Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:04:00.870139Z
Paper Citation Record · LEDGER
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation
As of 19 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2506.03675.
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 2026-08-19T06:32:44.657259+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
87 of 87 outbound references displayed
- verified exact0
- verified fuzzy57
- unresolved30
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1b258a05-46e4-45ef-bdfb-6084bf9e584a · outbound
This paper cites Fully convolu- tional networks for semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Fully convolu- tional networks for semantic segmentation,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.463645Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cbc2be4c-6205-4a5e-8d69-a3b2d74079b6 · outbound
This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.468441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f6eac92-46b4-4706-9320-7ab406e8ce3a · outbound
This paper cites Encoder-decoder with atrous separable con- volution for semantic image segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Encoder-decoder with atrous separable con- volution for semantic image segmentation,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.473059Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd0a2738-d60c-462a-a08c-9a860a46db87 · outbound
This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.477789Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7fc3c472-5236-4337-b9c0-00710d45137a · outbound
This paper cites Contour knowledge-aware perception learning for seman- tic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Contour knowledge-aware perception learning for seman- tic segmentation,
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.482334Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc321bbf-6e3f-438f-9d3b-22e9b9b7364a · outbound
This paper cites Omnisam: Omnidirectional seg- ment anything model for uda in panoramic semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Omnisam: Omnidirectional seg- ment anything model for uda in panoramic semantic segmentation,
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.486394Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3edec125-49c8-4d1f-9286-f6f1edd3799e · outbound
This paper cites Muses: The multi-sensor semantic perception dataset for driving under uncertainty,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Muses: The multi-sensor semantic perception dataset for driving under uncertainty,
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.491149Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5927e21-e05f-4ee9-bbf4-56d8874d9ed9 · outbound
This paper cites Delivering arbitrary- modal semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Delivering arbitrary- modal semantic segmentation,
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.495448Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d14dabd-e728-493f-a73b-a4a152d4f0b0 · outbound
This paper cites Indoor segmentation and support inference from rgbd images,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Indoor segmentation and support inference from rgbd images,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.111109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8d864917-01d2-40cc-960d-8de33b8fe35b · outbound
This paper cites Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.097218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7684729b-52e1-4c8c-8a7a-8331fa9631ae · outbound
This paper cites Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Learning Robust Anymodal Segmentor with Unimodal and Cross-modal Distillation
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.509320Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dada0cfc-525c-4500-9f1f-b5742af6cbf3 · outbound
This paper cites Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.082719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a66de4a8-c509-4bc3-9181-0cb903a6115d · outbound
This paper cites Learning modality- agnostic representation for semantic segmentation from any modalities,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Learning modality- agnostic representation for semantic segmentation from any modalities,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.068649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4c02518b-9a2d-4196-853e-2d865590ebbb · outbound
This paper cites Cmx: Cross-modal fusion for rgb-x semantic segmenta- tion with transformers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Cmx: Cross-modal fusion for rgb-x semantic segmenta- tion with transformers,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.054223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 85c2180f-2b79-46a1-8897-ba83e26bbc90 · outbound
This paper cites Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.526326Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60019358-77f7-4544-af4c-11f73801300b · outbound
This paper cites Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.530644Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4b7217e-9a89-473a-8f95-479c9f0055df · outbound
This paper cites X-prompt: Multi-modal visual prompt for video object segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation X-prompt: Multi-modal visual prompt for video object segmentation,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.038582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 87e16734-282d-4612-a637-01de39377f06 · outbound
This paper cites Ro- bust multimodal learning with missing modalities via parameter-efficient adaptation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Ro- bust multimodal learning with missing modalities via parameter-efficient adaptation,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.024031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d7ed4ac6-79ef-4b78-9c47-0cb2e8190850 · outbound
This paper cites DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.543430Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d483dc7a-46b7-4694-96bf-d2f64c94c675 · outbound
This paper cites Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:02.008789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4d989659-0d80-4d5f-b850-251511ee77f8 · outbound
This paper cites Context-aware interaction network for rgb-t semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Context-aware interaction network for rgb-t semantic segmentation,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.993801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e7dd0730-d7f9-4bbe-9a49-b8a13798a4d9 · outbound
This paper cites Embracing events and frames with hierarchical feature refinement network for object detection,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Embracing events and frames with hierarchical feature refinement network for object detection,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.978318Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 739022c0-43f9-4078-8430-5ad5962d06a9 · outbound
This paper cites Mawkdn: A multimodal fusion wavelet knowledge distillation approach based on cross-view attention for action recognition,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Mawkdn: A multimodal fusion wavelet knowledge distillation approach based on cross-view attention for action recognition,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.962794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6e923aa9-94b9-4009-8c9c-01e670eb183e · outbound
This paper cites Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Cpal: Cross- prompting adapter with loras for rgb+ x semantic seg- mentation,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.947259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a7957b06-7a5a-4f04-9884-19d81db84db3 · outbound
This paper cites S3f2net: Spatial- spectral-structural feature fusion network for hyperspectral image and lidar data classification,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation S3f2net: Spatial- spectral-structural feature fusion network for hyperspectral image and lidar data classification,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.930862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c5013d83-4476-4994-b741-7593ca0b105f · outbound
This paper cites T 2 ea: Target-aware taylor expansion approx- imation network for infrared and visible image fusion,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation T 2 ea: Target-aware taylor expansion approx- imation network for infrared and visible image fusion,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.915599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e1ac892b-907c-46ea-b348-451fd1f7947c · outbound
This paper cites Nuc-net: Non- uniform cylindrical partition network for efficient lidar semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Nuc-net: Non- uniform cylindrical partition network for efficient lidar semantic segmentation,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.900337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c7243d09-eeae-4c5f-a155-1e5b031e0ed9 · outbound
This paper cites Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.581807Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e395c87-633c-4aa0-ac8d-6a716ccb44f1 · outbound
This paper cites Primkd: Primary modality guided multimodal fusion for rgb-d semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Primkd: Primary modality guided multimodal fusion for rgb-d semantic segmentation,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.884761Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e30a52cb-98e5-4b6e-aaf7-a686af2fe480 · outbound
This paper cites Imagenet large scale visual recognition challenge,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Imagenet large scale visual recognition challenge,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.869683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9c3577d8-fe22-4ab8-a290-51d59436bcbc · outbound
This paper cites Masked-attention mask transformer for universal image segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Masked-attention mask transformer for universal image segmentation,
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.594913Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbdeb659-6f03-4004-b13c-2a8497727dc8 · outbound
This paper cites Per-pixel classi- fication is not all you need for semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Per-pixel classi- fication is not all you need for semantic segmentation,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.844583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5f309f33-de7f-4c28-aadb-8add8eb703a1 · outbound
This paper cites Group detr: Fast detr training with group-wise one-to-many assignment,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Group detr: Fast detr training with group-wise one-to-many assignment,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.829820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f1345d5d-ac5c-4535-a10e-5520b4578dcf · outbound
This paper cites Detrs with collaborative hybrid assignments training,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Detrs with collaborative hybrid assignments training,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.815228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c432808b-9ec8-49bd-9934-4284ad5ea05b · outbound
This paper cites Detrs with hybrid matching,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Detrs with hybrid matching,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.799859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation eded07c9-8f60-4282-9921-d9a2db812599 · outbound
This paper cites Ms-detr: Efficient detr training with mixed supervision,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Ms-detr: Efficient detr training with mixed supervision,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.783744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2ff03b25-9c8b-4fcd-ab0b-b257366ea2a9 · outbound
This paper cites Detection transformer with stable matching,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Detection transformer with stable matching,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.766791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation bde81fa0-16db-4a6a-91a7-42171bd1d085 · outbound
This paper cites Rank-detr for high quality object detection,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Rank-detr for high quality object detection,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.752114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 09f8dc25-66df-4cc2-8d7e-3d3f6c38b237 · outbound
This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.627257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40ae5cb2-1281-4b87-93dd-a3e3e29953a5 · outbound
This paper cites Emo2-detr: Efficient-matching oriented object detection with transformers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Emo2-detr: Efficient-matching oriented object detection with transformers,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.736649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fce1bd6b-e1b2-4a83-9514-afedbcc918c5 · outbound
This paper cites Hybrid proposal refiner: Revisiting detr series from the faster r-cnn perspective,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Hybrid proposal refiner: Revisiting detr series from the faster r-cnn perspective,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.719811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0a0893db-8e2e-4eda-8b11-d2b86db6af07 · outbound
This paper cites Salience detr: Enhancing detection transformer with hierarchical salience filtering refinement,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Salience detr: Enhancing detection transformer with hierarchical salience filtering refinement,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.704051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 04b723a1-e390-410c-bdf4-ddbf0f874ce0 · outbound
This paper cites Deep residual learning for image recognition,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Deep residual learning for image recognition,
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.644467Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68e7a3b6-f594-48a5-a09f-d611ac3218c0 · outbound
This paper cites Going deeper with convolutions,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Going deeper with convolutions,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.678869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5b093bbc-cdf5-4d9b-a91e-b49e4f6181c0 · outbound
This paper cites A convnet for the 2020s,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation A convnet for the 2020s,
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.652621Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c21fc46d-3cad-44e9-a2f6-8e307c9da61d · outbound
This paper cites Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.657432Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8135c43d-c0ba-47db-9bde-be3a68684a20 · outbound
This paper cites Adversarial co-training for semantic segmen- tation over medical images,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Adversarial co-training for semantic segmen- tation over medical images,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.653572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dee1a2ac-6f76-49e2-aa27-4ae7c6eba2c3 · outbound
This paper cites Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation,
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.667774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25bbc081-3c56-4608-9c27-35f3124fd4e0 · outbound
This paper cites Deformable convolutional networks,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Deformable convolutional networks,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.628584Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2815ca5a-15a9-4103-8559-e60d21c5023a · outbound
This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.613184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 90830fe5-444e-492d-8c35-35960de78dde · outbound
This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Segnext: Rethinking convolutional attention design for semantic segmentation,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.599650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7ceb9021-8c9c-4ef7-8032-b9f80244d04c · outbound
This paper cites Frozen is better than learning: A new design of prototype- based classifier for semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Frozen is better than learning: A new design of prototype- based classifier for semantic segmentation,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.585874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1fae5683-53f4-42f2-a1ec-282b96baaf68 · outbound
This paper cites Uncertainty-aware deep co-training for semi- supervised medical image segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Uncertainty-aware deep co-training for semi- supervised medical image segmentation,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.571937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1285a183-63a3-4a73-bda0-dbce9c4426d7 · outbound
This paper cites Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.558494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 467d4ae0-adf9-447b-9f87-0575eae88b9c · outbound
This paper cites A good student is cooperative and reliable: Cnn-transformer collaborative learning for semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation A good student is cooperative and reliable: Cnn-transformer collaborative learning for semantic segmentation,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.544352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b1bff59f-0b95-4ad6-819f-18b403aac779 · outbound
This paper cites Semantics distortion and style matter: Towards source- free uda for panoramic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Semantics distortion and style matter: Towards source- free uda for panoramic segmentation,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.530177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3811f309-0207-400c-811b-084416b6bea8 · outbound
This paper cites Attention is all you need,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Attention is all you need,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.516392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f01095fd-8be7-4ea8-8285-473cbe345e81 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.721118Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fbefb9b9-6294-401e-9abb-3c3080be1ec6 · outbound
This paper cites EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.727420Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c0434cf8-ba3b-402e-aabc-5f41d0e2de49 · outbound
This paper cites OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.732923Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60965e60-4676-4b21-b9e6-183321bf6050 · outbound
This paper cites Exact: Language-guided conceptual reasoning and uncertainty estimation for event-based action recognition and more,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Exact: Language-guided conceptual reasoning and uncertainty estimation for event-based action recognition and more,
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.501835Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b46f05cb-96bc-4f91-ab6f-0c17a1b693e0 · outbound
This paper cites Missing modal- ity robustness in semi-supervised multi-modal semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Missing modal- ity robustness in semi-supervised multi-modal semantic segmentation,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.487199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 33d0b394-ede4-45ca-9375-f9f366a24afe · outbound
This paper cites Towards good practices for missing modality robust action recognition,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Towards good practices for missing modality robust action recognition,
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.472508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f32ade72-c97a-44d4-b057-9d2ed05f7db6 · outbound
This paper cites Unified multi-modal image synthesis for missing modality imputation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Unified multi-modal image synthesis for missing modality imputation,
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.457939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation abf56ed5-9e0e-43c7-8f6a-cd83eba56d94 · outbound
This paper cites M3ae: multimodal representation learning for brain tumor segmentation with missing modalities,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation M3ae: multimodal representation learning for brain tumor segmentation with missing modalities,
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.443336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1b957305-4cdd-4da7-ae4c-c3255a7c6409 · outbound
This paper cites Semi-mamba: Mamba-driven semi-supervised multimodal remote sensing feature classification,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Semi-mamba: Mamba-driven semi-supervised multimodal remote sensing feature classification,
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.427608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c9d38745-974f-4587-b0fc-16b5690e280c · outbound
This paper cites Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.771080Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a947058e-ad07-4925-b7e2-3cae33de7f12 · outbound
This paper cites Chasing day and night: Towards robust and efficient all-day object detection guided by an event camera,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Chasing day and night: Towards robust and efficient all-day object detection guided by an event camera,
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.411602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4f717564-93c7-47ed-aa3c-8f81e612f825 · outbound
This paper cites Unibind: Llm- augmented unified and balanced representation space to bind them all,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Unibind: Llm- augmented unified and balanced representation space to bind them all,
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.396350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5a389bdd-68b1-4914-b3dc-3a288fb8a2ec · outbound
This paper cites Eventdance: Unsupervised source-free cross-modal adaptation for event-based object recognition,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Eventdance: Unsupervised source-free cross-modal adaptation for event-based object recognition,
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.381116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3ac21538-25ad-4b79-9435-6ef779c7d4cb · outbound
This paper cites Eventbind: Learning a unified representation to bind them all for event-based open-world understanding,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Eventbind: Learning a unified representation to bind them all for event-based open-world understanding,
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.366901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3722a80e-04f1-49be-b4ac-0f7b9bfeeb85 · outbound
This paper cites MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.798023Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79a60b95-a4a7-4e0a-938b-6a32b5a57d18 · outbound
This paper cites MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.803912Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e677c59-3523-4910-a01d-737c2c5e6edc · outbound
This paper cites Fcos: Fully convolutional one-stage object detection,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Fcos: Fully convolutional one-stage object detection,
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.351857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c4c44799-24d6-4376-9f9a-e3d76b2f2fb9 · outbound
This paper cites Focal loss for dense object detection,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Focal loss for dense object detection,
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.814311Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74076452-ed14-4cf9-8c99-0b525e30dec1 · outbound
This paper cites End-to-end object detection with transformers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation End-to-end object detection with transformers,
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.326502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2229a1c8-baea-4986-8170-68a654d8715b · outbound
This paper cites Mask dino: Towards a unified transformer- based framework for object detection and segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Mask dino: Towards a unified transformer- based framework for object detection and segmentation,
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.310990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e7fee3c7-777e-4e40-b0fd-69b11354e52a · outbound
This paper cites Primitive generation and semantic-related alignment for universal zero-shot segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Primitive generation and semantic-related alignment for universal zero-shot segmentation,
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.296122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7810af1e-3748-4971-8b65-b40194d410ef · outbound
This paper cites Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.833790Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4c40b69-dc76-4db3-a7cb-c7066ea7c017 · outbound
This paper cites Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation,
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.281044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f6e336a3-0e03-4250-81b7-1d30fbe18aa2 · outbound
This paper cites Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Shapeconv: Shape-aware convolutional layer for indoor rgb-d semantic segmentation,
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.266073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d4177e31-54f2-4720-9402-5490a648afd4 · outbound
This paper cites Aggregated residual transformations for deep neural networks,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Aggregated residual transformations for deep neural networks,
Reference 82
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.847251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c559ef2-90ee-4002-95f3-f983288c102b · outbound
This paper cites Efficient rgb-d semantic segmentation for indoor scene analysis,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Efficient rgb-d semantic segmentation for indoor scene analysis,
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.241496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 97a397eb-72f7-4464-ac1d-5528e8023c9c · outbound
This paper cites Multimodal token fusion for vision transform- ers,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Multimodal token fusion for vision transform- ers,
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.225280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 56ff219a-4c1b-4c22-88cc-16e5e2204c51 · outbound
This paper cites Omnivore: A single model for many visual modalities,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Omnivore: A single model for many visual modalities,
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.860997Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9be0eaf8-2e99-4e47-8a0f-16d7ef59df28 · outbound
This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,
Reference 86
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:04:00.865416Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8c3a830-b3c7-4975-98bf-27300e50b614 · outbound
This paper cites Prompting multi-modal image segmentation with semantic grouping,.
BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation Prompting multi-modal image segmentation with semantic grouping,
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:04:01.191808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.