for Visual Tracking TrackingNet: A large-scale dataset and benchmark for object tracking in the wild

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.362222Z

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Observation 39165ac0-61c4-4eec-a9d1-41d5865ac35e · outbound

This paper cites Learning multi-domain convolutional neural networks for visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Learning multi-domain convolutional neural networks for visual tracking

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.394598Z

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Observation 299cd1ac-5f60-4eba-ab2d-b7ce1bbc69f9 · outbound

This paper cites DINOv2: Learning robust visual features without supervi- sion.

Drift-Resilient Temporal Priors for Visual Tracking DINOv2: Learning robust visual features without supervi- sion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.308554Z

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Observation 07814236-9524-42bf-8819-52c411ba5f4f · outbound

This paper cites Vast- track: Vast category visual object tracking.

Drift-Resilient Temporal Priors for Visual Tracking Vast- track: Vast category visual object tracking

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.378368Z

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Observation ee937aa5-6b7e-42bf-9640-f141a66bb86c · outbound

This paper cites Self-attention Does Not Need $O(n^2)$ Memory.

Drift-Resilient Temporal Priors for Visual Tracking Self-attention Does Not Need $O(n^2)$ Memory

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T19:53:11.800078Z

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Observation 37f57161-8c2a-45f1-97ec-1f2a13d9e0c6 · outbound

This paper cites Generalized in- tersection over union: A metric and a loss for bounding box regression.

Drift-Resilient Temporal Priors for Visual Tracking Generalized in- tersection over union: A metric and a loss for bounding box regression

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.300590Z

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Observation f78bc9eb-e2cf-47f6-a3e8-2a044b1fa440 · outbound

This paper cites DeiT III: Revenge of the ViT.

Drift-Resilient Temporal Priors for Visual Tracking DeiT III: Revenge of the ViT

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.317676Z

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Observation b34eb598-4f6d-4aef-8bce-194ef2d0bf63 · outbound

This paper cites Towards more flexible and accurate object tracking with natural language: Algo- rithms and benchmark.

Drift-Resilient Temporal Priors for Visual Tracking Towards more flexible and accurate object tracking with natural language: Algo- rithms and benchmark

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.322285Z

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Observation daa4c3e8-c477-4b51-9167-6668d008d0ab · outbound

This paper cites Autoregressive visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Autoregressive visual tracking

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.348480Z

Source-reported events for the cited work

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Observation e67782a8-84d6-46ae-9350-f5fec7a9c530 · outbound

This paper cites DropMAE: Masked autoen- coders with spatial-attention dropout for tracking tasks.

Drift-Resilient Temporal Priors for Visual Tracking DropMAE: Masked autoen- coders with spatial-attention dropout for tracking tasks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.399854Z

Source-reported events for the cited work

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Observation 973322e3-4970-4827-8443-1c819b8f1da2 · outbound

This paper cites Object track- ing benchmark.IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9):1834–1848.

Drift-Resilient Temporal Priors for Visual Tracking Object track- ing benchmark.IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9):1834–1848

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.283852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd45768a-35cd-4545-a2f5-2ce89eea7fae · outbound

This paper cites Motiontrack: Learning motion predictor for multiple object tracking.Neu- ral Networks, 179:106539.

Drift-Resilient Temporal Priors for Visual Tracking Motiontrack: Learning motion predictor for multiple object tracking.Neu- ral Networks, 179:106539

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.248694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa70c845-07ac-402d-818e-95ad240a0c7e · outbound

This paper cites Video- track: Learning to track objects via video transformer.

Drift-Resilient Temporal Priors for Visual Tracking Video- track: Learning to track objects via video transformer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.262745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 600097a5-a0fd-4b22-b19d-a3b6c640dbc0 · outbound

This paper cites Diffusiontrack: Point set diffusion model for visual object tracking.

Drift-Resilient Temporal Priors for Visual Tracking Diffusiontrack: Point set diffusion model for visual object tracking

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.288582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e8addb3-9b7e-4424-bfd8-f6ed1b4f5be3 · outbound

This paper cites Autore- gressive queries for adaptive tracking with spatio-temporal transformers.

Drift-Resilient Temporal Priors for Visual Tracking Autore- gressive queries for adaptive tracking with spatio-temporal transformers

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.352858Z

Source-reported events for the cited work

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Observation 8769a117-8c88-4f0a-9633-e98892ebc254 · outbound

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

Drift-Resilient Temporal Priors for Visual Tracking Learning spatio-temporal transformer for vi- sual tracking

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.266828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf6a8b08-6880-4c6b-927a-23f0c631530d · outbound

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

Drift-Resilient Temporal Priors for Visual Tracking Joint feature learning and relation modeling for tracking: A one-stream framework

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.390382Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 536e3529-c68b-4d90-accb-9d4e7b59a4c0 · outbound

This paper cites Odtrack: Online dense temporal token learning for visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Odtrack: Online dense temporal token learning for visual tracking

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.386229Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bc83ae2-fe68-4fc3-9f90-58567346ee7d · outbound

This paper cites Two-stream beats one-stream: asymmetric siamese network for efficient visual tracking.

Drift-Resilient Temporal Priors for Visual Tracking Two-stream beats one-stream: asymmetric siamese network for efficient visual tracking

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T02:44:56.236253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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