Capturing Fine-Grained Alignments Improves 3D Affordance Detection | Paper Citation Record

Observation 0e00b700-2aba-4d11-b894-6736bb655ab2 · outbound

This paper cites A4T: Hi- erarchical Affordance Detection for Transparent Ob- jects Depth Reconstruction and Manipulation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A4T: Hi- erarchical Affordance Detection for Transparent Ob- jects Depth Reconstruction and Manipulation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.881383Z

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Observation f5be5950-39e1-4bc5-8fd1-8155ba865d07 · outbound

This paper cites Deep Affordance-Grounded Sensorimotor Ob- ject Recognition,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Deep Affordance-Grounded Sensorimotor Ob- ject Recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.858502Z

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Observation 2dd4be0a-71ee-4226-aafe-e5e76fa50244 · outbound

This paper cites Af- fordance Transfer Learning for Human-Object Inter- action Detection,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Af- fordance Transfer Learning for Human-Object Inter- action Detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.823763Z

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Observation 389e5eeb-5ccf-4dd5-8426-db0ddb25339a · outbound

This paper cites Pre- dicting 3D Human Dynamics From Video,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Pre- dicting 3D Human Dynamics From Video,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.794811Z

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Observation a05d1ed5-1aa2-40b1-a142-0aa2ba835007 · outbound

This paper cites Predicting hu- man activities using stochastic grammar,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Predicting hu- man activities using stochastic grammar,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.754420Z

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Observation f1576fad-b1d1-4a5e-8ca9-780480961cd8 · outbound

This paper cites Af- fordance grounding from demonstration video to tar- get image,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Af- fordance grounding from demonstration video to tar- get image,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.724545Z

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Observation c46d9943-58af-46bd-bde5-b239c2804d45 · outbound

This paper cites Affor- dance Research in Developmental Robotics: A Sur- vey,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Affor- dance Research in Developmental Robotics: A Sur- vey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.698748Z

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Observation 8bf83273-906d-4e23-8897-13e284478dbc · outbound

This paper cites A survey of visual affordance recognition based on deep learning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A survey of visual affordance recognition based on deep learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.665818Z

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Observation f6eca6ea-bb28-45c9-8b7f-3f60cd28631e · outbound

This paper cites Visual affor- dance and function understanding: A survey,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Visual affor- dance and function understanding: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.641003Z

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Observation 813784fa-daec-491c-a1f9-f6578c10face · outbound

This paper cites 3d af- fordancenet: A benchmark for visual object affordance understanding,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection 3d af- fordancenet: A benchmark for visual object affordance understanding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.616376Z

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Observation 98d5ca03-0004-47ee-9350-8570c1c539e8 · outbound

This paper cites Open-vocabulary affordance detection in 3d point clouds,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Open-vocabulary affordance detection in 3d point clouds,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.592290Z

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Observation b2c4ffa2-6fe9-4573-9326-8e627c69de51 · outbound

This paper cites 3D ShapeNets: A Deep Representation for Volumetric Shapes,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection 3D ShapeNets: A Deep Representation for Volumetric Shapes,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.566059Z

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Observation 3ea55269-ad29-4b67-90e8-00083640a5cb · outbound

This paper cites Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.537935Z

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Observation bf95c1f5-366f-4885-bb6a-63e5bcd915b8 · outbound

This paper cites Open-vocabulary affordance detection using knowledge distillation and text-point correlation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Open-vocabulary affordance detection using knowledge distillation and text-point correlation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.505981Z

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Observation e9dd02c6-a436-4616-bbbd-4f4f551fab21 · outbound

This paper cites Transductive zero-shot learning for 3d point cloud classification,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Transductive zero-shot learning for 3d point cloud classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.471404Z

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Observation 80fc2dce-bb5f-49ff-85b9-25d0b9176321 · outbound

This paper cites Generative zero-shot learning for semantic seg- mentation of 3d point clouds,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Generative zero-shot learning for semantic seg- mentation of 3d point clouds,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.443595Z

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Observation d5f1b7c3-1438-46c2-8e2e-274e41e8e0f3 · outbound

This paper cites Zero- shot learning of 3d point cloud objects,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Zero- shot learning of 3d point cloud objects,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.226904Z

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Observation 8b689183-96d4-4da2-9204-4a9e5dacf1cc · outbound

This paper cites InstructBLIP 2: Extending Vision- Language Models with Fine-Grained Instruction Tun- ing,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection InstructBLIP 2: Extending Vision- Language Models with Fine-Grained Instruction Tun- ing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.202062Z

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Observation 11b3a8ef-fa14-4743-b86d-63adb9ee50d6 · outbound

This paper cites Flamingo: A Visual Language Model for Few-Shot Learning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Flamingo: A Visual Language Model for Few-Shot Learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.174695Z

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Observation ebd797fc-6114-4351-ac9f-8f36949bda24 · outbound

This paper cites Attention Is All You Need.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Attention Is All You Need

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.149453Z

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Observation 3b9763ef-807e-4840-a6a8-7c0f2621be4b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.129739Z

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Observation 9fbd1cba-b91e-40cc-97ab-a764ad9fda82 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Training Compute-Optimal Large Language Models

Reference 22

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Observation c7af728e-d662-4ec5-8b10-a56cc6d4d560 · outbound

This paper cites Detecting object affordances with Convo- lutional Neural Networks,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Detecting object affordances with Convo- lutional Neural Networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.101474Z

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Observation b456abc3-a3e0-484a-892e-1a8a1067aac0 · outbound

This paper cites Affordancenet: An end-to-end deep learning approach for object af- fordance detection,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Affordancenet: An end-to-end deep learning approach for object af- fordance detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.073919Z

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Observation 574333d8-32eb-43e7-949a-fd05802d09ea · outbound

This paper cites Object-based affordances detection with Convolutional Neural Networks and dense Conditional Random Fields,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Object-based affordances detection with Convolutional Neural Networks and dense Conditional Random Fields,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:38.044353Z

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Observation f65f59b2-e2e0-4dc8-b1ac-aab9b5560997 · outbound

This paper cites A multi-scale cnn for af- fordance segmentation in rgb images,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A multi-scale cnn for af- fordance segmentation in rgb images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.997854Z

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Observation a4fd111a-31f1-42ec-ba25-3ebf611e32ad · outbound

This paper cites A deep learning approach to object affordance segmentation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A deep learning approach to object affordance segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.951998Z

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Observation e3ce8c61-2827-49d2-8c25-042c3b2444ed · outbound

This paper cites Cerberus transformer: Joint semantic, affordance and attribute parsing,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Cerberus transformer: Joint semantic, affordance and attribute parsing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.928066Z

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Observation c237df84-8db4-4e9c-b5d6-76e8649a4621 · outbound

This paper cites Learning affordance grounding from exocentric im- ages,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Learning affordance grounding from exocentric im- ages,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.890230Z

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Observation 555c8fcb-3714-4496-aca1-bce57ca6553b · outbound

This paper cites Affordancellm: Grounding affordance from vision language models,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Affordancellm: Grounding affordance from vision language models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.863300Z

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Observation 57a26f55-b690-41d0-a2f0-cac02ca36296 · outbound

This paper cites Visual instruc- tion tuning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Visual instruc- tion tuning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.832914Z

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Observation aa93a09e-f258-441b-acf0-e8579292e685 · outbound

This paper cites Semantic labeling of 3d point clouds with object affordance for robot ma- nipulation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Semantic labeling of 3d point clouds with object affordance for robot ma- nipulation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.799409Z

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Observation 0168a083-cef6-4539-8210-02e6dc873a08 · outbound

This paper cites Affordance detection for task-specific grasping using deep learning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Affordance detection for task-specific grasping using deep learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.769920Z

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Observation 1651265b-0e6c-475d-ae4c-30729755eb74 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 34

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Observation 516cb30f-7334-41db-857a-bd2b992c2ef1 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.728084Z

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Observation 766f8039-1870-4082-a716-d7c42a4add5a · outbound

This paper cites Dynamic graph cnn for learning on point clouds,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Dynamic graph cnn for learning on point clouds,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.697043Z

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Observation 83a9fd46-cc76-46f8-aca4-c5ed4b202c08 · outbound

This paper cites Point Transformer,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Point Transformer,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.661790Z

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Observation 15eb2b83-ffed-40b1-ac27-ff97060dfe9c · outbound

This paper cites A robustly opti- mized BERT pre-training approach with post-training,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A robustly opti- mized BERT pre-training approach with post-training,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.632212Z

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Observation 9fd8db11-7c57-4f99-b177-d96c9eaca4d9 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:35.825141Z

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Observation d61bb13a-e619-42f1-8d77-3d175699ebe5 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Learning Transferable Visual Models From Natural Language Supervision,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.577061Z

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Observation 5fc8a9c9-4af4-40b8-9928-48fcb5cdbfe2 · outbound

This paper cites Multimodal Alignment and Fu- sion: A Survey,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Multimodal Alignment and Fu- sion: A Survey,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:35.844233Z

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Observation b9941c22-7afd-418d-89d8-94ef2d5624a4 · outbound

This paper cites Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.543973Z

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Observation 43524f2e-ecd0-45db-a5d3-72770bf4a452 · outbound

This paper cites Multimodal repre- sentation learning for tourism recommendation with two-tower architecture,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Multimodal repre- sentation learning for tourism recommendation with two-tower architecture,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.518422Z

Source-reported events for the cited work

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Observation c235b6ce-2702-42f1-accc-0afeebd19d8c · outbound

This paper cites I can listen but cannot read: An evaluation of two-tower multi- modal systems for instrument recognition,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection I can listen but cannot read: An evaluation of two-tower multi- modal systems for instrument recognition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.490452Z

Source-reported events for the cited work

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Observation f4c84bb9-a486-4f6b-a409-01c105a86a95 · outbound

This paper cites Bridgetower: Building bridges between encoders in vision-language representation learning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Bridgetower: Building bridges between encoders in vision-language representation learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.457987Z

Source-reported events for the cited work

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Observation dc048dec-31b1-42fc-84b4-c01cc30053b1 · outbound

This paper cites Be- yond Two-Tower Matching: Learning Sparse Retriev- able Cross-Interactions for Recommendation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Be- yond Two-Tower Matching: Learning Sparse Retriev- able Cross-Interactions for Recommendation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.406626Z

Source-reported events for the cited work

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Observation 157ccc69-635e-4627-afa0-b5ea6c0fa0ac · outbound

This paper cites Touchformer: A Transformer-based two-tower architecture for tactile temporal signal classification,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Touchformer: A Transformer-based two-tower architecture for tactile temporal signal classification,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.381193Z

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Observation b6eaa045-dd91-4faa-842f-cd125dd15eb1 · outbound

This paper cites Mix-tower: Light visual question answering framework based on exclusive self-attention mechanism,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Mix-tower: Light visual question answering framework based on exclusive self-attention mechanism,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.342148Z

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Observation 07ca6b59-85eb-4a07-960e-de66f3983f2a · outbound

This paper cites Towards artificial general intelligence via a multimodal foundation model,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Towards artificial general intelligence via a multimodal foundation model,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.304468Z

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Observation 48e2d963-7456-4ab0-b662-7c5d1bb7a49f · outbound

This paper cites Multimodal Reranking for Knowledge- Intensive Visual Question Answering,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Multimodal Reranking for Knowledge- Intensive Visual Question Answering,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.261409Z

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 b1d06ad8-da76-4b7d-9b99-c413003e4547 · outbound

This paper cites Dif- ferentiable cross-modal hashing via multimodal trans- formers,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Dif- ferentiable cross-modal hashing via multimodal trans- formers,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.235788Z

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 02b33984-3811-4660-94ce-67e37b5ee21b · outbound

This paper cites Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.210564Z

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 d26529e8-d484-4d4d-a4db-cc281b5b394c · outbound

This paper cites Fusing information from multifidelity computer models of physical sys- tems,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Fusing information from multifidelity computer models of physical sys- tems,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.182231Z

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 b32b9c27-effe-46f9-af17-f4e6248fedc5 · outbound

This paper cites Seg- Net: A Deep Convolutional Encoder-Decoder Archi- tecture for Image Segmentation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Seg- Net: A Deep Convolutional Encoder-Decoder Archi- tecture for Image Segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.162429Z

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 e012b1a5-e8a9-4247-8f5a-72a14c7e5d12 · outbound

This paper cites Sensor fusion of camera and LiDAR raw data for vehicle detection,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Sensor fusion of camera and LiDAR raw data for vehicle detection,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.126389Z

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 c9687933-4d52-49dc-9f47-974d117e2d0d · outbound

This paper cites A Model-Level Fusion-Based Multi-Modal Object Detection and Recognition Method,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A Model-Level Fusion-Based Multi-Modal Object Detection and Recognition Method,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.099882Z

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 7e9ba028-cc50-43d3-b1f1-f1584326c9cd · outbound

This paper cites Learning to combine local models for facial Action Unit detec- tion,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Learning to combine local models for facial Action Unit detec- tion,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.072641Z

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 dbf5b439-85ca-4f2c-a295-a8ae18ba4a09 · outbound

This paper cites Polos: Multimodal Metric Learning from Human Feedback for Image Captioning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Polos: Multimodal Metric Learning from Human Feedback for Image Captioning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.050351Z

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 22993178-5dff-44a7-8985-b81a889f91d7 · outbound

This paper cites Hierarchical Feature Fusion Network for Salient Object Detection,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Hierarchical Feature Fusion Network for Salient Object Detection,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:37.025923Z

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 10013bb5-63eb-4188-ad44-ba7fcd2643af · outbound

This paper cites DenseFuse: A Fusion Approach to Infrared and Visible Images,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection DenseFuse: A Fusion Approach to Infrared and Visible Images,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.984794Z

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 be9b7e67-cfc0-46b4-bcc9-e05af9636ae7 · outbound

This paper cites Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affec- tive Computing,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affec- tive Computing,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.959099Z

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 9b254963-3447-4e66-9925-d6ae53e63000 · outbound

This paper cites A hierarchical feature fusion frame- work for adaptive visual tracking,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection A hierarchical feature fusion frame- work for adaptive visual tracking,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.930252Z

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 77063f44-bda1-4c2e-808f-bc107ca93ecb · outbound

This paper cites Model level fusion of edge histogram descriptors and gabor wavelets for landmine detection with ground penetrating radar,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Model level fusion of edge histogram descriptors and gabor wavelets for landmine detection with ground penetrating radar,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.904553Z

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 1f525350-1fb3-4d28-b752-7ac2163de450 · outbound

This paper cites Towards Raw Sensor Fu- sion in 3D Object Detection,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Towards Raw Sensor Fu- sion in 3D Object Detection,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.856881Z

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 ba86160e-f83c-4622-828b-09d8074bf7c5 · outbound

This paper cites Design of a Low-Level Radar and Time-of-Flight Sen- sor Fusion Framework,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Design of a Low-Level Radar and Time-of-Flight Sen- sor Fusion Framework,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.795207Z

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 dc5beebc-298c-4d71-b5b3-24df6548d886 · outbound

This paper cites Guided Deep Decoder: Unsupervised Image Pair Fusion,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Guided Deep Decoder: Unsupervised Image Pair Fusion,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.757432Z

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 80e4baf2-6a32-4c30-b854-cb9b8c0287c4 · outbound

This paper cites Decision-Level Data Fusion in Quality Control and Predictive Main- tenance,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Decision-Level Data Fusion in Quality Control and Predictive Main- tenance,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.723183Z

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 614807a0-1fed-4010-80e1-e513f8a1eb69 · outbound

This paper cites ViLT: Vision and lan- guage transformer without convolution or region su- pervision.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection ViLT: Vision and lan- guage transformer without convolution or region su- pervision

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.695855Z

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 7993308d-6174-441e-8c9a-a8737c1bbdf2 · outbound

This paper cites VLMo: Unified Vision-Language Pre-Training with Mixture of Modal- ity Experts,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection VLMo: Unified Vision-Language Pre-Training with Mixture of Modal- ity Experts,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.669490Z

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 e54610a1-e00f-45ac-a0c4-335956a45cd6 · outbound

This paper cites BLIP: Boot- strapping language image pre-training for unified vi- sion language understanding and generation,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection BLIP: Boot- strapping language image pre-training for unified vi- sion language understanding and generation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.632313Z

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 af77ee36-574d-4085-8e86-2fd793af470c · outbound

This paper cites BLIP-2: Boot- strapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection BLIP-2: Boot- strapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.603527Z

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 11ba52d7-f35b-4b16-a130-2f0773829cf2 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruc- tion Tuning,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection InstructBLIP: Towards General-purpose Vision-Language Models with Instruc- tion Tuning,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.574066Z

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 33e1aecd-4a67-4505-8116-3782aeeef652 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:36.165953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83aca980-d79e-4313-8b3e-6acea65d873c · outbound

This paper cites SimVLM: Simple Visual Language Model Pretraining with Weak Supervision,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection SimVLM: Simple Visual Language Model Pretraining with Weak Supervision,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.549214Z

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 7bd85ec4-9da9-4ef3-b60e-e43e24af7da1 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.524421Z

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 2edbe88f-c577-4590-b57b-850640eb8234 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localiza- tion, Text Reading, and Beyond,.

Capturing Fine-Grained Alignments Improves 3D Affordance Detection Qwen-VL: A Versatile Vision-Language Model for Understanding, Localiza- tion, Text Reading, and Beyond,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:36.497986Z

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.

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