Pikaliov/fuze_task
1
0
Fork 1
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity

fuse_proj: Initial operational package for 3 researchers (Pavlenko/Blizno/Moroz)

Browse Source 
Multimodal fusion research on StripNet+GTA-UAV proxy:
- 3 independent fusion tracks: condition-aware (A), token/bottleneck (B), role-aware (C)
- Shared interfaces, protocol, dataset audit, baseline benchmarks
- Canonical version-chain references to vault (SPEC, ANALYSIS, TRIAGE)
- Personalized task plans and decision tables for each researcher
- 3 generated DOCX task assignment files with milestones and DoD checklist
- Full modality dropout diagnostics and missing-modality robustness requirements
- Data contract, benchmark registry, experiment tracking infrastructure

Operational documents:
- docs/00_project/: MERIDIAN context, protocol, repository reuse guide, experiment specification
- docs/01_tasks/: Master assignment + 3 individual researcher tracks + joint integration
- docs/02_references/: Core literature, version-chain bases, code maps
- docs/03_codebase_guides/: Existing code snapshots from vault
- scripts/: gen_task_plans.js (DOCX generation), placeholder infrastructure
- vendor_reference/: Snapshots of caption_test, depth_edges_annotate, existing SOFIA/SegModel code
- reports/, results/, experiments/: Shared output structure for all 3 researchers

3 DOCX files generated from gen_task_plans.js (Times New Roman 14pt, GOST format):
- План_заданий_Павленко_БВ.docx (Condition-Aware track, fusion API owner)
- План_заданий_Близно_МВ.docx (Token/Bottleneck track, benchmark owner)
- План_заданий_Мороз_ЕС.docx (Role-Aware track, data contract owner)

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
Pikaliov
2026-06-11 17:16:57 +03:00
commit 2c6a00a4ca
155 changed files with 39765 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

83
docs/02_references/MANIFEST.csv Normal file
View File

@@ -0,0 +1,83 @@
"path","size_bytes","sha256"
"docs\02_references\00_READING_MAP.md","13130","1b11a9a899750e232413d95a500ac5e103e0f04c0aa42a10f2a6db3c015549b9"
"docs\02_references\01_required\CHMv2 vs Depth Anything V3 для CVGL.md","4341","2ffd52b6ca75fbbb5465f5f672efa55d537b1fa5d94b2fc05be5832f85e45a8d"
"docs\02_references\01_required\PROTOCOL_text_encoder_benchmark_StripNet_GTA_UAV.md","48077","e4763c53e5b29085511383420ea8abcb2d8c5005be67db8522a84f8bfc844f8d"
"docs\02_references\01_required\Методология_автоматического_аннотирования_World_UAV_v5.md","39687","ae4bed052d0ebf4aa167b0ab47021c5909c69ca8bb7f1b4f990590bb8116d515"
"docs\02_references\01_required\обзор_моделей_depth_normals_segmentation.md","29895","21acb09c9755967d7312a631042137fad0b01798119509a33c908afc5958ad03"
"docs\02_references\01_required\профилирование_VRAM_pipeline_256.md","8537","293a836426883e1b575f60d32415420c55cf39dca8baf7e7799b6ddd9b7c67c4"
"docs\02_references\02_fusion_core\01_film_fusion.md","30747","4317ba6f89e5e79777176f7c97ac316da8392159800341fe7aac2072cfca27ff"
"docs\02_references\02_fusion_core\ANALYSIS_FiLM_alternatives_student_only_v4.md","4510","08fdc4b4afafec5be77f4eba6f99f6dda8f531759d3308f0a183b228e7fb9013"
"docs\02_references\02_fusion_core\ANALYSIS_FiLM_alternatives_taxonomy_v4.md","6833","7182e2d521fdb6b918d8d7ffd16bf983d04ddc7f9ff89b21817915f44a6f992b"
"docs\02_references\02_fusion_core\ANALYSIS_fusion_v4.md","2656","6ad87497fec6b6dbd42ec55010fc21ded1ba4d225a1dd03553425a0c647fb1b7"
"docs\02_references\02_fusion_core\BLUEPRINT_unified_teacher_partD3_v2.md","4313","3433a343a8c8364124f0fa665471836fe1deab6bd01911f7c9013db043b47825"
"docs\02_references\02_fusion_core\DELTA_E1_fusion_taxonomy_audit_v4.md","6220","9bf3354f188974f661db8b156ca727cce1ee379dc57b384993df9c38bdc604ce"
"docs\02_references\02_fusion_core\DELTA_E1_teacher_5modal_Arch_A_final_v4.md","4211","a068712dbdb60699c63c8dfc2d98338ca52473933d928f7e98ede2b85fdbb5d3"
"docs\02_references\02_fusion_core\HYP_fusion_variants_v4.md","5497","21cdb9710fc57dcde7701662136b05c323e34f41d02dd07321a4bcfc34f1f582"
"docs\02_references\02_fusion_core\SPEC_fusion_ACF_MERIDIAN_v5.md","7780","9610677cb79f4c519e8784ab5087efdb90b6d468065ef13748a238aec0377c52"
"docs\02_references\02_fusion_core\TRIAGE_teacher_fusion_dropout_2026-06-11.md","25590","4ce887c315c03ba20a4008a70965b849940826f78723be26e72eb01680c05102"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only.md","30956","0a9ee6991691fc91ed281827b1bbda38f90321aefcadf984adc09251c30a02e3"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only_v2.md","5052","16ea17b9f6a86801475a33ddeed1110350a928887b45889d8d3fcddac0ffb11c"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only_v3.md","4125","9bb097ec1f0522b8c0fe81a238b5c198540b961e108079ca1b027d18b3ac7542"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy.md","44044","a939f7fafc612c08bc7a2ecef374237df55d02027d70e6a249f4c11b0ddd3b93"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy_v2.md","6008","7d59fe223ad7466c3e025530ce44902c176a00828381a5e6d30b60652dbed022"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy_v3.md","12524","9258cf1cd71b9e903ccd7abe1169ca5e84b798306640ca8907e933b21a5c0e83"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion.md","73794","b87502ddbaec7f6a5f756c20d95bb8a1448a6a5f27caa9f2fdc35efe513d1aff"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion_v2.md","10047","3eaea5dbcda9ef7aab79daed15a415589f2a120851de0f25bace29b35dd17d72"
"docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion_v3.md","3724","1d74869453c6984f7511d9711a5a3bc49ea941acee45e9b0f80d22da50568e48"
"docs\02_references\02_fusion_core\_version_chain\BLUEPRINT_unified_teacher_partD3.md","49938","1dedcfbe072023a8f98563500a1a7b9e692ce98f0cc6c9062fa85e453509d91f"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit.md","22946","da70d6cc7bcc80cad6aa7ab597fa2762f7a60a9f2d00b1c0a85f49445d67f96d"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit_v2.md","5063","8c96d0863eea48c76552997aaead8cffa27df742d92692623e66eba801d37b00"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit_v3.md","13728","37062be3b0de1ec86bc451a5e4e48635965d704ffc4559d31155d559ff67903c"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final.md","43457","3bcb9073a1312f1a56f6b2665626a4f9600d9682bf726de34b6d64f9a104ee32"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final_v2.md","6595","40d0564f45711f40a0a076d65d3d11b8adbb8221d7fd93cd627b77c344c0328a"
"docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final_v3.md","2465","39edb491cb2586168bcfc9964030850636304b4f798dea802c8a4cca1a576aa7"
"docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants.md","42416","0260c520335410e5b33b73463c2826db3bf4a1268d9b5624be71fa8b529b5ff5"
"docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants_v2.md","7279","9825c530cc1406a08b556f31346b86c55ec181f74bf886fcdecf853a18d35852"
"docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants_v3.md","10398","ca8dc907b315aeda29cfe264e48ca7d5da84f3b59e18ae1b0a78bdd546515d48"
"docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v1.md","29401","29ee3614054592308ae5cb152074b5f364aa8ea7cb79d280ed3955d63c7d104b"
"docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v2.md","9603","2e6d79d485ba0dd070aacbb94f7a9f79600abdb7a284a3c0bfa881eaedba93ad"
"docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v3.md","10514","b1e20268b62ffcd26eee69c7daf88e863a1f2972d2949bc528b3cd3fbdc2070a"
"docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v4.md","8271","3f56d3b720765a2da1cba39e630bd58013cb4af467c1ccf62fdfa3694ddba30a"
"docs\02_references\02_fusion_core\ОБЗОР_cvgl_detailed_v1.md","38294","0c29417b746e8a170e459181b7ff56f1fcdcb515a953290be23fffdcad2a5e75"
"docs\02_references\02_fusion_core\ОБЗОР_fusion_detailed_v1.md","35764","41aee9c285a98255233e5f500a65e85a59e6afdcf8032adee84110abf18a8494"
"docs\02_references\02_fusion_core\ОБЗОР_fusion_rgb_edges_v1.md","25668","8060c25f611087e6e7f166d03a413b64d1b2342ca2b79716a7b1de624afa4518"
"docs\02_references\02_fusion_core\ОБЗОР_fusion_rgb_multimodal_full_v1.md","34816","ee8fb56323e827bbbe8dd041c5eb70d399760df44ccee80e332121856625acf1"
"docs\02_references\02_fusion_core\СИНТЕЗ_3_fusion.md","21391","48dd39a9283655eae8e0f00e7713c94668ef0fd67c757f85a91c04b09ff9231e"
"docs\02_references\02_fusion_core\СИНТЕЗ_3_fusion_taxonomy_audit.md","14296","92dc3d7b5dabb86afac541428e6ed6fe36372cb160de30a717bfe4b7ed4898e8"
"docs\02_references\03_segmentation\DELTA_pair_A_seg_revised_v3.md","2166","19ceeac4ff3a9d81c8b50fa3b62d6160dc3a340aba470798bb2e8103ec455cc8"
"docs\02_references\03_segmentation\F36_FC-CLIP.md","13087","bcb15b98f86feefb37be740ea2b91e4c01ed0782132e160586018bb9f04786ef"
"docs\02_references\03_segmentation\P52_CVGL_2024_A guided approach for cross-view geolocalization estimation with land cover semantic segmentation.md","28822","6d900588e4cd2791065aa717c09414bc9be1f69eac1a4d4d2dc263a42c396776"
"docs\02_references\03_segmentation\REVIEW_segmentation_pairA.md","48438","164e7650f767a4eaa332a1cfb9ece8d9d256eed0d3a496a119d7577ef3670055"
"docs\02_references\03_segmentation\_version_chain\DELTA_pair_A_seg_revised.md","48087","55193489ada06e143f2f0c6adbda9e403c1e0509daf9af1f6ce5f4f0df3087fe"
"docs\02_references\03_segmentation\_version_chain\DELTA_pair_A_seg_revised_v2.md","3656","55dfed92175cc51c9f0bf86d6605616fa4308421addc93576ec2ca9732504c8c"
"docs\02_references\03_segmentation\ОБЗОР_fusion_rgb_segmentation_v1.md","28537","1984731f8ecf977106f6bac0de5421e538da3835567e30e09f9ec2a7988b6e33"
"docs\02_references\04_geometry\DELTA_pair_B_depth_uav_v3.md","1287","1cf5f2911fa236f42dc1b234076b8957fd4a9564f2c8e5f6e5990e0725943424"
"docs\02_references\04_geometry\DELTA_pair_C_chm_sat_v3.md","897","d461e25a217fc7a120189b366757a1576570669dbd23d5ee6ad1eb01c6d74243"
"docs\02_references\04_geometry\M11_CHMv2_deep_dive_for_MERIDIAN.md","24793","7d7183e69f80f640145ad94b96dd78400c87884fdd101755ce21183eab0abe57"
"docs\02_references\04_geometry\M4_2025_JRN-Geo A Joint Perception Network based on RGB and Normal images for Cross-view Geo-localization.md","63596","5467cd31dec3ee17e0959c9f0d4fdebecdc94b7cb137ccf2ff1e344bbc99628f"
"docs\02_references\04_geometry\P44_MGS2_deep_dive_for_MERIDIAN.md","32928","e915dc4649fa310127bcdfd40b747eab5b434ecc91189b3e5163aab207712903"
"docs\02_references\04_geometry\REVIEW_chm_pairC.md","37175","dc2aa9147a8f1b7003151d9d000fa49ba0ebd25d3f5d2c7f75cf79283f77cfe4"
"docs\02_references\04_geometry\REVIEW_depth_normals_pairB.md","40515","96c8d91fa215daca6b3e350a9d9ce8ad24ea664456d8aaede599771d71ffbac2"
"docs\02_references\04_geometry\_version_chain\DELTA_pair_B_depth_uav.md","34079","5dcd59100443b6a917411d9d2ad3ed045ee1b5669e5ef560e9d64be2e58d0ccc"
"docs\02_references\04_geometry\_version_chain\DELTA_pair_B_depth_uav_v2.md","2349","8c05b6f921decfc0f27698debe6351848445f7712937bb934cae732ea8b18eb8"
"docs\02_references\04_geometry\_version_chain\DELTA_pair_C_chm_sat.md","29880","5a0944f7319226a6d06009235a24217a2216247dca3792101fe3bdec6754233a"
"docs\02_references\04_geometry\_version_chain\DELTA_pair_C_chm_sat_v2.md","2144","a6217ea116d071756bfbc8b4f3ed84ddc8c2ed0e71d954b5be1ae3eb93b1244c"
"docs\02_references\04_geometry\ОБЗОР_fusion_rgb_chm_v1.md","25620","0ecd50cc167be1f995807c7e5ebb5692b00de7fc9c416e6ff23411fef9539501"
"docs\02_references\05_text\C5_2025_WeatherPrompt.md","68530","2e2642ce5ad74762093775304d8e44dd590e851549405326680108e9e1338a9c"
"docs\02_references\05_text\F14_2025_WeatherPrompt_deep_dive_for_MERIDIAN.md","9287","348e09f0a83165a193ac50feefdf8cbf6891d5f35ece992cdda8953055d8af72"
"docs\02_references\05_text\F47_2026_TacFiLM_Tactile_Modality_Fusion_VLA.md","12125","34c54c9ac53d4fe1b59c9d8db438b11909b6a1fdaf1d68f15e81e29c52c221e0"
"docs\02_references\05_text\P64_2025_CGSI_deep_dive_for_MERIDIAN.md","9248","bcfda7d0941d0ce08cf8d3b423e5254b303d98e93b709288ad747311c0236e50"
"docs\02_references\05_text\REVIEW_text_pairD_final.md","65291","aa50f6e91083254f7e66ae069b2de5d70ee5435c721ab89178f321b53935a710"
"docs\02_references\05_text\REVIEW_text_pairD_methodology.md","55998","a9a614b49af30937d5d1dff2cde172cf9f18e8b1a7f92f0bea785b32601b8b23"
"docs\02_references\06_paper_analyses\B14_BB_2025_Strip R-CNN Large Strip Convolution for Remote Sensing Object Detection.md","32747","325b06f23759e1d350a7993bea0caf98cb2af6a070eed0debdd597e1d1602eb3"
"docs\02_references\06_paper_analyses\F37_2024_AsymFormer_Asymmetrical_Cross-Modal_Mobile_RGB-D.md","15122","8f317cb0a0444e2f41fe703a1184ddc869bf688f29475cdf7079129d420e3b68"
"docs\02_references\06_paper_analyses\F39_2025_CAFuser_Condition-Aware_Multimodal_Fusion.md","18951","4d57de3d2c7e11733f45b960349cb3724b2ddf5436d2b94572f1c814e77ff3e8"
"docs\02_references\06_paper_analyses\F40_2025_M3amba_CLIP-driven_Mamba_Multi-modal_RS.md","22925","a5245b92f711d2e265e38c76431433db059e29adf8618389b3a7ab6ba0a96c7b"
"docs\02_references\06_paper_analyses\F43_2024_Robust_Multimodal_Missing_Modalities_PEFT.md","15245","2c03440afad961f8b34100d979b5cd71a92d9ce504764b14b0d382ecd9aa8a17"
"docs\02_references\06_paper_analyses\F44_2025_Fusion-Mamba_Cross-modality_Object_Detection.md","21071","ceeaca8a6f61f8f09320dfe71d389555dcda328d24445e20ec474a7e4541727d"
"docs\02_references\06_paper_analyses\F45_2024_Flex-MoE_Modeling_Arbitrary_Modality_Combination.md","16044","24411dd7890d3194c90f53893abe329cf0375a76a4bbac5593bcc4a37576b517"
"docs\02_references\06_paper_analyses\F46_2024_FuseMoE_Mixture-of-Experts_Transformers_Fleximodal.md","13877","ffdf6c72ee4e0fc115c5288931fe3683795b38fa754432c0c0f179a926113acf"
"docs\02_references\06_paper_analyses\F4_2025_EARTHMIND LEVERAGING CROSS-SENSOR DATA FOR ADVANCED EARTH OBSERVATION INTERPRETATION WITH A UNIFIED MULTIMODAL LLM.md","53238","03d1f357fccc48cb65680206c49a075201b8d7e233998027bfd0dcfa4a88f72b"
"docs\02_references\06_paper_analyses\F68_2024_RemoteDet-Mamba_Hybrid_RS.md","44010","819b22445e97cd2e24d1cbe9afa282e1f6c32e7fe25bdfaa966bbd646f044b12"
"docs\02_references\06_paper_analyses\ImageBind One Embedding Space To Bind Them All.md","86769","655cf7af3c25781bf8e2368108d494d0122333e603255c1e5b37a6f58ba82657"
"docs\02_references\06_paper_analyses\P50_2026_MMGeo_deep_dive_for_MERIDIAN.md","8699","d08809943d528500fc3c4769530934c5f2c042408359b4e911dc981d7be3849a"
1 path size_bytes sha256
2 docs\02_references\00_READING_MAP.md 13130 1b11a9a899750e232413d95a500ac5e103e0f04c0aa42a10f2a6db3c015549b9
3 docs\02_references\01_required\CHMv2 vs Depth Anything V3 для CVGL.md 4341 2ffd52b6ca75fbbb5465f5f672efa55d537b1fa5d94b2fc05be5832f85e45a8d
4 docs\02_references\01_required\PROTOCOL_text_encoder_benchmark_StripNet_GTA_UAV.md 48077 e4763c53e5b29085511383420ea8abcb2d8c5005be67db8522a84f8bfc844f8d
5 docs\02_references\01_required\Методология_автоматического_аннотирования_World_UAV_v5.md 39687 ae4bed052d0ebf4aa167b0ab47021c5909c69ca8bb7f1b4f990590bb8116d515
6 docs\02_references\01_required\обзор_моделей_depth_normals_segmentation.md 29895 21acb09c9755967d7312a631042137fad0b01798119509a33c908afc5958ad03
7 docs\02_references\01_required\профилирование_VRAM_pipeline_256.md 8537 293a836426883e1b575f60d32415420c55cf39dca8baf7e7799b6ddd9b7c67c4
8 docs\02_references\02_fusion_core\01_film_fusion.md 30747 4317ba6f89e5e79777176f7c97ac316da8392159800341fe7aac2072cfca27ff
9 docs\02_references\02_fusion_core\ANALYSIS_FiLM_alternatives_student_only_v4.md 4510 08fdc4b4afafec5be77f4eba6f99f6dda8f531759d3308f0a183b228e7fb9013
10 docs\02_references\02_fusion_core\ANALYSIS_FiLM_alternatives_taxonomy_v4.md 6833 7182e2d521fdb6b918d8d7ffd16bf983d04ddc7f9ff89b21817915f44a6f992b
11 docs\02_references\02_fusion_core\ANALYSIS_fusion_v4.md 2656 6ad87497fec6b6dbd42ec55010fc21ded1ba4d225a1dd03553425a0c647fb1b7
12 docs\02_references\02_fusion_core\BLUEPRINT_unified_teacher_partD3_v2.md 4313 3433a343a8c8364124f0fa665471836fe1deab6bd01911f7c9013db043b47825
13 docs\02_references\02_fusion_core\DELTA_E1_fusion_taxonomy_audit_v4.md 6220 9bf3354f188974f661db8b156ca727cce1ee379dc57b384993df9c38bdc604ce
14 docs\02_references\02_fusion_core\DELTA_E1_teacher_5modal_Arch_A_final_v4.md 4211 a068712dbdb60699c63c8dfc2d98338ca52473933d928f7e98ede2b85fdbb5d3
15 docs\02_references\02_fusion_core\HYP_fusion_variants_v4.md 5497 21cdb9710fc57dcde7701662136b05c323e34f41d02dd07321a4bcfc34f1f582
16 docs\02_references\02_fusion_core\SPEC_fusion_ACF_MERIDIAN_v5.md 7780 9610677cb79f4c519e8784ab5087efdb90b6d468065ef13748a238aec0377c52
17 docs\02_references\02_fusion_core\TRIAGE_teacher_fusion_dropout_2026-06-11.md 25590 4ce887c315c03ba20a4008a70965b849940826f78723be26e72eb01680c05102
18 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only.md 30956 0a9ee6991691fc91ed281827b1bbda38f90321aefcadf984adc09251c30a02e3
19 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only_v2.md 5052 16ea17b9f6a86801475a33ddeed1110350a928887b45889d8d3fcddac0ffb11c
20 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_student_only_v3.md 4125 9bb097ec1f0522b8c0fe81a238b5c198540b961e108079ca1b027d18b3ac7542
21 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy.md 44044 a939f7fafc612c08bc7a2ecef374237df55d02027d70e6a249f4c11b0ddd3b93
22 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy_v2.md 6008 7d59fe223ad7466c3e025530ce44902c176a00828381a5e6d30b60652dbed022
23 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_FiLM_alternatives_taxonomy_v3.md 12524 9258cf1cd71b9e903ccd7abe1169ca5e84b798306640ca8907e933b21a5c0e83
24 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion.md 73794 b87502ddbaec7f6a5f756c20d95bb8a1448a6a5f27caa9f2fdc35efe513d1aff
25 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion_v2.md 10047 3eaea5dbcda9ef7aab79daed15a415589f2a120851de0f25bace29b35dd17d72
26 docs\02_references\02_fusion_core\_version_chain\ANALYSIS_fusion_v3.md 3724 1d74869453c6984f7511d9711a5a3bc49ea941acee45e9b0f80d22da50568e48
27 docs\02_references\02_fusion_core\_version_chain\BLUEPRINT_unified_teacher_partD3.md 49938 1dedcfbe072023a8f98563500a1a7b9e692ce98f0cc6c9062fa85e453509d91f
28 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit.md 22946 da70d6cc7bcc80cad6aa7ab597fa2762f7a60a9f2d00b1c0a85f49445d67f96d
29 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit_v2.md 5063 8c96d0863eea48c76552997aaead8cffa27df742d92692623e66eba801d37b00
30 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_fusion_taxonomy_audit_v3.md 13728 37062be3b0de1ec86bc451a5e4e48635965d704ffc4559d31155d559ff67903c
31 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final.md 43457 3bcb9073a1312f1a56f6b2665626a4f9600d9682bf726de34b6d64f9a104ee32
32 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final_v2.md 6595 40d0564f45711f40a0a076d65d3d11b8adbb8221d7fd93cd627b77c344c0328a
33 docs\02_references\02_fusion_core\_version_chain\DELTA_E1_teacher_5modal_Arch_A_final_v3.md 2465 39edb491cb2586168bcfc9964030850636304b4f798dea802c8a4cca1a576aa7
34 docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants.md 42416 0260c520335410e5b33b73463c2826db3bf4a1268d9b5624be71fa8b529b5ff5
35 docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants_v2.md 7279 9825c530cc1406a08b556f31346b86c55ec181f74bf886fcdecf853a18d35852
36 docs\02_references\02_fusion_core\_version_chain\HYP_fusion_variants_v3.md 10398 ca8dc907b315aeda29cfe264e48ca7d5da84f3b59e18ae1b0a78bdd546515d48
37 docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v1.md 29401 29ee3614054592308ae5cb152074b5f364aa8ea7cb79d280ed3955d63c7d104b
38 docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v2.md 9603 2e6d79d485ba0dd070aacbb94f7a9f79600abdb7a284a3c0bfa881eaedba93ad
39 docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v3.md 10514 b1e20268b62ffcd26eee69c7daf88e863a1f2972d2949bc528b3cd3fbdc2070a
40 docs\02_references\02_fusion_core\_version_chain\SPEC_fusion_ACF_MERIDIAN_v4.md 8271 3f56d3b720765a2da1cba39e630bd58013cb4af467c1ccf62fdfa3694ddba30a
41 docs\02_references\02_fusion_core\ОБЗОР_cvgl_detailed_v1.md 38294 0c29417b746e8a170e459181b7ff56f1fcdcb515a953290be23fffdcad2a5e75
42 docs\02_references\02_fusion_core\ОБЗОР_fusion_detailed_v1.md 35764 41aee9c285a98255233e5f500a65e85a59e6afdcf8032adee84110abf18a8494
43 docs\02_references\02_fusion_core\ОБЗОР_fusion_rgb_edges_v1.md 25668 8060c25f611087e6e7f166d03a413b64d1b2342ca2b79716a7b1de624afa4518
44 docs\02_references\02_fusion_core\ОБЗОР_fusion_rgb_multimodal_full_v1.md 34816 ee8fb56323e827bbbe8dd041c5eb70d399760df44ccee80e332121856625acf1
45 docs\02_references\02_fusion_core\СИНТЕЗ_3_fusion.md 21391 48dd39a9283655eae8e0f00e7713c94668ef0fd67c757f85a91c04b09ff9231e
46 docs\02_references\02_fusion_core\СИНТЕЗ_3_fusion_taxonomy_audit.md 14296 92dc3d7b5dabb86afac541428e6ed6fe36372cb160de30a717bfe4b7ed4898e8
47 docs\02_references\03_segmentation\DELTA_pair_A_seg_revised_v3.md 2166 19ceeac4ff3a9d81c8b50fa3b62d6160dc3a340aba470798bb2e8103ec455cc8
48 docs\02_references\03_segmentation\F36_FC-CLIP.md 13087 bcb15b98f86feefb37be740ea2b91e4c01ed0782132e160586018bb9f04786ef
49 docs\02_references\03_segmentation\P52_CVGL_2024_A guided approach for cross-view geolocalization estimation with land cover semantic segmentation.md 28822 6d900588e4cd2791065aa717c09414bc9be1f69eac1a4d4d2dc263a42c396776
50 docs\02_references\03_segmentation\REVIEW_segmentation_pairA.md 48438 164e7650f767a4eaa332a1cfb9ece8d9d256eed0d3a496a119d7577ef3670055
51 docs\02_references\03_segmentation\_version_chain\DELTA_pair_A_seg_revised.md 48087 55193489ada06e143f2f0c6adbda9e403c1e0509daf9af1f6ce5f4f0df3087fe
52 docs\02_references\03_segmentation\_version_chain\DELTA_pair_A_seg_revised_v2.md 3656 55dfed92175cc51c9f0bf86d6605616fa4308421addc93576ec2ca9732504c8c
53 docs\02_references\03_segmentation\ОБЗОР_fusion_rgb_segmentation_v1.md 28537 1984731f8ecf977106f6bac0de5421e538da3835567e30e09f9ec2a7988b6e33
54 docs\02_references\04_geometry\DELTA_pair_B_depth_uav_v3.md 1287 1cf5f2911fa236f42dc1b234076b8957fd4a9564f2c8e5f6e5990e0725943424
55 docs\02_references\04_geometry\DELTA_pair_C_chm_sat_v3.md 897 d461e25a217fc7a120189b366757a1576570669dbd23d5ee6ad1eb01c6d74243
56 docs\02_references\04_geometry\M11_CHMv2_deep_dive_for_MERIDIAN.md 24793 7d7183e69f80f640145ad94b96dd78400c87884fdd101755ce21183eab0abe57
57 docs\02_references\04_geometry\M4_2025_JRN-Geo A Joint Perception Network based on RGB and Normal images for Cross-view Geo-localization.md 63596 5467cd31dec3ee17e0959c9f0d4fdebecdc94b7cb137ccf2ff1e344bbc99628f
58 docs\02_references\04_geometry\P44_MGS2_deep_dive_for_MERIDIAN.md 32928 e915dc4649fa310127bcdfd40b747eab5b434ecc91189b3e5163aab207712903
59 docs\02_references\04_geometry\REVIEW_chm_pairC.md 37175 dc2aa9147a8f1b7003151d9d000fa49ba0ebd25d3f5d2c7f75cf79283f77cfe4
60 docs\02_references\04_geometry\REVIEW_depth_normals_pairB.md 40515 96c8d91fa215daca6b3e350a9d9ce8ad24ea664456d8aaede599771d71ffbac2
61 docs\02_references\04_geometry\_version_chain\DELTA_pair_B_depth_uav.md 34079 5dcd59100443b6a917411d9d2ad3ed045ee1b5669e5ef560e9d64be2e58d0ccc
62 docs\02_references\04_geometry\_version_chain\DELTA_pair_B_depth_uav_v2.md 2349 8c05b6f921decfc0f27698debe6351848445f7712937bb934cae732ea8b18eb8
63 docs\02_references\04_geometry\_version_chain\DELTA_pair_C_chm_sat.md 29880 5a0944f7319226a6d06009235a24217a2216247dca3792101fe3bdec6754233a
64 docs\02_references\04_geometry\_version_chain\DELTA_pair_C_chm_sat_v2.md 2144 a6217ea116d071756bfbc8b4f3ed84ddc8c2ed0e71d954b5be1ae3eb93b1244c
65 docs\02_references\04_geometry\ОБЗОР_fusion_rgb_chm_v1.md 25620 0ecd50cc167be1f995807c7e5ebb5692b00de7fc9c416e6ff23411fef9539501
66 docs\02_references\05_text\C5_2025_WeatherPrompt.md 68530 2e2642ce5ad74762093775304d8e44dd590e851549405326680108e9e1338a9c
67 docs\02_references\05_text\F14_2025_WeatherPrompt_deep_dive_for_MERIDIAN.md 9287 348e09f0a83165a193ac50feefdf8cbf6891d5f35ece992cdda8953055d8af72
68 docs\02_references\05_text\F47_2026_TacFiLM_Tactile_Modality_Fusion_VLA.md 12125 34c54c9ac53d4fe1b59c9d8db438b11909b6a1fdaf1d68f15e81e29c52c221e0
69 docs\02_references\05_text\P64_2025_CGSI_deep_dive_for_MERIDIAN.md 9248 bcfda7d0941d0ce08cf8d3b423e5254b303d98e93b709288ad747311c0236e50
70 docs\02_references\05_text\REVIEW_text_pairD_final.md 65291 aa50f6e91083254f7e66ae069b2de5d70ee5435c721ab89178f321b53935a710
71 docs\02_references\05_text\REVIEW_text_pairD_methodology.md 55998 a9a614b49af30937d5d1dff2cde172cf9f18e8b1a7f92f0bea785b32601b8b23
72 docs\02_references\06_paper_analyses\B14_BB_2025_Strip R-CNN Large Strip Convolution for Remote Sensing Object Detection.md 32747 325b06f23759e1d350a7993bea0caf98cb2af6a070eed0debdd597e1d1602eb3
73 docs\02_references\06_paper_analyses\F37_2024_AsymFormer_Asymmetrical_Cross-Modal_Mobile_RGB-D.md 15122 8f317cb0a0444e2f41fe703a1184ddc869bf688f29475cdf7079129d420e3b68
74 docs\02_references\06_paper_analyses\F39_2025_CAFuser_Condition-Aware_Multimodal_Fusion.md 18951 4d57de3d2c7e11733f45b960349cb3724b2ddf5436d2b94572f1c814e77ff3e8
75 docs\02_references\06_paper_analyses\F40_2025_M3amba_CLIP-driven_Mamba_Multi-modal_RS.md 22925 a5245b92f711d2e265e38c76431433db059e29adf8618389b3a7ab6ba0a96c7b
76 docs\02_references\06_paper_analyses\F43_2024_Robust_Multimodal_Missing_Modalities_PEFT.md 15245 2c03440afad961f8b34100d979b5cd71a92d9ce504764b14b0d382ecd9aa8a17
77 docs\02_references\06_paper_analyses\F44_2025_Fusion-Mamba_Cross-modality_Object_Detection.md 21071 ceeaca8a6f61f8f09320dfe71d389555dcda328d24445e20ec474a7e4541727d
78 docs\02_references\06_paper_analyses\F45_2024_Flex-MoE_Modeling_Arbitrary_Modality_Combination.md 16044 24411dd7890d3194c90f53893abe329cf0375a76a4bbac5593bcc4a37576b517
79 docs\02_references\06_paper_analyses\F46_2024_FuseMoE_Mixture-of-Experts_Transformers_Fleximodal.md 13877 ffdf6c72ee4e0fc115c5288931fe3683795b38fa754432c0c0f179a926113acf
80 docs\02_references\06_paper_analyses\F4_2025_EARTHMIND LEVERAGING CROSS-SENSOR DATA FOR ADVANCED EARTH OBSERVATION INTERPRETATION WITH A UNIFIED MULTIMODAL LLM.md 53238 03d1f357fccc48cb65680206c49a075201b8d7e233998027bfd0dcfa4a88f72b
81 docs\02_references\06_paper_analyses\F68_2024_RemoteDet-Mamba_Hybrid_RS.md 44010 819b22445e97cd2e24d1cbe9afa282e1f6c32e7fe25bdfaa966bbd646f044b12
82 docs\02_references\06_paper_analyses\ImageBind One Embedding Space To Bind Them All.md 86769 655cf7af3c25781bf8e2368108d494d0122333e603255c1e5b37a6f58ba82657
83 docs\02_references\06_paper_analyses\P50_2026_MMGeo_deep_dive_for_MERIDIAN.md 8699 d08809943d528500fc3c4769530934c5f2c042408359b4e911dc981d7be3849a