第62回名古屋CV・PRML勉強会：CVPR2025論文紹介 (MambaOut)

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4FMFDUJWF44.T ɿϞσϧ͕ೖྗʹԠͯ͡ύϥϝʔλΛಈతʹௐ੔ ϋʔυ΢ΣΞʹదͨ͠ΞϧΰϦζϜ ɿޮ཰తͳֶशͱਪ࿦Λ࣮ݱ γϯϓϧͳϞσϧߏ଄ ɿ)º(BUFE.-1ˠ.BNCB .BNCB<(VBOE%BP BS9JW> H3 Gated MLP Mamba Linear projection Sequence transformation Nonlinearity (activation or multiplication) X X X ! X Conv SSM X ! ! Conv SSM ⨂ Project Discretize !! ℎ!"# ℎ! "! # $! %! Selection Mechanism GPU SRAM GPU HBM ∆! Selective State Space Model with Hardware-aware State Expansion 4FMFDUJWF44.T .BNCB#MPDL ϋʔυ΢ΣΞʹదͨ͠ΞϧΰϦζϜ <(VBOE%BP BS9JW>͔ΒҾ༻ɼҰ෦վม

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.BNCBͷޮՌ ܭࢉίετɾ଎౓ͷൺֱ <2V BS9JW`>͔ΒҾ༻ capability allows SSMs to achieve
not only ecient inference but also parallel training. However, it should be noted that the most conventional SSMs are time-invariant, meaning that their A, B, C, and are unrelated to the model input G. This would limit context-aware modeling, which leads to inferior performance of SSMs in certain tasks such as selective copying [55]. Table 1. Pros and cons of three primary architectures-RNNs, Transformers, and SSMs-in auto-regressive sequential modeling tasks. Comparison RNNs Transformers SSMs Training Speed Slow (Recurrent) Fast (Parallel) Fast (Convolutional) Inference Speed Fast (Recurrent) Slow (Quadratic-Time) Fast (Recurrent) Complexity $(!⇡2) $(!2⇡) $(!⇡2) Modeling Capabilities (Hidden State) (Attention) (Time-Invariance) Manuscript submitted to ACM θϩγϣοτੑೳͷൺֱ <(VBOE%BP BS9JW>͔ΒҾ༻ɼҰ෦վม RWKV (B. Peng et al. 2023) which were trained with the same tokenizer, dataset, and training length (300B tokens) as our models. (Note that Mamba and Pythia are trained with context length 2048, while RWKV was trained with context length 1024.) Table 3: (Zero-shot Evaluations.) Best results for each size in bold. We compare against open source LMs with various tokenizers, trained for up to 300B tokens. Pile refers to the validation split, comparing only against models trained on the same dataset and tokenizer (GPT-NeoX-20B). For each model size, Mamba is best-in-class on every single evaluation result, and generally matches baselines at twice the model size. M T. P LAMBADA LAMBADA HS PIQA AE AC WG A # # " " " " " " " Hybrid H3-130M GPT2 — 89.48 25.77 31.7 64.2 44.4 24.2 50.6 40.1 Pythia-160M NeoX 29.64 38.10 33.0 30.2 61.4 43.2 24.1 51.9 40.6 Mamba-130M NeoX 10.56 16.07 44.3 35.3 64.5 48.0 24.3 51.9 44.7 Hybrid H3-360M GPT2 — 12.58 48.0 41.5 68.1 51.4 24.7 54.1 48.0 Pythia-410M NeoX 9.95 10.84 51.4 40.6 66.9 52.1 24.6 53.8 48.2 Mamba-370M NeoX 8.28 8.14 55.6 46.5 69.5 55.1 28.0 55.3 50.0 Pythia-1B NeoX 7.82 7.92 56.1 47.2 70.7 57.0 27.1 53.5 51.9 Mamba-790M NeoX 7.33 6.02 62.7 55.1 72.1 61.2 29.5 56.1 57.1 GPT-Neo 1.3B GPT2 — 7.50 57.2 48.9 71.1 56.2 25.9 54.9 52.4 Hybrid H3-1.3B GPT2 — 11.25 49.6 52.6 71.3 59.2 28.1 56.9 53.0 OPT-1.3B OPT — 6.64 58.0 53.7 72.4 56.7 29.6 59.5 55.0 Pythia-1.4B NeoX 7.51 6.08 61.7 52.1 71.0 60.5 28.5 57.2 55.2 RWKV-1.5B NeoX 7.70 7.04 56.4 52.5 72.4 60.5 29.4 54.6 54.3 Mamba-1.4B NeoX 6.80 5.04 64.9 59.1 74.2 65.5 32.8 61.5 59.7 GPT-Neo 2.7B GPT2 — 5.63 62.2 55.8 72.1 61.1 30.2 57.6 56.5 Hybrid H3-2.7B GPT2 — 7.92 55.7 59.7 73.3 65.6 32.3 61.4 58.0 OPT-2.7B OPT — 5.12 63.6 60.6 74.8 60.8 31.3 61.0 58.7 Pythia-2.8B NeoX 6.73 5.04 64.7 59.3 74.0 64.1 32.9 59.7 59.1 RWKV-3B NeoX 7.00 5.24 63.9 59.6 73.7 67.8 33.1 59.6 59.6 Mamba-2.8B NeoX 6.22 4.23 69.2 66.1 75.2 69.7 36.3 63.5 63.3 GPT-J-6B GPT2 – 4.10 68.3 66.3 75.4 67.0 36.6 64.1 63.0 OPT-6.7B OPT – 4.25 67.7 67.2 76.3 65.6 34.9 65.5 62.9 Pythia-6.9B NeoX 6.51 4.45 67.1 64.0 75.2 67.3 35.5 61.3 61.7 RWKV-7.4B NeoX 6.31 4.38 67.2 65.5 76.1 67.8 37.5 61.0 62.5 4.3 DNA Modeling Motivated by the success of large language models, there has been recent exploration into using the foundation model paradigm for genomics. DNA has been likened to language in that it consists of sequences of discrete tokens with a nite 4.2.2 Downstream Evaluations Table 3 shows the performance of Mamba on a range of popular downstream zero-shot evaluation tasks. We compare against the most well-known open source models at these sizes, most importantly Pythia (Biderman et al. 2023) and RWKV (B. Peng et al. 2023) which were trained with the same tokenizer, dataset, and training length (300B tokens) as our models. (Note that Mamba and Pythia are trained with context length 2048, while RWKV was trained with context length 1024.) Table 3: (Zero-shot Evaluations.) Best results for each size in bold. We compare against open source LMs with various tokenizers, trained for up to 300B tokens. Pile refers to the validation split, comparing only against models trained on the same dataset and tokenizer (GPT-NeoX-20B). For each model size, Mamba is best-in-class on every single evaluation result, and generally matches baselines at twice the model size. M T. P LAMBADA LAMBADA HS PIQA AE AC WG A # # " " " " " " " Hybrid H3-130M GPT2 — 89.48 25.77 31.7 64.2 44.4 24.2 50.6 40.1 Pythia-160M NeoX 29.64 38.10 33.0 30.2 61.4 43.2 24.1 51.9 40.6 Mamba-130M NeoX 10.56 16.07 44.3 35.3 64.5 48.0 24.3 51.9 44.7 Hybrid H3-360M GPT2 — 12.58 48.0 41.5 68.1 51.4 24.7 54.1 48.0 Pythia-410M NeoX 9.95 10.84 51.4 40.6 66.9 52.1 24.6 53.8 48.2 Mamba-370M NeoX 8.28 8.14 55.6 46.5 69.5 55.1 28.0 55.3 50.0 Pythia-1B NeoX 7.82 7.92 56.1 47.2 70.7 57.0 27.1 53.5 51.9 Mamba-790M NeoX 7.33 6.02 62.7 55.1 72.1 61.2 29.5 56.1 57.1 GPT-Neo 1.3B GPT2 — 7.50 57.2 48.9 71.1 56.2 25.9 54.9 52.4 Hybrid H3-1.3B GPT2 — 11.25 49.6 52.6 71.3 59.2 28.1 56.9 53.0 OPT-1.3B OPT — 6.64 58.0 53.7 72.4 56.7 29.6 59.5 55.0 Pythia-1.4B NeoX 7.51 6.08 61.7 52.1 71.0 60.5 28.5 57.2 55.2 RWKV-1.5B NeoX 7.70 7.04 56.4 52.5 72.4 60.5 29.4 54.6 54.3 Mamba-1.4B NeoX 6.80 5.04 64.9 59.1 74.2 65.5 32.8 61.5 59.7 GPT-Neo 2.7B GPT2 — 5.63 62.2 55.8 72.1 61.1 30.2 57.6 56.5 ˠͷϞσϧαΠζͰ5SBOTGPSNFSΑΓߴ͍ੑೳ ˠ44.Tʢ.BNCBͷϕʔεख๏ʣ͸γʔέϯγϟϧͳॲཧˠ3//Tͱಉ౳ͷਪ࿦଎౓ɾܭࢉྔ

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University of Singapore [email protected] [email protected] Code: https://github.com/yuweihao/MambaOut Linear Linear Conv σ SSM Linear σ Linear Linear Conv Linear σ Gated CNN block (e.g. Our MambaOut) Mamba block (e.g. Vision Mamba) (a) (b) Figure 1. (a) Architecture of Gated CNN [20] and Mamba [28] blocks (omitting Normalization and shortcut). The Mamba block extends the Gated CNN with an additional state space model (SSM). As will be conceptually discussed in Section 3, SSM is not necessary for image classification on ImageNet [21, 72]. To empirically verify this claim, we stack Gated CNN blocks to build a series of models named MambaOut. (b) MambaOut outperforms visual Mamba models, e.g., Vision Mamhba [112], VMamba [56] and PlainMamba [96], on ImageNet image classification. .BNCBϒϩοΫ Weihao Yu X National Universit [email protected] Code: https://github.com Linear Linear Conv σ SSM Linear σ Linear Linear Conv Linear σ Gated CNN block (e.g. Our MambaOut) Mamba block (e.g. Vision Mamba) (a) Figure 1. (a) Architecture of Gated CNN [20] and Mamba [28] blocks the Gated CNN with an additional state space model (SSM). As will b image classification on ImageNet [21, 72]. To empirically verify this cla MambaOut. (b) MambaOut outperforms visual Mamba models, e.g., ImageNet image classification. .BNCB0VUϒϩοΫ 44.Λ࡟আ ੑೳ͕.BNCBϞσϧ.BNCB0VUͷͱ͖44.͕༗༻ͱ൑ఆ

w σʔληοτɿ*NBHF/FU ੑೳධՁɿΫϥε෼ྨ Model Token Mixing Type Param (M)
Test@2242 MAC (G) Acc (%) VAN-B0 [31] Conv 4 0.9 75.4 MogaNet-T [50] Conv 5 1.1 79.0 FasterNet-T1 [9] Conv 8 0.9 76.2 InceptionNeXt-A [101] Conv 4 0.5 75.3 DeiT-Ti [79] Attn 6 1.3 72.2 T2T-ViT-7 [102] Attn 4 1.1 71.7 PVTv2-B0 [85] Conv + Attn 3 0.6 70.5 MobileViTv3-XS [83] Conv + Attn 3 0.9 76.7 EMO-6M [109] Conv + Attn 6 1.0 79.0 Vim-Ti [112] Conv + SSM 7 1.5 76.1 LocalVim-T [41] Conv + SSM 8 1.5 76.2 EfficientVMamba-T [64] Conv + SSM 6 0.8 76.5 EfficientVMamba-S [64] Conv + SSM 11 1.3 78.7 MambaOut-Femto Conv 7 1.2 78.9 PoolFormer-S24 [99] Pool 21 3.4 80.3 ConvNeXt-T [58] Conv 29 4.5 82.1 VAN-B2 [31] Conv 27 5.0 82.8 ConvFormer-S18 [100] Conv 27 3.9 83.0 MogaNet-S [50] Conv 25 5.0 83.4 InternImage-T [86] Conv 30 5 83.5 InceptionNeXt-T [101] Conv 28 4.2 82.3 DeiT-S [79] Attn 22 4.6 79.8 T2T-ViT-14 [102] Attn 22 4.8 81.5 Swin-T [57] Attn 29 4.5 81.3 Focal-Tiny [97] Attn 29 4.9 82.2 CSWin-T [24] Attn 23 4.3 82.7 CoAtNet-0 [19] Conv + Attn 25 4.2 81.6 iFormer-S [76] Conv + Attn 20 4.8 83.4 MOAT-0 [95] Conv + Attn 28 5.7 83.3 CAFormer-S18 [100] Conv + Attn 26 4.1 83.6 SG-Former-S [71] Conv + Attn 23 4.8 83.2 TransNeXt-Tiny [75] Conv + Attn 28 5.7 84.0 Vim-S [112] Conv + SSM 26 5.1 80.5 VMamba-T [56] Conv + SSM 22 5.6 82.2 Mamba-2D-S [49] Conv + SSM 24 – 81.7 LocalVim-S [41] Conv + SSM 28 4.8 81.2 LocalVMamba-T [41] Conv + SSM 26 5.7 82.7 EfficientVMamba-B [64] Conv + SSM 33 4.0 81.8 PlainMamba-L1 [96] Conv + SSM 7 3.0 77.9 VMambaV3-T* [55] Conv + SSM 31 4.9 82.6 MambaOut-Tiny Conv 27 4.5 82.7 Model Token Mixing Type Param (M) Test@2242 MAC (G) Acc (%) ConvNeXt-S [58] Conv 50 8.7 83.1 VAN-B3 [31] Conv 45 9.0 83.9 ConvFormer-S36 [100] Conv 40 7.6 84.1 InternImage-S [86] Conv 50 8 84.2 MogaNet-B [50] Conv 44 9.9 84.3 T2T-ViT-19 [102] Attn 39 8.5 81.9 Swin-S [57] Attn 50 8.7 83.0 Focal-Small [97] Attn 51 9.1 83.5 CSWin-S [24] Attn 35 6.9 83.6 MViTv2-S [51] Attn 35 7.0 83.6 CoAtNet-1 [19] Conv + Attn 42 8.4 83.3 UniFormer-B [48] Conv + Attn 50 8.3 83.9 CAFormer-S36 [100] Conv + Attn 39 8.0 84.5 SG-Former-M [71] Conv + Attn 39 7.5 84.1 TransNeXt-Small [75] Conv + Attn 50 10.3 84.7 VMamba-S [56] Conv + SSM 44 11.2 83.5 LocalVMamba-S [41] Conv + SSM 50 11.4 83.7 PlainMamba-L2 [96] Conv + SSM 25 8.1 81.6 VMambaV3-S* [55] Conv + SSM 50 8.7 83.6 MambaOut-Small Conv 48 9.0 84.1 ConvNeXt-B [58] Conv 89 15.4 83.8 RepLKNet-31B [23] Conv 79 15.3 83.5 ConvFormer-M36 [100] Conv 57 12.8 84.5 HorNet-B [70] Conv 88 15.5 84.3 MogaNet-L [50] Conv 83 15.9 84.7 InternImage-B [86] Conv 97 16 84.9 DeiT-B [79] Attn 86 17.5 81.8 T2T-ViT-24 [102] Attn 64 13.8 82.3 Swin-B [57] Attn 88 15.4 83.5 CSwin-B [24] Attn 78 15.0 84.2 MViTv2-B [51] Attn 52 10.2 84.4 CoAtNet-2 [19] Conv + Attn 75 15.7 84.1 iFormer-L [76] Conv + Attn 87 14.0 84.8 MOAT-2 [95] Conv + Attn 73 17.2 84.7 CAFormer-M36 [100] Conv + Attn 56 13.2 85.2 TransNeXt-Base [75] Conv + Attn 90 18.4 84.8 VMamba-B [56] Conv + SSM 75 18.0 83.7 Mamba-2D-B [49] Conv + SSM 92 – 83.0 PlainMamba-L3 [96] Conv + SSM 50 14.4 82.3 VMambaV3-B* [55] Conv + SSM 89 15.4 83.9 MambaOut-Base Conv 85 15.8 84.2 Table 1. Performance of models on ImageNet at the resolution of 2242. Our MambaOut model employs the Gated CNN block [20]. The Mamba block [28], derived from the Gated CNN block, incorporates an additional SSM (state space model). It is evident that visual .BNCB0VUϞσϧ͕Ұ؏ͯ͠ .BNCBϞσϧΛ্ճΔੑೳΛൃش ˣ Ϋϥε෼ྨʹ44.ͷಋೖ͸ෆཁ $POW "UUOϞσϧ͕ಉαΠζͷ .BNCBϞσϧΛ௒͑ΔੑೳΛൃش ˣ Ϋϥε෼ྨʹ͓͍ͯ .BNCBϞσϧͷੑೳվળʹ௅ઓ͢Δࡍ͸ ৞ΈࠐΈͱ44.ͷ૊Έ߹Θ͕ͤඞཁ

w σʔληοτɿ$0$0 ੑೳධՁɿ෺ମݕग़ɼΠϯελϯεηάϝϯςʔγϣϯ Backbone Token Mixing Type Param (M)
MAC (G) Mask R-CNN 1→ schedule APb APb 50 APb 75 APm APm 50 APm 75 ConvNeXt-T [54] Conv 48 262 44.2 66.6 48.3 40.1 63.3 42.8 FocalNet-T [98] Conv 49 268 46.1 68.2 50.6 41.5 65.1 44.5 Swin-T [57] Attn 48 267 42.7 65.2 46.8 39.3 62.2 42.2 ViT-Adapter-S [12] Attn 48 403 44.7 65.8 48.3 39.9 62.5 42.8 CSWin-T [24] Attn 42 279 46.7 68.6 51.3 42.2 65.6 45.4 PVTv2-B2 [85] Conv + Attn 45 309 45.3 67.1 49.6 41.2 64.2 44.4 SG-Former-S [71] Conv + Attn 41 – 47.4 69.0 52.0 42.6 65.9 46.0 TransNeXt-Tiny [75] Conv + Attn 48 356 49.9 71.5 54.9 44.6 68.6 48.1 VMamba-T [56] Conv + SSM 42 286 46.5 68.5 50.7 42.1 65.5 45.3 LocalVMamba-T [41] Conv + SSM 45 291 46.7 68.7 50.8 42.2 65.7 45.5 EfﬁcientVMamba-B [64] Conv + SSM 53 252 43.7 66.2 47.9 40.2 63.3 42.9 VMambaV3-T [55] Conv + SSM 50 270 47.4 69.5 52.0 42.7 66.3 46.0 PlainMamba-L1 [96] Conv + SSM 31 388 44.1 64.8 47.9 39.1 61.6 41.9 MambaOut-Tiny Conv 43 262 45.1 67.3 49.6 41.0 64.1 44.1 ConvNeXt-S [54] Conv 70 348 45.4 67.9 50.0 41.8 65.2 45.1 FocalNet-S [98] Conv 72 365 48.3 70.5 53.1 43.1 67.4 46.2 Swin-S [57] Attn 69 354 44.8 66.6 48.9 40.9 63.2 44.2 CSWin-S [24] Attn 54 342 47.9 70.1 52.6 43.2 67.1 46.2 PVTv2-B3 [85] Conv + Attn 65 397 47.0 68.1 51.7 42.5 65.7 45.7 SG-Former-M [71] Conv + Attn 51 – 48.2 70.3 53.1 43.6 66.9 47.0 TransNeXt-Small [75] Conv + Attn 69 516 51.1 72.6 56.2 45.5 69.8 49.1 VMamba-S [56] Conv + SSM 64 400 48.2 69.7 52.5 43.0 66.6 46.4 LocalVMamba-S [41] Conv + SSM 69 414 48.4 69.9 52.7 43.2 66.7 46.5 VMambaV3-S [55] Conv + SSM 64 357 48.7 70.0 53.4 43.7 67.3 47.0 MambaOut-Small Conv 65 354 47.4 69.1 52.4 42.7 66.1 46.2 ConvNeXt-B [54] Conv 108 486 47.0 69.4 51.7 42.7 66.3 46.0 FocalNet-B [98] Conv 111 507 49.0 70.9 53.9 43.5 67.9 46.7 Swin-B [57] Attn 107 496 46.9 – – 42.3 – – ViT-Adapter-B [12] Attn 102 557 47.0 68.2 51.4 41.8 65.1 44.9 CSWin-B [24] Attn 97 526 48.7 70.4 53.9 43.9 67.8 47.3 PVTv2-B5 [85] Conv + Attn 102 557 47.4 68.6 51.9 42.5 65.7 46.0 TransNeXt-Base [75] Conv + Attn 109 728 51.7 73.2 56.9 45.9 70.5 49.7 VMamba-B [56] Conv + SSM 96 540 48.5 69.6 53.0 43.1 67.0 46.4 PlainMamba-L2 [96] Conv + SSM 53 542 46.0 66.9 50.1 40.6 63.8 43.6 VMambaV3-B [55] Conv + SSM 108 485 49.2 70.9 53.9 43.9 67.7 47.6 MambaOut-Base Conv 100 495 47.4 69.3 52.2 43.0 66.4 46.3 le 2. Performance of object detection and instance segmentation on COCO with Mask R-CNN. The MACs are measured with ut size of 800 → 1280. ImageNet is unnecessary, aligning with the principle of the longer side does not exceed 1333 pixels. The AdamW 405"ͷ.BNCBϞσϧ͸ .BNCB0VUϞσϧΛ্ճΔੑೳΛൃش ˣ ௕͍γʔέϯεͷλεΫʹ͓͍ͯ44.͸༗༻ .BNCBϞσϧͷੑೳ͸ 405"ͷ$POW "UUOϞσϧ 5SBOT/F9U ͱͷؒʹେ͖ͳΪϟοϓ͕ଘࡏ ˣ ը૾ೝࣝʹ͓͚Δ.BNCBͷ༗ޮੑΛࣔ͢ʹ͸ ͜ΕΒͷ405"ϞσϧͷੑೳΛ௒͑Δඞཁ͕͋Δ

w σʔληοτɿ"%&, ੑೳධՁɿηϚϯςΟοΫηάϝϯςʔγϣϯ Backbone Token Mixing Type UperNet Param
MAC mIoU mIoU (M) (G) (SS) (MS) ConvNeXt-T [54] Conv 60 939 46.0 46.7 HorNet-T [70] Conv 55 924 49.2 49.3 ConvFormer-S18 [100] Conv 54 925 47.5 48.6 InternImage-T [86] Conv 59 944 47.9 48.1 Swin-T [57] Attn 60 945 44.4 45.8 Twins-S [15] Attn 54 901 46.2 47.1 Focal-T [97] Attn 62 998 45.8 47.0 CSWin-T [24] Attn 60 959 49.3 50.7 UniFormer-S [48] Conv + Attn 52 955 47.0 48.5 CAFormer-S18 [100] Conv + Attn 54 1024 48.1 48.9 SG-Former-S [71] Conv + Attn 53 989 49.9 51.5 TransNeXt-Tiny [75] Conv + Attn 59 978 51.1 51.2 VMamba-T [56] Conv + SSM 55 964 47.3 48.3 LocalVMamba-T [41] Conv + SSM 57 970 47.9 49.1 EfﬁcientVMamba-B [64] Conv + SSM 65 930 46.5 47.3 PlainMamba-L2 [96] Conv + SSM 55 285 – 46.8 PlainMamba-L3 [96] Conv + SSM 81 419 – 49.1 VMambaV3-T [56] Conv + SSM 62 949 47.9 48.8 MambaOut-Tiny Conv 54 938 47.4 48.6 ConvNeXt-S [54] Conv 82 1027 48.7 49.6 HorNet-S [70] Conv 85 1027 50.0 50.5 ConvFormer-S36 [100] Conv 67 1003 49.6 50.7 InternImage-S [86] Conv 80 1017 50.1 50.9 Swin-S [57] Attn 81 1038 47.6 49.5 Twins-B [15] Attn 89 1020 47.7 48.9 to be continued to the right Backbone Token Mixing Type UperNet Param MAC mIoU mIoU (M) (G) (SS) (MS) continued from the left Focal-S [97] Attn 85 1130 48.0 50.0 CSWin-S [24] Attn 65 1027 50.4 51.5 CAFormer-S36 [100] Conv + Attn 67 1197 50.6 50.8 SG-Former-M [71] Conv + Attn 68 1114 51.2 52.1 TransNeXt-Small [75] Conv + Attn 80 1089 52.2 52.3 VMamba-S [56] Conv + SSM 76 1081 49.5 50.5 LocalVMamba-S [41] Conv + SSM 81 1095 50.0 51.0 VMambaV3-S [55] Conv + SSM 82 1028 50.6 51.2 MambaOut-Small Conv 76 1032 49.5 50.6 ConvNeXt-B [54] Conv 122 1170 49.1 49.9 FocalNet-B [98] Conv 126 1192 50.5 51.4 HorNet-B [70] Conv 126 1171 50.5 50.9 ConvFormer-M36 [100] Conv 85 1113 50.4 51.3 InternImage-B [86] Conv 128 1185 50.8 51.3 Swin-B [57] Attn 121 1188 48.1 49.7 Twins-L [15] Attn 133 1164 48.8 50.2 Focal-B [97] Attn 126 1354 49.0 50.5 CSWin-B [24] Attn 110 1222 51.1 52.2 UniFormer-B [48] Conv + Attn 80 1106 49.5 50.7 CAFormer-M36 [100] Conv + Attn 84 1346 51.7 51.7 SG-Former-B [71] Conv + Attn 109 1304 52.0 52.7 TransNeXt-Base [75] Conv + Attn 121 1268 53.0 53.4 VMamba-B [56] Conv + SSM 110 1226 50.0 51.3 VMambaV3-B [55] Conv + SSM 122 1170 51.0 51.6 MambaOut-Base Conv 112 1178 49.6 51.0 Table 3. Performance of Semantic segmentation with UperNet [93] on ADE20K [111] validation set. The MACs are measured with input size of 512 → 2048. egories. It includes 20,000 images in the training set and 2,000 images in the validation set. In our experiments, Mamba is employed as the backbone for UperNet [93], with initialization from ImageNet pre-trained weights. The train- tion tasks, which align with at least the long-sequence char- acteristic, merits further exploration. To substantiate our claims empirically, we develop MambaOut models that em- ploy Mamba blocks without their core token mixer, SSM. $0$0σʔληοτͱ ಉ༷ͷ܏޲

w .BNCB ঢ়ଶۭؒϞσϧʹج͍ͮͨ/-1෼໺ͷ৽͍͠ϞσϧΞʔΩςΫνϟ 5SBOTGPSNFSͱൺ΂ͯ௿͍ܭࢉྔɼͷύϥϝʔλ਺Ͱಉ౳Ҏ্ͷੑೳΛൃش w .BNCB0VU .BNCB
44. ͸ຊ౰ʹը૾ೝࣝλεΫʹඞཁͳͷ͔Λ࣮ݧతʹ෼ੳ Ϋϥε෼ྨͷΑ͏ͳ୹͍γʔέϯεͰ͸44.͕ͳ͍৔߹͕ߴ͍ੑೳΛൃش ෺ମݕग़΍ηάϝϯςʔγϣϯͷΑ͏ͳ௕͍γʔέϯεͰ͸44.ͷಋೖʹΑͬͯߴ͍ੑೳΛൃش ͨͩ͠ɼ405"ͷ$POW "UUOϞσϧͱൺ΂Δͱ.BNCBϞσϧͷੑೳ͸௿͍ ·ͱΊ

第62回名古屋CV・PRML勉強会：CVPR2025論文紹介 (MambaOut)

第62回名古屋CV・PRML勉強会：CVPR2025論文紹介 (MambaOut)

Naoki Okamoto

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ୈճ໊ݹ԰$7ɾ13.-ษڧձ $713࿦จ঺հɿ.BNCB0VU Ԭຊ௚थʢத෦େֶɾػց஌֮ϩϘςΟΫεݚڀάϧʔϓʣ IUUQNQSHKQ

ࣗݾ঺հ ΞϯαϯϒϧֶशͷͨΊͷ஌ࣝৠཹ IUUQTXXXFDWBOFUQBQFSTFDDW@QBQFST@&$$7 IUNM@&$$7@@QBQFSQIQ ࣗݾڭࢣ͋ΓֶशͷνϡʔτϦΞϧ #FTUHSBQIGPSFOTFNCMF OPEFT

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w ࣌ؒʹ൐͏γεςϜͷಈతͳڍಈΛදݱ͢ΔͨΊͷ਺ֶతϑϨʔϜϫʔΫ<,BMNBO > ݱࡏͷ࣌ࠁ ʹ͓͚Δೖྗ ͱग़ྗ ͷؔ܎Λঢ়ଶ Λհͯ͠ϞσϧԽ

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w 44.TʹೖྗʹԠͯ͡ύϥϝʔλΛಈతʹௐ੔͢Δબ୒ϝΧχζϜΛಋೖ ೖྗ Λೖྗͱ͢Δઢܗ૚ͷग़ྗΛ44.ͷύϥϝʔλ ʹ࢖༻ xt Bt

.BNCBͷޮՌ ܭࢉίετɾ଎౓ͷൺֱ <2V BS9JW`>͔ΒҾ༻ capability allows SSMs to achieve

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w .BNBCͷBS9JWެ։ɿ೥݄೔ w &$$7ͷ࿦จ౤ߘక੾ɿ೥݄೔ .BNCBొ৔͔Βϲ݄ؒͷݚڀ ࿦จ਺ɿຊ ˠԬຊͷҹ৅ɿͲͷΑ͏ʹ.BNCBΛը૾ೝࣝλεΫʹద༻͢Δͷ͔ʢྫɿ.BNCB/%ʣ w

w .BNCBΛը૾ೝࣝλεΫʹԠ༻ͨ͠Ϟσϧ͕਺ଟ͘ొ৔ ͔͠͠ɼ৞ΈࠐΈ΍"UUFOUJPOϕʔεͷ405"Ϟσϧͱൺ΂ͯੑೳ໘ͰྼΔ͜ͱ͕ଟ͍ w ஶऀΒͷٙ໰ɿ.BNCB 44. ͸ຊ౰ʹը૾ೝࣝλεΫʹඞཁʁ .BNCBͷಛੑͱը૾ೝࣝλεΫͷಛੑ͔ΒԾઆΛఏى

w ը૾ೝࣝλεΫ͕௕͍γʔέϯεͷϞσϦϯάΛඞཁͱ͢Δ͔Λߟ࡯ .BNCBɿ5SBOTGPSNFSͷܭࢉίετΛվળ͍ͨ͠ˠ5SBOTGPSNFSͷܭࢉίετΛج४ʹߟ࡯ w τʔΫϯ௕ ɼ࣍ݩ਺ ͷσʔλΛೖྗͱͨ͠ͱ͖ͷ5SBOTGPSNFSϒϩοΫͷ'-01T L D

w τʔΫϯ௕ ͕ Λ௒͍͑ͯΔ͔ΛධՁ 7J54ɿ ɼ7J5#ɿ w Ϋϥε෼ྨ

Ծઆ σʔληοτ λεΫ ঢ়ଶۭؒϞσϧ 44. ͷಛੑ Ծઆ ঢ়ଶۭؒϞσϧ͸ ௕͍γʔέϯεॲཧ

w ԾઆΛݕূ͢ΔͨΊʹ.BNCB͔Β44.ΛऔΓআ͍ͨ.BNCB0VUϞσϧΛߏங .BNCB0VU ˠ(BUFE $// Weihao Yu Xinchao Wang National

w σʔληοτɿ*NBHF/FU ੑೳධՁɿΫϥε෼ྨ Model Token Mixing Type Param (M)

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w σʔληοτɿ"%&, ੑೳධՁɿηϚϯςΟοΫηάϝϯςʔγϣϯ Backbone Token Mixing Type UperNet Param

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