Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model | Paper Citation Record

Observation b4364b3c-ffc0-4b8e-ba0e-302441cff9dc · outbound

This paper cites So- cial lstm: Human trajectory prediction in crowded spaces.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model So- cial lstm: Human trajectory prediction in crowded spaces

Reference 1

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Observation b3744196-c0d0-424c-969f-a986c8012fe6 · outbound

This paper cites Long-tailed recognition via weight balancing.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Long-tailed recognition via weight balancing

Reference 2

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Observation a30dd0cc-1cd8-4160-8ad3-5b33eca334c2 · outbound

This paper cites A set of control points con- ditioned pedestrian trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model A set of control points con- ditioned pedestrian trajectory prediction

Reference 3

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Observation 7b58f7bc-484a-46d1-a007-2269925d2022 · outbound

This paper cites Learning pedestrian group representations for multi-modal trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Learning pedestrian group representations for multi-modal trajectory prediction

Reference 4

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Observation 7602308c-b650-4ce6-b27a-e3cb04e7cdad · outbound

This paper cites Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Eigentrajectory: Low-rank descriptors for multi-modal trajectory forecasting

Reference 5

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Observation 3f687f7f-ffc3-4d20-a885-5df5a4a8a711 · outbound

This paper cites Can language beat numerical regression? language-based multimodal tra- jectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Can language beat numerical regression? language-based multimodal tra- jectory prediction

Reference 6

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Observation e2c73df5-209a-47a8-b9b6-d851b04aeef3 · outbound

This paper cites Can language beat numerical regression? language-based multimodal tra- jectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Can language beat numerical regression? language-based multimodal tra- jectory prediction

Reference 7

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Observation b2fe642c-d645-4e96-b607-5d0474528acc · outbound

This paper cites Singu- lartrajectory: Universal trajectory predictor using diffusion model.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Singu- lartrajectory: Universal trajectory predictor using diffusion model

Reference 8

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Observation 8aef26c0-3cec-426b-9ef7-3fecb2bc3725 · outbound

This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom

Reference 9

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Observation 9e7277ff-2e55-4ad4-a4da-1b3d79c3384d · outbound

This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Learning imbalanced datasets with label- distribution-aware margin loss

Reference 10

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Observation d4e04e76-4658-40b4-a0e5-16fa6902835d · outbound

This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Learning imbalanced datasets with label- distribution-aware margin loss

Reference 11

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Observation f16deb46-efda-42cc-8ea5-322000e6d778 · outbound

This paper cites Ar- goverse: 3D Tracking and Forecasting With Rich Maps.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Ar- goverse: 3D Tracking and Forecasting With Rich Maps

Reference 12

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Observation 7bf91f6c-aa5b-45a4-8300-01a20530d68c · outbound

This paper cites Ppad: Iterative interactions of prediction and planning for end-to-end autonomous driving.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Ppad: Iterative interactions of prediction and planning for end-to-end autonomous driving

Reference 13

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Observation 1c9ef82a-be9e-4eb4-b6d1-541d55782314 · outbound

This paper cites Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders

Reference 14

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Observation cb0008e4-8ad2-428a-b737-4217d9c9e154 · outbound

This paper cites Parametric contrastive learning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Parametric contrastive learning

Reference 15

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Observation b2b4cf61-76e3-463f-bff1-a36ec7fdfcf3 · outbound

This paper cites Large scale fine-grained categorization and domain-specific transfer learning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Large scale fine-grained categorization and domain-specific transfer learning

Reference 16

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Observation 5818a240-aa67-445c-9f58-e9475800a3e5 · outbound

This paper cites Large Scale In- teractive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Large Scale In- teractive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset

Reference 17

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Observation 7fa25db7-df18-4355-a0a3-446431e322c8 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 18

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Observation 0586a0e0-fc01-46e8-834e-404b6f5f1bc3 · outbound

This paper cites Unitraj: A unified framework for scalable vehicle trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Unitraj: A unified framework for scalable vehicle trajectory prediction

Reference 19

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Observation c25cc8a0-5f26-46e8-a779-f7cad1686e51 · outbound

This paper cites Producing and leveraging on- line map uncertainty in trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Producing and leveraging on- line map uncertainty in trajectory prediction

Reference 20

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Observation a3642e65-1882-4977-b03d-8319de6b3037 · outbound

This paper cites Social gan: Socially acceptable tra- jectories with generative adversarial networks.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Social gan: Socially acceptable tra- jectories with generative adversarial networks

Reference 21

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Observation 9e9998b6-1e4b-44f3-a20d-d047e7254234 · outbound

This paper cites IS SYN- THETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? In The Eleventh Interna- tional Conference on Learning Representations, 2023.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model IS SYN- THETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? In The Eleventh Interna- tional Conference on Learning Representations, 2023

Reference 22

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Observation 3e5f1726-7cfe-4a35-a2c4-1986d11a35c8 · outbound

This paper cites Distilling vir- tual examples for long-tailed recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Distilling vir- tual examples for long-tailed recognition

Reference 23

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Observation 4c9a7b75-1efd-4b7d-a6f2-42037e9694c2 · outbound

This paper cites Subclass-balancing contrastive learning for long- tailed recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Subclass-balancing contrastive learning for long- tailed recognition

Reference 24

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Observation dc339f5f-6bc3-432a-ba24-e8dad8aaba7e · outbound

This paper cites Planning-oriented autonomous driving.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Planning-oriented autonomous driving

Reference 25

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Observation ffd7eb1f-41e2-44ae-b417-eff1592bcccc · outbound

This paper cites Active Generation for Image Classification.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Active Generation for Image Classification

Reference 26

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Observation 3d3d5d21-920f-4b52-98a2-963246491c39 · outbound

This paper cites Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving

Reference 27

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Observation 9750b9cf-8a56-4050-9e9d-1be0d5545cc1 · outbound

This paper cites Versatile behavior dif- fusion for generalized traffic agent simulation.arXiv preprint arXiv:2404.02524, 2024.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Versatile behavior dif- fusion for generalized traffic agent simulation.arXiv preprint arXiv:2404.02524, 2024

Reference 28

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Observation 5662da6a-e5a4-423a-b553-d171d2787807 · outbound

This paper cites The trajectron: Proba- bilistic multi-agent trajectory modeling with dynamic spa- tiotemporal graphs.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model The trajectron: Proba- bilistic multi-agent trajectory modeling with dynamic spa- tiotemporal graphs

Reference 29

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Observation 82b7addc-93fa-4262-bed9-f33dff2855ca · outbound

This paper cites Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective

Reference 30

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Observation 0939a58c-61d3-4e7c-8da6-1f9634ee24df · outbound

This paper cites Multi-agent long-term 3d human pose forecasting via interaction-aware trajectory conditioning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Multi-agent long-term 3d human pose forecasting via interaction-aware trajectory conditioning

Reference 31

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Observation c97214a8-115d-462b-93d2-5c119fcc29d5 · outbound

This paper cites Multi-modal knowledge distillation-based human trajectory forecasting.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Multi-modal knowledge distillation-based human trajectory forecasting

Reference 32

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Observation fb7dabd3-04bb-4cf5-950d-206e78d080cd · outbound

This paper cites Motiondiffuser: Controllable multi-agent motion prediction using diffusion.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Motiondiffuser: Controllable multi-agent motion prediction using diffusion

Reference 33

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Observation 29e49352-566f-4d02-ab48-502f4017c129 · outbound

This paper cites Scenediffuser: Efficient and controllable driving simulation initialization and rollout.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Scenediffuser: Efficient and controllable driving simulation initialization and rollout

Reference 34

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Observation df6cecbc-4f53-49d2-b19f-662fa93378aa · outbound

This paper cites Decou- pling representation and classifier for long-tailed recogni- tion.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Decou- pling representation and classifier for long-tailed recogni- tion

Reference 35

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Observation 9cf7dd5c-4b4d-4309-974a-ba2c25771b2c · outbound

This paper cites M2m: Imbalanced classification via major-to-minor translation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model M2m: Imbalanced classification via major-to-minor translation

Reference 36

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Observation bd2633d4-f66a-486b-8eaf-e5121d1445c2 · outbound

This paper cites Higher-order relational reasoning for pedestrian trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Higher-order relational reasoning for pedestrian trajectory prediction

Reference 37

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Observation fc0819f7-274e-4f8b-bbfd-91b3f8300a4d · outbound

This paper cites Active generative adversarial network for image classification.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Active generative adversarial network for image classification

Reference 38

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Observation d4a8a25d-1d8a-46be-bf09-99b85e14376e · outbound

This paper cites Sept: Towards efficient scene represen- tation learning for motion prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Sept: Towards efficient scene represen- tation learning for motion prediction

Reference 39

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Observation 9477e58b-7f42-474b-930f-950d54d316e3 · outbound

This paper cites Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning

Reference 40

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Observation bafc57cd-f090-491a-a1c7-7c46d82890d8 · outbound

This paper cites Desire: Distant future prediction in dynamic scenes with interacting agents.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Desire: Distant future prediction in dynamic scenes with interacting agents

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.318540Z

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Observation 94642c81-f514-453e-808e-31e516b6f88e · outbound

This paper cites Semantic-guided generative image augmentation method with diffusion models for image classification.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Semantic-guided generative image augmentation method with diffusion models for image classification

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.309887Z

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Observation 2f2faa03-c6f0-4166-81d5-48cb6fbe1afb · outbound

This paper cites Nested collaborative learning for long-tailed visual recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Nested collaborative learning for long-tailed visual recognition

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.300588Z

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Observation 1d67c942-359a-4033-a938-b7c1dcf222e8 · outbound

This paper cites Metasaug: Meta semantic augmentation for long-tailed visual recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Metasaug: Meta semantic augmentation for long-tailed visual recognition

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.291416Z

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Observation cf7fd1b7-0ad2-4c1c-94fe-548d93e4dae7 · outbound

This paper cites Targeted su- pervised contrastive learning for long-tailed recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Targeted su- pervised contrastive learning for long-tailed recognition

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.282459Z

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Observation fd73b43e-daab-46a8-a09d-4116d7729bda · outbound

This paper cites Meid: mixture-of-experts with internal distillation for long-tailed video recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Meid: mixture-of-experts with internal distillation for long-tailed video recognition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.273365Z

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Observation db673155-0a1b-40d6-839e-e7c43bd3f3fa · outbound

This paper cites Cdkformer: Contextual deviation knowledge-based transformer for long- tail trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Cdkformer: Contextual deviation knowledge-based transformer for long- tail trajectory prediction

Reference 47

Resolution: verified exact; raw_fallback, observed 2026-08-06T11:34:38.769487Z

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Observation 34c7699b-5031-4153-979c-7a39d757d22d · outbound

This paper cites Deep representation learning on long-tailed data: A learnable embedding augmentation perspective.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Deep representation learning on long-tailed data: A learnable embedding augmentation perspective

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.264381Z

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Observation ae1dc65f-16f5-4784-8d5a-2c929cdcbe49 · outbound

This paper cites an unresolved cited work.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Unresolved cited work

Reference 49

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Observation 94fc3bb5-2e3a-49dc-9c16-142d33cf92e3 · outbound

This paper cites On exposing the challenging long tail in future prediction of traffic actors.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model On exposing the challenging long tail in future prediction of traffic actors

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.246940Z

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Observation 4f5eb755-ac4d-44a8-be18-0d52cc0c709f · outbound

This paper cites Long-tail learning via logit adjustment.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Long-tail learning via logit adjustment

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.238589Z

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.

Observation b4b0fd48-0d78-4dff-8eb2-f1864edc6708 · outbound

This paper cites Long-tail learning via logit adjustment.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Long-tail learning via logit adjustment

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.230043Z

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.

Observation be5bc3e8-bb93-4f88-90f6-8bdb20e0652e · outbound

This paper cites Amend: A mixture of experts framework for long-tailed trajectory prediction,.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Amend: A mixture of experts framework for long-tailed trajectory prediction,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.220293Z

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.

Observation 4dd02218-0acf-4c22-bb15-15e5830c2aef · outbound

This paper cites Most: Multi-modality scene to- kenization for motion prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Most: Multi-modality scene to- kenization for motion prediction

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.211921Z

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.

Observation acb30c83-abef-4b05-aad9-9a77f78f9e66 · outbound

This paper cites Factors in finetuning deep model for object detec- tion with long-tail distribution.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Factors in finetuning deep model for object detec- tion with long-tail distribution

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.203570Z

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.

Observation 00c00545-0b9c-4774-a96d-44a879413374 · outbound

This paper cites Leveraging future relation- ship reasoning for vehicle trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Leveraging future relation- ship reasoning for vehicle trajectory prediction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.194420Z

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.

Observation 17349ae9-6a04-4047-8fea-805f62c4c5bc · outbound

This paper cites Improv- ing transferability for cross-domain trajectory prediction via neural stochastic differential equation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Improv- ing transferability for cross-domain trajectory prediction via neural stochastic differential equation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.186040Z

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

Observation 09ab8c70-4627-4916-a05d-2cb686555378 · outbound

This paper cites T4p: Test-time training of tra- jectory prediction via masked autoencoder and actor-specific token memory.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model T4p: Test-time training of tra- jectory prediction via masked autoencoder and actor-specific token memory

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.177303Z

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.

Observation dd834ce8-8589-40cf-a078-3d946837cd7a · outbound

This paper cites What truly matters in trajectory pre- diction for autonomous driving? Advances in Neural Infor- mation Processing Systems, 36, 2024.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model What truly matters in trajectory pre- diction for autonomous driving? Advances in Neural Infor- mation Processing Systems, 36, 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.168907Z

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.

Observation 75d834bc-2bac-4b29-982c-a759680b4d63 · outbound

This paper cites Cadet: a causal disentanglement approach for robust trajec- tory prediction in autonomous driving.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Cadet: a causal disentanglement approach for robust trajec- tory prediction in autonomous driving

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.160229Z

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.

Observation 2996df47-5339-4d7b-bb45-0fc249c64201 · outbound

This paper cites Scenario diffusion: Controllable driving scenario gen- eration with diffusion.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Scenario diffusion: Controllable driving scenario gen- eration with diffusion

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.150933Z

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.

Observation bf747fcd-d793-4d79-b8dc-7285ebbd2816 · outbound

This paper cites R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.142539Z

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.

Observation 4590ab5c-55f2-4dcf-b5d6-3c53f80e358e · outbound

This paper cites Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic interaction graphs.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic interaction graphs

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.133760Z

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.

Observation 706f757f-a3fc-4401-8a5a-c305913a830d · outbound

This paper cites Distributional robustness loss for long-tail learning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Distributional robustness loss for long-tail learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.124894Z

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.

Observation 3f9b2633-7eb7-4bdd-9a4b-f58695f66059 · outbound

This paper cites Motionlm: Multi-agent motion forecast- ing as language modeling.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Motionlm: Multi-agent motion forecast- ing as language modeling

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.116243Z

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.

Observation cb4be157-56c1-406b-b532-4d2088e01976 · outbound

This paper cites How re-sampling helps for long-tail learning? Advances in Neural Information Processing Systems, 36, 2023.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model How re-sampling helps for long-tail learning? Advances in Neural Information Processing Systems, 36, 2023

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.107068Z

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.

Observation de521492-905d-471c-930a-c9ba7dfbb451 · outbound

This paper cites Motion transformer with global intention localization and lo- cal movement refinement.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Motion transformer with global intention localization and lo- cal movement refinement

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.098549Z

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.

Observation bb79920f-3886-4a3a-9ce7-363127cf2a81 · outbound

This paper cites Language conditioned traffic generation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Language conditioned traffic generation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.089695Z

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.

Observation d486feb0-bcf2-4068-85e8-55772bee3537 · outbound

This paper cites Hpnet: Dynamic trajectory fore- casting with historical prediction attention.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Hpnet: Dynamic trajectory fore- casting with historical prediction attention

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.079781Z

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.

Observation 601f4179-9276-4a85-82b8-4874f0158b7f · outbound

This paper cites Rsg: A simple but effective mod- ule for learning imbalanced datasets.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Rsg: A simple but effective mod- ule for learning imbalanced datasets

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.070915Z

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.

Observation 795170ea-f668-449f-af0a-10d3b887bd43 · outbound

This paper cites Contrastive learning based hybrid networks for long- tailed image classification.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Contrastive learning based hybrid networks for long- tailed image classification

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.062214Z

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.

Observation 6d8bf6f0-fed9-4dc1-a211-c05c37789286 · outbound

This paper cites Fend: A future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Fend: A future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.053092Z

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.

Observation 70d4000f-1b3b-4176-8feb-20e092ebbccf · outbound

This paper cites Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:34:38.641696Z

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.

Observation 61a73e66-9aa2-4dcf-934d-ff5fd2a3c272 · outbound

This paper cites Density-adaptive model based on motif ma- trix for multi-agent trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Density-adaptive model based on motif ma- trix for multi-agent trajectory prediction

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.044638Z

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.

Observation 04650064-f593-40a8-8a82-47fa55a5bbfd · outbound

This paper cites Argoverse 2: Next generation datasets for self-driving perception and fore- casting.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Argoverse 2: Next generation datasets for self-driving perception and fore- casting

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.035048Z

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.

Observation bfb16255-049e-42f4-b55d-9f1f9ba43a7d · outbound

This paper cites Adapting to length shift: Flexilength network for trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Adapting to length shift: Flexilength network for trajectory prediction

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.026133Z

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.

Observation 1b3e3bdb-f893-464a-96e6-a587190e1a41 · outbound

This paper cites Sports-traj: A unified trajectory gen- eration model for multi-agent movement in sports.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Sports-traj: A unified trajectory gen- eration model for multi-agent movement in sports

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.016598Z

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.

Observation c64bad09-ab52-4cd7-b182-e39f47d0a22c · outbound

This paper cites Towards cal- ibrated model for long-tailed visual recognition from prior perspective.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Towards cal- ibrated model for long-tailed visual recognition from prior perspective

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:39.007071Z

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.

Observation 9bffbe1c-c1ef-4002-a996-51a46c18fc51 · outbound

This paper cites Rethinking the value of labels for improving class-imbalanced learning.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Rethinking the value of labels for improving class-imbalanced learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.998533Z

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.

Observation 93668cee-59f9-4739-813d-45770cc787d9 · outbound

This paper cites Delving into deep imbalanced regression.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Delving into deep imbalanced regression

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.989815Z

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.

Observation 82fd5779-0c3d-4474-97ea-de3073f2aef3 · outbound

This paper cites Feature transfer learning for face recog- nition with under-represented data.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Feature transfer learning for face recog- nition with under-represented data

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.981218Z

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.

Observation 061ec106-af1b-47d2-8aa7-50717caa6d4e · outbound

This paper cites Fasa: Feature augmentation and sampling adaptation for long- tailed instance segmentation.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Fasa: Feature augmentation and sampling adaptation for long- tailed instance segmentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.972487Z

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.

Observation 186e45ae-f1f6-41e3-9876-fa4e53c46642 · outbound

This paper cites Deep long-tailed learning: A survey.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Deep long-tailed learning: A survey

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.962891Z

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.

Observation 7d1106b1-4a3a-4439-b4d9-1108315788a4 · outbound

This paper cites Lcsim: A large-scale controllable traffic simulator, 2024.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Lcsim: A large-scale controllable traffic simulator, 2024

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.953855Z

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.

Observation 14e193ef-7930-4367-8976-104634305c0c · outbound

This paper cites Expanding small-scale datasets with guided imag- ination.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Expanding small-scale datasets with guided imag- ination

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.945081Z

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.

Observation 1d0c7c3d-3ad3-4a4b-a80a-6545c508da59 · outbound

This paper cites Real-time motion prediction via het- erogeneous polyline transformer with relative pose encod- ing.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Real-time motion prediction via het- erogeneous polyline transformer with relative pose encod- ing

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.935738Z

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.

Observation f59505bf-0dfc-4daf-80dd-e7a3a1f36e0d · outbound

This paper cites Im- proving calibration for long-tailed recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Im- proving calibration for long-tailed recognition

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.926362Z

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.

Observation 4203b127-35e8-45ca-ba97-6db379b18dc6 · outbound

This paper cites Guided conditional diffusion for controllable traffic simula- tion.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Guided conditional diffusion for controllable traffic simula- tion

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.916441Z

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.

Observation 99836869-f97e-4286-9724-f701e751ba6d · outbound

This paper cites Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.906112Z

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.

Observation f5b1c49a-1545-4ba3-98b1-20a3439d82f4 · outbound

This paper cites Imb- sam: A closer look at sharpness-aware minimization in class- imbalanced recognition.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Imb- sam: A closer look at sharpness-aware minimization in class- imbalanced recognition

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.896405Z

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.

Observation 8b8ee09a-bcec-4a9b-8e8f-f88073c77c78 · outbound

This paper cites Smartrefine: A scenario-adaptive refinement framework for efficient motion prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Smartrefine: A scenario-adaptive refinement framework for efficient motion prediction

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.885502Z

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.

Observation 59d735ec-97b7-46e2-8175-15ebf68c9c07 · outbound

This paper cites Query-centric trajectory prediction.

Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model Query-centric trajectory prediction

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:34:38.875831Z

Source-reported events for the cited work

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