Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:40:03.893730Z
Paper Citation Record · LEDGER
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach
As of 13 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2411.13302.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
51 of 51 outbound references displayed
- verified exact0
- verified fuzzy41
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 568f800f-14aa-4918-98fd-4b2d03a797b7 · outbound
This paper cites Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.639269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5763cc60-e779-4e33-b290-31d4a514cefa · outbound
This paper cites Cou- pling intent and action for pedestrian crossing behavior prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Cou- pling intent and action for pedestrian crossing behavior prediction,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.624792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f6e05f4a-60f8-4c73-8e65-1a4233f97304 · outbound
This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach The Cityscapes Dataset for Semantic Urban Scene Understanding,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.610106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 67a158c8-3c86-42f0-a035-6ae7ca028433 · outbound
This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.596405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0c3aecce-d412-4c3a-abd8-bb1415242f46 · outbound
This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Are we ready for autonomous driving? the kitti vision benchmark suite,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.582226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e7d93c29-bda3-47d4-8623-2df3681921ef · outbound
This paper cites Vulnerable road users and connected autonomous vehicles interaction: A survey,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Vulnerable road users and connected autonomous vehicles interaction: A survey,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.567220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b3c3c09f-c908-4a7a-b231-34f167fd709d · outbound
This paper cites Vulnerable Road Users, Position/Policy Statement, NATIONAL SAFETY COUNCIL,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Vulnerable Road Users, Position/Policy Statement, NATIONAL SAFETY COUNCIL,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.552933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 89530718-1beb-488d-8d21-32943396e354 · outbound
This paper cites VULNERABLE ROAD USER (VRU) PROTECTION,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach VULNERABLE ROAD USER (VRU) PROTECTION,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.538862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation a40eba53-da14-4277-ab68-7ec3e8e8fe52 · outbound
This paper cites Spatiotemporal relationship reasoning for pedestrian intent prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Spatiotemporal relationship reasoning for pedestrian intent prediction,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.524863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation f9dd9b16-b485-4d70-beb1-8d9c7b237ebe · outbound
This paper cites IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.716516Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd0adcbd-ee5a-4aed-81a1-768f790586cd · outbound
This paper cites Fussi-net: Fusion of spatio-temporal skeletons for intention prediction network,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Fussi-net: Fusion of spatio-temporal skeletons for intention prediction network,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.510210Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 9ec45aff-4c6d-4f2b-a8b5-14236fa212b2 · outbound
This paper cites Context model for pedestrian intention prediction using factored latent-dynamic condi- tional random fields,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Context model for pedestrian intention prediction using factored latent-dynamic condi- tional random fields,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.495361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 74ae767c-dad8-46c8-911f-d8f00f81e758 · outbound
This paper cites Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.480856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8365f61a-bfd2-4c39-ac66-9499c797b60c · outbound
This paper cites Intent prediction of vulnerable road users from motion trajec- tories using stacked lstm network,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Intent prediction of vulnerable road users from motion trajec- tories using stacked lstm network,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.466155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 51a90498-86c8-4c07-857b-1e2893288e05 · outbound
This paper cites Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.450766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4ae50168-bd59-4604-8b2a-583fd685b62b · outbound
This paper cites Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.434535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 90a4ec05-349a-4e27-99f1-89a6d010f039 · outbound
This paper cites Bifold and semantic reasoning for pedestrian behavior prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Bifold and semantic reasoning for pedestrian behavior prediction,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.419488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e012cb25-675e-43f3-b0df-31c889fc1d0e · outbound
This paper cites Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.750274Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1522eee-9dc4-4fc8-8245-f8c82b8ab1a6 · outbound
This paper cites Graph-sim: A graph-based spatiotemporal interaction mod- elling for pedestrian action prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Graph-sim: A graph-based spatiotemporal interaction mod- elling for pedestrian action prediction,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.404550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 0bc10c02-f63f-4a4e-b6f2-21d04127dc86 · outbound
This paper cites Benchmark for evaluating pedestrian action prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Benchmark for evaluating pedestrian action prediction,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.389655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 176d0f86-1ff7-4fc7-a48a-b45081c16553 · outbound
This paper cites Social aware multi- modal pedestrian crossing behavior prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Social aware multi- modal pedestrian crossing behavior prediction,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.375006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 79b3d094-d01e-4746-8fb6-d09c7010cd3b · outbound
This paper cites Multi-modal hybrid architecture for pedestrian action prediction,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Multi-modal hybrid architecture for pedestrian action prediction,
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.769103Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3546144-e90a-4cd4-895d-f4d49cae9d6b · outbound
This paper cites Context-aware captions from context-agnostic supervision,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Context-aware captions from context-agnostic supervision,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.351676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation dbb340db-5581-4307-bc4a-47099257c92a · outbound
This paper cites Grounding visual explanations,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Grounding visual explanations,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.336704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 06854ead-d220-4a54-b6f7-183f00965aa0 · outbound
This paper cites Show, attend and tell: Neural image caption generation with visual attention,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Show, attend and tell: Neural image caption generation with visual attention,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.321995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 73bac432-0cae-4bfe-95fd-29bd18519ba7 · outbound
This paper cites Deep learning,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Deep learning,
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.786667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0bc3f2bc-5344-468a-a50d-73b6530e7058 · outbound
This paper cites Textual explanations for self-driving vehicles,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Textual explanations for self-driving vehicles,
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.790784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d28c0620-92c0-427f-9d2d-dc345db748b4 · outbound
This paper cites PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.794969Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25d4b08d-5d15-4fdf-89df-51030b0eb142 · outbound
This paper cites A peek into the reasoning of neural networks: Interpreting with structural visual concepts,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach A peek into the reasoning of neural networks: Interpreting with structural visual concepts,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.288849Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation dd806b61-2a96-47cc-af7b-d2e74a17fe46 · outbound
This paper cites Drive: Deep reinforced accident anticipation with visual explanation,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Drive: Deep reinforced accident anticipation with visual explanation,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.274600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation ba0c6d4d-9b07-4d7c-abe2-3179f0852e98 · outbound
This paper cites Learning transferable visual models from natural language supervision,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning transferable visual models from natural language supervision,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.260548Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 4abd2fe3-18e1-4546-8eb5-fc2cf823376a · outbound
This paper cites Multimodal contrastive training for visual representation learning,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Multimodal contrastive training for visual representation learning,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.245922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b808f374-c887-4a54-83be-9003ac329812 · outbound
This paper cites Shared cross-modal trajectory prediction for autonomous driving,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Shared cross-modal trajectory prediction for autonomous driving,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.231322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 15fd85e8-2b30-4975-a472-22a61e8b1bb5 · outbound
This paper cites Pedestrian inten- tion prediction for autonomous driving using a multiple stakeholder perspective model,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian inten- tion prediction for autonomous driving using a multiple stakeholder perspective model,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.216666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 88216d5f-0ceb-4fa5-85d4-b3ceb6a868c6 · outbound
This paper cites Joint intention and trajectory prediction based on transformer,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Joint intention and trajectory prediction based on transformer,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.202042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation e0e8b0f5-df33-4674-8124-51826c398201 · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.829524Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b91b304d-7b8f-4f03-82ad-8c02bde4ffc1 · outbound
This paper cites Learning phrase representations using rnn encoder-decoder for statistical machine translation,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning phrase representations using rnn encoder-decoder for statistical machine translation,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.187401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 98a66487-ecdc-4f68-943c-20f90c88b21d · outbound
This paper cites Swin transformer v2: Scaling up capacity and resolution,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Swin transformer v2: Scaling up capacity and resolution,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.171750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 518a878e-8eba-461a-897e-fb2827469745 · outbound
This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Sentence-bert: Sentence embeddings using siamese bert-networks,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.156832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 42dcefa3-7547-410f-83ca-73609671d087 · outbound
This paper cites Attention is all you need,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Attention is all you need,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.142526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8b916601-dfe5-494d-aeb5-40b01c134e68 · outbound
This paper cites Pedx: Bench- mark dataset for metric 3-d pose estimation of pedestrians in complex urban intersections,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedx: Bench- mark dataset for metric 3-d pose estimation of pedestrians in complex urban intersections,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.128592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation aebda185-ae3f-4736-81f3-cadb7a499fd6 · outbound
This paper cites nuscenes: A multimodal dataset for autonomous driving,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach nuscenes: A multimodal dataset for autonomous driving,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.114367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 39df48ca-33c1-4177-aa32-124a29a055d3 · outbound
This paper cites Intraclass correlations: uses in assessing rater reliability.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Intraclass correlations: uses in assessing rater reliability
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.858818Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6733adf-19b0-4473-953b-00b6165d84f3 · outbound
This paper cites Semi-supervised classification with graph convolutional networks,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Semi-supervised classification with graph convolutional networks,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.089604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation efa28b45-4881-43da-8f93-176d882935b2 · outbound
This paper cites Imagenet large scale visual recognition challenge,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Imagenet large scale visual recognition challenge,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.072861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 13f3b255-7bbf-4acc-8a76-aaa13f3d7bc7 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach An image is worth 16x16 words: Transformers for image recognition at scale,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.058470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5412dace-c436-4833-ad98-cf17642c8ea4 · outbound
This paper cites Learning deep transformer models for machine translation,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Learning deep transformer models for machine translation,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.042105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 874c0def-2dca-4b90-a628-7da34f793aad · outbound
This paper cites Adaptive input representations for neural language modeling,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Adaptive input representations for neural language modeling,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.027120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b6fc43bc-29f3-4637-916d-3a4109968276 · outbound
This paper cites Effective approaches to attention-based neural machine translation,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Effective approaches to attention-based neural machine translation,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T16:40:04.011714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 414845fc-03fb-464f-aa81-25d71811f9c1 · outbound
This paper cites Pedestrian Intention Prediction: A Multi-task Perspective.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Pedestrian Intention Prediction: A Multi-task Perspective
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.888954Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1484627d-73cf-48fd-8fcd-f97a7dd25e0d · outbound
This paper cites Glove: Global vectors for word representation,.
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach Glove: Global vectors for word representation,
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:40:03.893730Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.