MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving | Paper Citation Record

Observation e64d1923-c86c-4a5d-9140-2d82c2fc6823 · outbound

This paper cites Modeling interactions between autonomous agents in a multi-agent self-awareness architecture,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Modeling interactions between autonomous agents in a multi-agent self-awareness architecture,

Reference 1

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Observation 8440c05c-3c91-4856-b17a-528746be26ed · outbound

This paper cites Privacy-concealing coopera- tive perception for bev scene segmentation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Privacy-concealing coopera- tive perception for bev scene segmentation,

Reference 2

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Observation 3ca7f860-fd39-41b9-8904-2375d021849b · outbound

This paper cites Nitedr: Nighttime image de-raining with cross-view sensor cooperative learning for dynamic driving scenes,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Nitedr: Nighttime image de-raining with cross-view sensor cooperative learning for dynamic driving scenes,

Reference 3

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Observation b2c1275c-5e36-465d-95bc-391636c3140d · outbound

This paper cites Ubtransformer: Uncertainty-based transformer model for complex scenarios detection in autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Ubtransformer: Uncertainty-based transformer model for complex scenarios detection in autonomous driving,

Reference 4

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Observation 890ff969-2f36-4564-b3ab-44e8750f5d6e · outbound

This paper cites Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook,

Reference 5

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Observation de9cfdee-5272-458f-93f6-9d699031035e · outbound

This paper cites Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation,

Reference 6

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Observation 229fb365-9e5d-412d-aff8-5adc890f0367 · outbound

This paper cites Synet: A synergistic network for 3d object detection through geometric-semantic-based multi- interaction fusion,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Synet: A synergistic network for 3d object detection through geometric-semantic-based multi- interaction fusion,

Reference 7

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Observation 65067286-d310-498b-ba96-d6aee888b96c · outbound

This paper cites Multi-sensor fusion and cooperative perception for autonomous driving: A review,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Multi-sensor fusion and cooperative perception for autonomous driving: A review,

Reference 8

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Observation 759276d6-7977-4339-b542-b44ada66d543 · outbound

This paper cites Cg-mae: Bev masked autoencoders based on cross-modal guidance for 3d object detection in autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Cg-mae: Bev masked autoencoders based on cross-modal guidance for 3d object detection in autonomous driving,

Reference 9

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Observation 2626b1f7-859f-4019-ab1b-2ecda03471a2 · outbound

This paper cites Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,

Reference 10

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Observation f0f013fd-9782-42ce-a3f7-8102ff50aef6 · outbound

This paper cites Mapfusion: A novel bev feature fusion network for multi-modal map construction,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Mapfusion: A novel bev feature fusion network for multi-modal map construction,

Reference 11

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Observation d60c57d8-d1be-4e1e-98bb-91f5c9e6ca9a · outbound

This paper cites Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,

Reference 12

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Observation bedfc64e-e907-4c78-89b6-47aa0557ae82 · outbound

This paper cites Mta: Multimodal task alignment for bev perception and captioning,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Mta: Multimodal task alignment for bev perception and captioning,

Reference 13

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Observation 6a59ece2-7293-45b8-b5d0-36daf918b63d · outbound

This paper cites M3net: Multimodal multi-task learning for 3d detection, segmentation, and occupancy prediction in autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving M3net: Multimodal multi-task learning for 3d detection, segmentation, and occupancy prediction in autonomous driving,

Reference 14

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Observation c8fe9a96-a2e6-4f93-a78c-7840926c2a16 · outbound

This paper cites Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration,

Reference 15

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Observation b092e448-5338-4472-9cde-e962bac1be24 · outbound

This paper cites M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation

Reference 16

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Observation 469c5d0a-cc65-4d49-84d2-2ed33cccd50f · outbound

This paper cites Unitr: A unified and efficient multi-modal transformer for bird’s-eye- view representation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Unitr: A unified and efficient multi-modal transformer for bird’s-eye- view representation,

Reference 17

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Observation 4579b44b-b931-4409-b68d-5a6ead173116 · outbound

This paper cites Maskbev: Towards a unified framework for bev detection and map seg- mentation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Maskbev: Towards a unified framework for bev detection and map seg- mentation,

Reference 18

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Observation a42ce000-adde-4a51-9e7e-88c117f2fb7d · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving nuscenes: A multimodal dataset for autonomous driving,

Reference 19

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Observation da11ec59-1e35-47db-814c-eaa454b91a4b · outbound

This paper cites Deep learning for 3d point clouds: A survey,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Deep learning for 3d point clouds: A survey,

Reference 20

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Observation 846b80c6-9595-4eeb-8a48-687999043179 · outbound

This paper cites Automotive radars: A review of signal processing techniques,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Automotive radars: A review of signal processing techniques,

Reference 21

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Observation 0656571f-d805-4534-8683-37b4488687a6 · outbound

This paper cites Static multitarget- based autocalibration of rgb cameras, 3-d radar, and 3-d lidar sensors,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Static multitarget- based autocalibration of rgb cameras, 3-d radar, and 3-d lidar sensors,

Reference 22

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Observation a3101ce2-24d0-47f4-ba58-ccb4ce82025e · outbound

This paper cites High dimensional frustum pointnet for 3d object detection from camera, lidar, and radar,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving High dimensional frustum pointnet for 3d object detection from camera, lidar, and radar,

Reference 23

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Observation 4bb97308-05d1-4692-af81-355b95224161 · outbound

This paper cites Ezfusion: A close look at the integration of lidar, millimeter-wave radar, and camera for accurate 3d object detection and tracking,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Ezfusion: A close look at the integration of lidar, millimeter-wave radar, and camera for accurate 3d object detection and tracking,

Reference 24

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Observation 90675b89-f8ed-4296-9fdb-7ec7fffd4cba · outbound

This paper cites Camera, lidar, and radar sensor fusion based on bayesian neural network (clr-bnn),.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Camera, lidar, and radar sensor fusion based on bayesian neural network (clr-bnn),

Reference 25

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Observation 9d803f52-58a1-4609-a425-db4c6a2a427d · outbound

This paper cites Mt-detr: Robust end-to-end multimodal detection with confidence fusion,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Mt-detr: Robust end-to-end multimodal detection with confidence fusion,

Reference 26

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Observation 8533b8b2-b7c9-44ea-8287-cb44645d9a92 · outbound

This paper cites Rcm-fusion: Radar-camera multi-level fusion for 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Rcm-fusion: Radar-camera multi-level fusion for 3d object detection,

Reference 27

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Observation d52f972b-3d8c-4481-bfb9-6f33478c2d0c · outbound

This paper cites Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,

Reference 28

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Observation 342399fa-24e9-48a5-96ca-6d42f9e0803b · outbound

This paper cites Simple- bev: What really matters for multi-sensor bev perception?.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Simple- bev: What really matters for multi-sensor bev perception?

Reference 29

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Observation 46cb9254-40e6-44d1-8b11-8831a854f732 · outbound

This paper cites Multifusionnet: Spatio-temporal camera-radar fusion in dynamic urban environments,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Multifusionnet: Spatio-temporal camera-radar fusion in dynamic urban environments,

Reference 30

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Observation 460fe63d-f648-4151-aed9-d4cc09e0f6ad · outbound

This paper cites Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,

Reference 31

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Observation 31f780ea-a013-4d8a-bda3-9ec112e9667f · outbound

This paper cites Eliminating cross-modal conflicts in bev space for lidar-camera 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Eliminating cross-modal conflicts in bev space for lidar-camera 3d object detection,

Reference 32

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Observation db4f40f0-faaf-4c2d-a5b2-20bf94051042 · outbound

This paper cites Henet: Hybrid encoding for end-to-end multi-task 3d perception from multi-view cameras,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Henet: Hybrid encoding for end-to-end multi-task 3d perception from multi-view cameras,

Reference 33

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Observation 0862b9e5-7961-4018-840d-d1eb7f45e6db · outbound

This paper cites Adversarial multi-task learning for liver tumor segmentation, dynamic enhancement regression, and classification,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Adversarial multi-task learning for liver tumor segmentation, dynamic enhancement regression, and classification,

Reference 34

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Observation e849de77-3455-455e-b517-faa577495701 · outbound

This paper cites Quadbev: An efficient quadruple-task perception framework via birds’- eye-view representation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Quadbev: An efficient quadruple-task perception framework via birds’- eye-view representation,

Reference 35

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Observation 7cbf1193-167e-4d05-b287-ef5e2acbaa36 · outbound

This paper cites Msc-bench: Benchmarking and analyzing multi-sensor corrup- tion for driving perception,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Msc-bench: Benchmarking and analyzing multi-sensor corrup- tion for driving perception,

Reference 36

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Observation 65542c8e-b448-4b02-af85-c988d8694193 · outbound

This paper cites Sgformer: Semantic-geometry fusion transformer for multi-modal 3d panoptic segmentation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Sgformer: Semantic-geometry fusion transformer for multi-modal 3d panoptic segmentation,

Reference 37

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Observation 09fde5e4-fc1f-436f-9ed5-eb3582c883af · outbound

This paper cites Graphbev: Towards robust bev feature alignment for multi-modal 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Graphbev: Towards robust bev feature alignment for multi-modal 3d object detection,

Reference 38

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Observation 87488360-d215-4f39-a3a6-b7dc2a2392b3 · outbound

This paper cites Cmgfa: A bev segmentation model based on cross-modal group-mix attention feature aggregator,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Cmgfa: A bev segmentation model based on cross-modal group-mix attention feature aggregator,

Reference 39

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Observation 43cbca86-7873-42a3-aa25-eb6325a5d6f1 · outbound

This paper cites Filter-based deep-compression with global average pooling for convolutional net- works,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Filter-based deep-compression with global average pooling for convolutional net- works,

Reference 40

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Observation c455d966-28be-42f5-b98d-f6c3fa5ee43b · outbound

This paper cites Convolution in convolution for network in network,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Convolution in convolution for network in network,

Reference 41

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Observation 9612e762-e08c-4c7d-ba0f-5069fb06c5a0 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Deep Learning using Rectified Linear Units (ReLU)

Reference 42

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Observation 073e0bf3-7600-49df-ba54-47b9c0ee4882 · outbound

This paper cites Adaptive mixtures of local experts,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Adaptive mixtures of local experts,

Reference 43

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Observation f1df9848-08b4-421c-be04-3edf93d31347 · outbound

This paper cites Long-tailed recog- nition by routing diverse distribution-aware experts,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Long-tailed recog- nition by routing diverse distribution-aware experts,

Reference 44

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Observation b27d6334-6f30-4e0b-af3f-806c6c46372d · outbound

This paper cites Adamv-moe: Adaptive multi-task vision mixture-of-experts,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Adamv-moe: Adaptive multi-task vision mixture-of-experts,

Reference 45

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Observation 3838c5da-951b-427f-81d7-8386f291a2ca · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 46

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Observation 50f3f963-108b-4839-bf13-b2219372563e · outbound

This paper cites Focal loss for dense object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Focal loss for dense object detection,

Reference 47

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Observation d62c801e-65c9-4258-96f9-ba622f3955b9 · outbound

This paper cites X-align: Cross-modal cross-view alignment for bird’s-eye-view segmentation,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving X-align: Cross-modal cross-view alignment for bird’s-eye-view segmentation,

Reference 48

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Observation 01295c1f-93ec-4ecb-9cb3-79f37c128e1a · outbound

This paper cites Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.499116Z

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Observation 0d7d8dc8-4153-4348-bd39-cd65250b1900 · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Pointpillars: Fast encoders for object detection from point clouds,

Reference 50

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Observation 3d3a5b56-2685-468f-8fbc-6864432b576c · outbound

This paper cites Center-based 3d object detection and tracking,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Center-based 3d object detection and tracking,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.507329Z

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Observation 0675447a-5a47-4064-a529-0986c4443754 · outbound

This paper cites Focalformer3d: focusing on hard instance for 3d object JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 12 detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Focalformer3d: focusing on hard instance for 3d object JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 12 detection,

Reference 52

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Observation e2283725-a02b-44a5-afb7-94f6a5becb31 · outbound

This paper cites Safdnet: A simple and effective network for fully sparse 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Safdnet: A simple and effective network for fully sparse 3d object detection,

Reference 53

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Observation 81497507-0f8d-42e5-8533-5089d8b19347 · outbound

This paper cites Pointpainting: Se- quential fusion for 3d object detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Pointpainting: Se- quential fusion for 3d object detection,

Reference 54

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Observation 86c311eb-dec4-40e6-90f7-1eda61f53ff0 · outbound

This paper cites Futr3d: A unified sensor fusion framework for 3d detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Futr3d: A unified sensor fusion framework for 3d detection,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.524956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7eb9173-a257-4fc9-b83e-78464ffa692a · outbound

This paper cites Multimodal virtual point 3d detection,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Multimodal virtual point 3d detection,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.529684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 755e74f2-fef4-45cd-a98b-2810c678b7e5 · outbound

This paper cites Mbfusion: A new multi-modal bev feature fusion method for hd map construction,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Mbfusion: A new multi-modal bev feature fusion method for hd map construction,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.534445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42b8c2e3-d8b7-46f9-bc55-550ac56570dd · outbound

This paper cites Maptr: Structured modeling and learning for online vectorized hd map construction,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Maptr: Structured modeling and learning for online vectorized hd map construction,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.539018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7c271ee-abcf-4f79-93f5-02d0e392547b · outbound

This paper cites Smab: Simple multimodal attention for effective bev fusion,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Smab: Simple multimodal attention for effective bev fusion,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.543137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e29177-7dad-405d-88bf-9799b006a4de · outbound

This paper cites Henet++: Hybrid encoding and multi-task learning for 3d perception and end-to-end autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Henet++: Hybrid encoding and multi-task learning for 3d perception and end-to-end autonomous driving,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.547685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2224070-e2e8-458e-8bc4-bb74a8152c2d · outbound

This paper cites Unisparsebev: A multi-task learning framework with unified sparse query for autonomous driving,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Unisparsebev: A multi-task learning framework with unified sparse query for autonomous driving,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.552206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56df480d-4e37-45a6-a178-df4b0ae6125b · outbound

This paper cites Daocc: 3d object detection assisted multi- sensor fusion for 3d occupancy prediction,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Daocc: 3d object detection assisted multi- sensor fusion for 3d occupancy prediction,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.556848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c430d8-29d9-4b82-a2df-fc36d08a9f65 · outbound

This paper cites Decoupled weight decay regularization,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Decoupled weight decay regularization,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.561341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2eb1ac7-f140-42f4-8418-aecf9fa6e9af · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates,.

MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving Super-convergence: Very fast training of neural networks using large learning rates,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-31T20:38:37.565303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers