Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation | Paper Citation Record

Observation 29242bd1-4b33-4dce-b126-b44e3331df4d · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Memory aware synapses: Learning what (not) to forget

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.118554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b71aebf-3b77-470a-b607-fff543b8191b · outbound

This paper cites Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:57:54.363270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6db85cec-b256-49ad-a51b-92713fc235f0 · outbound

This paper cites Modeling the background for incremental learning in semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Modeling the background for incremental learning in semantic segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.671171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85b4c9b7-c1a1-4092-afa9-c9211eaad711 · outbound

This paper cites Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.663328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81d9549c-64eb-479b-a5f0-807447ce552b · outbound

This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.131230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee4b80c9-bc0a-4f8a-bd3e-1bb601eb30bf · outbound

This paper cites Bi-directional cross-modality feature propagation with separation-and- aggregation gate for rgb-d semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Bi-directional cross-modality feature propagation with separation-and- aggregation gate for rgb-d semantic segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.654219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 80511ce5-4fd0-444c-af47-914c0ec43828 · outbound

This paper cites Indoor semantic segmentation using depth in- formation: 1st international conference on learning represen- tations, iclr 2013.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Indoor semantic segmentation using depth in- formation: 1st international conference on learning represen- tations, iclr 2013

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.645402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6f6ea13a-0c93-4db0-bea7-2a5e4d280699 · outbound

This paper cites RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:57:54.343999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 741b2850-cc85-46b0-8dc9-767529b7cd1d · outbound

This paper cites Replaying styles for continual semantic segmentation across domains.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Replaying styles for continual semantic segmentation across domains

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.636895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a120c8b6-3141-4414-805d-7a3fd1aaf19f · outbound

This paper cites Plop: Learning without forgetting for con- tinual semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Plop: Learning without forgetting for con- tinual semantic segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.628638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 45b1010e-92ce-4ce7-a7e6-a4731d21df0b · outbound

This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.148812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3136cdf6-0cc7-4e1f-a11c-34feed665337 · outbound

This paper cites Infraparis: A multi-modal and multi-task autonomous driving dataset.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Infraparis: A multi-modal and multi-task autonomous driving dataset

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.620282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4591c085-b63b-497d-ac26-399bd099f32a · outbound

This paper cites Born again neural net- works.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Born again neural net- works

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.611721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a1f0136b-477d-44d8-81e3-f0380c65334a · outbound

This paper cites Multi-domain incremental learning for semantic segmenta- tion.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Multi-domain incremental learning for semantic segmenta- tion

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.602656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1e0b42c5-f723-4fda-8ea4-dd8664f31e34 · outbound

This paper cites A bio-inspired in- cremental learning architecture for applied perceptual prob- lems.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation A bio-inspired in- cremental learning architecture for applied perceptual prob- lems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.594435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c4c95c76-111b-4c42-b50c-979bbb6a0eb9 · outbound

This paper cites Attribution-aware weight transfer: A warm- start initialization for class-incremental semantic segmenta- tion.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Attribution-aware weight transfer: A warm- start initialization for class-incremental semantic segmenta- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.586024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 897a69bd-ca7a-445e-b64b-e7d04d6b12e4 · outbound

This paper cites Memory efficient experience replay for streaming learning.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Memory efficient experience replay for streaming learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.577658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb790787-e391-47d8-a3bd-00870a2d74da · outbound

This paper cites Fusenet: Incorporating depth into semantic seg- mentation via fusion-based cnn architecture.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Fusenet: Incorporating depth into semantic seg- mentation via fusion-based cnn architecture

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.568679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89cc9d68-4a18-4976-a3ce-db80f0a45c16 · outbound

This paper cites Deep residual learning for image recognition.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Deep residual learning for image recognition

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.171583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e865a1d9-9211-444e-b4c5-c45f91ac472e · outbound

This paper cites Overcoming Catastrophic Interference by Conceptors.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Overcoming Catastrophic Interference by Conceptors

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:57:54.325064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2545c2d8-c4c8-4e27-97df-572f5e34d982 · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Learning a unified classifier incrementally via rebalancing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.178033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93708a4a-62f7-432e-bdbb-32311c2d07a0 · outbound

This paper cites Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.551002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7f8ffcf1-3771-45ae-9578-e6e4c3ef7ba6 · outbound

This paper cites Using conceptors to manage neural long- term memories for temporal patterns.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Using conceptors to manage neural long- term memories for temporal patterns

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.542662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ffb0c51-bfaa-443e-9488-3ff77790dbd1 · outbound

This paper cites Less-forgetting Learning in Deep Neural Networks.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Less-forgetting Learning in Deep Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.186807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6639a82a-80ca-4554-833f-921ba33fdbd8 · outbound

This paper cites Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.189844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7198032d-0d69-4d71-bac2-f6ef6f8db880 · outbound

This paper cites FearNet: Brain-Inspired Model for Incremental Learning.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation FearNet: Brain-Inspired Model for Incremental Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.192787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e9fafd-026c-444a-9c6b-215325916fe7 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Overcoming catastrophic forgetting in neu- ral networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.534233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2301be9e-c92e-427e-91e9-4c5750021ea7 · outbound

This paper cites Packnet: Adding mul- tiple tasks to a single network by iterative pruning.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Packnet: Adding mul- tiple tasks to a single network by iterative pruning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.526430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 23f65e75-bd9f-47a8-8ad5-14304b25e975 · outbound

This paper cites Catastrophic inter- ference in connectionist networks: The sequential learning problem.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Catastrophic inter- ference in connectionist networks: The sequential learning problem

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.518404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 77b24378-e804-47df-aa91-2eaae87c2f9a · outbound

This paper cites The stability-plasticity dilemma: Investigating the contin- uum from catastrophic forgetting to age-limited learning ef- fects.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation The stability-plasticity dilemma: Investigating the contin- uum from catastrophic forgetting to age-limited learning ef- fects

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.510320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a1f74675-b064-4233-8d86-727edfbdd7cf · outbound

This paper cites Knowledge dis- tillation for incremental learning in semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Knowledge dis- tillation for incremental learning in semantic segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.502381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60f676d6-45a8-4441-a958-9550bf9fd712 · outbound

This paper cites Motion and depth augmented semantic segmentation for autonomous navigation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Motion and depth augmented semantic segmentation for autonomous navigation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.493627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2761fdbe-47d1-45cd-acfd-0bb1b165ec66 · outbound

This paper cites Towards domain-aware knowledge distillation for continual model generalization.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Towards domain-aware knowledge distillation for continual model generalization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.485253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce233c7f-2edf-4156-a91a-a6ced3b66e0b · outbound

This paper cites Berg, and Li Fei-Fei.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Berg, and Li Fei-Fei

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.476879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ed53077a-9db3-47d6-ae6a-629d41bb9817 · outbound

This paper cites Progressive Neural Networks.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Progressive Neural Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.217795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da00d38d-7c19-4e7b-b434-5c423e4a4701 · outbound

This paper cites Continual learning with deep generative replay.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Continual learning with deep generative replay

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.468672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3cd7c354-f102-47ed-9759-98d94f2caef8 · outbound

This paper cites Rtfnet: Rgb- thermal fusion network for semantic segmentation of urban scenes.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Rtfnet: Rgb- thermal fusion network for semantic segmentation of urban scenes

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.459801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f3c35e84-4f1a-4b93-b6e6-4d3817783516 · outbound

This paper cites Fuseseg: Semantic segmentation of urban scenes based on rgb and thermal data fusion.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Fuseseg: Semantic segmentation of urban scenes based on rgb and thermal data fusion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.450993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0915335e-2e4b-442d-8e61-36843af55b44 · outbound

This paper cites HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:57:54.283055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f469dc0-8d69-4c97-bc45-1e8f7cc56a3e · outbound

This paper cites Learn- ing deep multimodal feature representation with asymmetric multi-layer fusion.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Learn- ing deep multimodal feature representation with asymmetric multi-layer fusion

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.442252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 789593e4-c431-479f-a475-bbc076a8af68 · outbound

This paper cites Grow- ing a brain: Fine-tuning by increasing model capacity.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Grow- ing a brain: Fine-tuning by increasing model capacity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.434043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e85c6eaa-bfb3-4129-b5e8-9f3577c6aebb · outbound

This paper cites Memory replay gans: Learn- ing to generate new categories without forgetting.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Memory replay gans: Learn- ing to generate new categories without forgetting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.425880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fa32ceec-d389-4293-bf84-a1367c4740b0 · outbound

This paper cites Ccaffmnet: Dual-spectral semantic segmentation network with channel- coordinate attention feature fusion module.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Ccaffmnet: Dual-spectral semantic segmentation network with channel- coordinate attention feature fusion module

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.417093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ba1ac31-45db-4206-a810-2eabcb51478e · outbound

This paper cites Lifelong learning with dynamically expandable net- works.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Lifelong learning with dynamically expandable net- works

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.408652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 561ecbdd-cd92-46dd-9d41-b524a4078e7a · outbound

This paper cites Contin- ual learning through synaptic intelligence.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Contin- ual learning through synaptic intelligence

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.245585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bef64a9-3059-47a1-840e-2735502624fe · outbound

This paper cites Representation compensation networks for continual semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Representation compensation networks for continual semantic segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.395518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c7020f9c-4070-434d-8cc9-0bae99541c7e · outbound

This paper cites Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:54.251091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57ede23a-7fef-4829-81a3-95022c01fec7 · outbound

This paper cites Abmdrnet: Adaptive-weighted bi-directional modality difference reduc- tion network for rgb-t semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Abmdrnet: Adaptive-weighted bi-directional modality difference reduc- tion network for rgb-t semantic segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:54.381756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f71782c4-4c24-4c23-9c3e-c79b70002baa · outbound

This paper cites Gmnet: Graded-feature multilabel-learning network for rgb-thermal urban scene semantic segmentation.

Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation Gmnet: Graded-feature multilabel-learning network for rgb-thermal urban scene semantic segmentation

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T11:57:54.372064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers