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1SFUFYUUBTL Published as a conference paper at ICLR 2018 Rotated image: X0 Rotated image: X3 Rotated image: X 2 Rotated image: X1 ConvNet model F(.) ConvNet model F(.) ConvNet model F(.) ConvNet model F(.) Image X Predict 270 degrees rotation (y=3) Rotate 270 degrees g( X , y=3) Rotate 180 degrees g( X , y=2) Rotate 90 degrees g( X , y=1) Rotate 0 degrees g( X , y=0) Maximize prob. F3( X 3) Predict 0 degrees rotation (y=0) Maximize prob. F2( X2) Maximize prob. F1( X 1) Maximize prob. F0( X 0) Predict 180 degrees rotation (y=2) Predict 90 degrees rotation (y=1) Objectives: Figure 2: Illustration of the self-supervised task that we propose for semantic feature learning. Given four possible geometric transformations, the 0, 90, 180, and 270 degrees rotations, we train a ConvNet model F(.) to recognize the rotation that is applied to the image that it gets as input. Fy(Xy⇤ ) is the probability of rotation transformation y predicted by model F(.) when it gets as input an image that has been transformed by the rotation transformation y⇤. to successfully predict the rotation of an image the ConvNet model must necessarily learn to localize <(JEBSJT *$-3>͔ΒҾ༻ ˠճసͷ༧ଌʹମͷ֓೦ʢҐஔɼ࢟ɼछྨͳͲʣͷཧղ͕ඞཁ ˠਖ਼ղϥϕϧը૾ʹద༻͞Εͨσʔλ֦ுͷใ͔ΒࣗಈͰ࡞Մೳ
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*$.-> ύονͷಛྔ͔ΒύονҐஔ͕ݸઌͷύονͷಛྔΛ༧ଌ ༷ʑͳϓϨςΩετλεΫ fθ gφ x z c InfoNCE [256, 256, 3] [7, 7, 4096] [7, 7, 4096] Masked ConvNet Patched ResNet-161 fθ hψ x z y Cross Ent [256, 256, 3] [7, 7, 4096] [1000, 1] Linear Self-supervised pre-training 100% images; 0% labels Linear classification 100% images and labels fθ hψ x z y Cross Ent [224, 224, 3] [14, 14, 4096] ResNet-33 Efficient classification 1% to 100% images and labels fθ hψ x z y Multi Task [H, W, 3] [H/16, W/16, 4096] Transfer learning 100% images and labels hψ x y Cross Ent [224, 224, 3] [1000, 1] ResNet-152 Supervised training 1% to 100% images and labels Baseline Pre-training Evaluation Pre-trained Fixed / Tuned ResNet-161 Image x Feature Extractor fθ Patched ResNet-161 z c Context Network gφ Masked ConvNet Faster-RCNN [20, 1] [1000, 1] Pre-trained Fixed / Tuned ResNet-161 Pre-trained Fixed Patched ResNet-161 al configuration (if there is no spe- e parts, then it is “stuff” [1]). We d approach to learn a visual repre- . We demonstrate that the resulting good for both object detection, pro- t on PASCAL VOC 2007 compared , as well as for unsupervised object mining. This means, surprisingly, generalizes across images, despite bjective function that operates on a That is, instance-level supervision ormance on category-level tasks. a good image representation is as n appropriate generative model. An of natural images would both gener- o their natural distribution, and be hat it would seek common causes d share information between them. atent structure given an image is in- vely simple models. To deal with sues, a number of works, such as m [23], contrastive divergence [22], 3 2 1 5 4 8 7 6 ); Y = 3 , X = ( Figure 2. The algorithm receives two patches in one of these eight possible spatial arrangements, without any context, and must then classify which configuration was sampled. model (e.g. a deep network) to predict, from a single word, the n preceding and n succeeding words. In principle, sim- ilar reasoning could be applied in the image domain, a kind of visual “fill in the blank” task, but, again, one runs into the problem of determining whether the predictions themselves $POUSBTUJWF1SFEJDUJWF$PEJOH $POUFYU1SFEJDUJPO k <)ÉOB ff *$.->͔ΒҾ༻ <%PFSTDI *$$7>͔ΒҾ༻
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of Visual Representations (a) Without color distortion. (b) With color distortion. Figure 6. Histograms of pixel intensities (over all channels) for different crops of two different images (i.e. two rows). The image for the first row is from Figure 4. All axes have the same range. Color distortion strength Methods 1/8 1/4 1/2 1 1 (+Blur) AutoAug SimCLR 59.6 61.0 62.6 63.2 64.5 61.1 Supervised 77.0 76.7 76.5 75.7 75.4 77.1 Table 1. Top-1 accuracy of unsupervised ResNet-50 using linear evaluation and supervised ResNet-505, under varied color distor- tion strength (see Appendix A) and other data transformations. Strength 1 (+Blur) is our default data augmentation policy. ric data augmentation hurts the performance. Nonetheless, this setup should not substantively change the impact of individual data augmentations or their compositions. 1 P R 3 P 0 R 7RS 5 5 5 5 5 5 5 5 5 5 5 5 5 6 S 5 6 S 5 6 S 5 5 5 5 Figure 7. Linear evaluation of models with varied depth and width. Models in blue dots are ours trained for 100 epochs, models in red stars are ours trained for 1000 epochs, and models in green crosses are supervised ResNets trained for 90 epochs7 (He et al., 2016). shown in Table 1. Stronger color augmentation substan- tially improves the linear evaluation of the learned unsuper- vised models. In this context, AutoAugment (Cubuk et al., 2019), a sophisticated augmentation policy found using su- pervised learning, does not work better than simple cropping όοναΠζͱΤϙοΫʹΑΔਫ਼มԽ ϞσϧαΠζʹΑΔਫ਼มԽ όοναΠζɼΤϙοΫɼϞσϧαΠζ͕େ͖͍΄ͲରরֶशͷޮՌ্͕ <$IFO *$.->͔ΒҾ༻ɼҰ෦վม <$IFO *$.->͔ΒҾ༻ ɿFQPDIͷڭࢣ͋Γֶश ɿFQPDIͷࣗݾڭࢣ͋Γֶश ɿ FQPDIͷࣗݾڭࢣ͋Γֶश
0 100 0 50 epochs kNN acc. w/ stop-grad w/o stop-grad acc. (%) w/ stop-grad 67.7±0.1 w/o stop-grad 0.1 oss. Without stop-gradient it degenerates immediately. Middle ged std over all channels. Right plot: validation accuracy of a ation (“w/ stop-grad” is mean±std over 5 trials). (ablation in Sec. 4.4) or ReLU. This MLP has 2 layers. The dimension of h’s input and output (z and p) is d = 2048, and h’s hidden layer’s dimension is 512, making h pred. MLP h acc. (%) baseline lr with cosine decay 67.7 (a) no pred. MLP 0.1 (b) fixed random init. 1.5 (c) lr not decayed 68.1 Table 1. Effect of prediction MLP (ImageNet linear evaluation accuracy with 100-epoch pre-training). In all these variants, we use the same schedule for the encoder f (lr with cosine decay). that with stop-gradient, the std value is near 1 p d . This indi- cates that the outputs do not collapse, and they are scattered on the unit hypersphere. batch size 64 acc. (%) 66.1 Table 2. Effect of b racy with 100-epoch case (a) none (b) hidden-only (c) default (d) all Table 3. Effect of geNet linear evaluat <$IFO $713>͔ΒҾ༻ ޯఀࢭॲཧͱ༧ଌϔουʹΑΔඇରশͳϞσϧߏͷΈ߹Θ͕ͤॏཁ <$IFO $713>͔ΒҾ༻
Properties in Self-Supervised Vision Transformers Mathilde Caron1,2 Hugo Touvron1,3 Ishan Misra1 Herv´ e Jegou1 Julien Mairal2 Piotr Bojanowski1 Armand Joulin1 1 Facebook AI Research 2 Inria⇤ 3 Sorbonne University Figure 1: Self-attention from a Vision Transformer with 8 ⇥ 8 patches trained with no supervision. We look at the self-attention of the [CLS] token on the heads of the last layer. This token is not attached to any label nor supervision. These maps show that the model automatically learns class-specific features leading to unsupervised object segmentations. Abstract In this paper, we question if self-supervised learning pro- vides new properties to Vision Transformer (ViT) [19] that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the follow- ing observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised 1. Introduction Transformers [70] have recently emerged as an alternative to convolutional neural networks (convnets) for visual recog- nition [19, 69, 83]. Their adoption has been coupled with a training strategy inspired by natural language processing (NLP), that is, pretraining on large quantities of data and finetuning on the target dataset [18, 55]. The resulting Vision Transformers (ViT) [19] are competitive with convnets but, they have not yet delivered clear benefits over them: they v:2104.14294v2 [cs.CV] 24 May 2021 ˠϥϕϧใ͕ແͯ͘ਖ਼֬ͳମྖҬΛ֫ಘ Supervised DINO Table 7: Important component for self-supervised ViT pre- training. Models are trained for 300 epochs with ViT-S/16. We study the different components that matter for the k-NN and linear (“Lin.”) evaluations. For the different variants, we highlight the differences from the default DINO setting. The best combination is the momentum encoder with the multicrop augmentation and the cross-entropy loss. We also report results with BYOL [30], MoCo-v2 [15] and SwAV [10]. Supervised DINO Random Supervised DINO ViT-S/16 22.0 27.3 45.9 Table 7: Imp training. Mod study the diffe (“Lin.”) evalu differences fro is the momen the cross-entr MoCo-v2 [15] Method M 1 DINO 2 3 ˠڭࢣ͋Γֶशͱൺͯ%*/0ମྖҬʹूத <$BSPO *$$7>͔ΒҾ༻ <$BSPO *$$7>͔ΒҾ༻
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5.5 0.0 1.9 3.5 3.3 ecoder can im- 128 84.9 69.1 256 84.8 71.3 512 84.9 73.5 768 84.4 73.1 1024 84.3 73.1 (b) Decoder width. The decoder can be nar- rower than the encoder (1024-d). encoder w/ [M] 84.2 59.6 3.3⇥ encoder w/o [M] 84.9 73.5 1⇥ (c) Mask token. An encoder without mask to- kens is more accurate and faster (Table 2). lin 73.5 73.9 72.3 71.6 xels as recon- case ft lin none 84.0 65.7 crop, fixed size 84.7 73.1 crop, rand size 84.9 73.5 crop + color jit 84.3 71.9 (e) Data augmentation. Our MAE works with minimal or no augmentation. case ratio ft lin random 75 84.9 73.5 block 50 83.9 72.3 block 75 82.8 63.9 grid 75 84.0 66.0 (f) Mask sampling. Random sampling works the best. See Figure 6 for visualizations. eriments with ViT-L/16 on ImageNet-1K. We report fine-tuning (ft) and linear probing (lin) accuracy (%). If he decoder has depth 8 and width 512, the reconstruction target is unnormalized pixels, the data augmentation he masking ratio is 75%, and the pre-training length is 800 epochs. Default settings are marked in gray . with the masking ratio until the y gap is up to ⇠20% (54.6% vs. encoder dec. depth ft acc hours speedup ViT-L, w/ [M] 8 84.2 42.4 - block 50% grid 75% random 75% ϚεΫྫͱ֤ύονͷ༧ଌ݁Ռ *NBHF/FU,ʹର͢Δੑೳ ϚεΫύονͷ༧ଌੑೳ͕ߴ͍ ྑ͍ಛදݱͷ֫ಘͱݶΒͳ͍ <)F $713>͔ΒҾ༻
. raining to e encoder ognition). y designed esign. We and shal- lt decoder With this only pro- cantly re- 10 20 30 40 50 60 70 80 90 83 84 85 83.2 83.4 83.4 84.7 84.9 85.0 84.9 84.9 84.5 83.0 fine-tuning masking ratio (%) 10 20 30 40 50 60 70 80 90 50 60 70 54.6 58.9 61.7 67.0 69.9 71.8 73.2 73.5 71.8 66.1 linear probing masking ratio (%) Figure 5. Masking ratio. A high masking ratio (75%) works well for both fine-tuning (top) and linear probing (bottom). The y-axes are ImageNet-1K validation accuracy (%) in all plots in this paper. 4. ImageNet Experiments ϥϯμϜϚεΩϯάઓུʹ͓͚ΔϚεΫʹΑΔੑೳมԽ cks ft lin 84.8 65.5 84.9 70.0 84.9 71.9 84.9 73.5 84.4 73.3 epth. A deep decoder can im- obing accuracy. dim ft lin 128 84.9 69.1 256 84.8 71.3 512 84.9 73.5 768 84.4 73.1 1024 84.3 73.1 (b) Decoder width. The decoder can be nar- rower than the encoder (1024-d). case ft lin FLOPs encoder w/ [M] 84.2 59.6 3.3⇥ encoder w/o [M] 84.9 73.5 1⇥ (c) Mask token. An encoder without mask to- kens is more accurate and faster (Table 2). ft lin norm) 84.9 73.5 orm) 85.4 73.9 84.6 72.3 n 85.3 71.6 ction target. Pixels as recon- ts are effective. case ft lin none 84.0 65.7 crop, fixed size 84.7 73.1 crop, rand size 84.9 73.5 crop + color jit 84.3 71.9 (e) Data augmentation. Our MAE works with minimal or no augmentation. case ratio ft lin random 75 84.9 73.5 block 50 83.9 72.3 block 75 82.8 63.9 grid 75 84.0 66.0 (f) Mask sampling. Random sampling works the best. See Figure 6 for visualizations. E ablation experiments with ViT-L/16 on ImageNet-1K. We report fine-tuning (ft) and linear probing (lin) accuracy (%). If the default is: the decoder has depth 8 and width 512, the reconstruction target is unnormalized pixels, the data augmentation ized cropping, the masking ratio is 75%, and the pre-training length is 800 epochs. Default settings are marked in gray . eases steadily with the masking ratio until the the accuracy gap is up to ⇠20% (54.6% vs. fine-tuning, the results are less sensitive to the wide range of masking ratios (40–80%) work encoder dec. depth ft acc hours speedup ViT-L, w/ [M] 8 84.2 42.4 - ViT-L 8 84.9 15.4 2.8⇥ ViT-L 1 84.8 11.6 3.7⇥ ViT-H, w/ [M] 8 - 119.6† - ϚεΩϯάઓུʹΑΔੑೳมԽ ."&ʹ͓͚ΔϚεΫͷධՁ ΑΓޮՌతͳࣄલֶशͱ͢ΔͨΊʹϚεΫํ๏.*.ͷઓུΛվળ <)F $713>͔ΒҾ༻
ߴ͍ྖҬΛϚεΫ͢Δ͜ͱͰੑೳ͕վળ ϚεΫͷվળɿ"UUFOUJPO(VJEFE.*.<,BLPHFPSHJPV &$$7> Attention-Guided Masked Image Modeling 3 (a) Input (b) Random (c) Random (d) Block (e) Attention (f) AttMask (g) AttMask Image (30) (75) Wise Map High Low Fig. 1. Different than random masking strategies (b-d), our attention-guided masking (AttMask) uses the attention map arising in the encoder (e) to mask the most highly attended by default (f), <,BLPHFPSHJPV &$$7>͔ΒҾ༻
H T W H input target Figure 1: Masked Autoencoders as spatiotemporal learners. We mask a large subset (e.g., 90%) of random patches in spacetime. An encoder operates on the set of visible patches. A small decoder then processes the full set of encoded patches and mask tokens to reconstruct the input. Except for patch and positional embeddings, neither the encoder, the decoder, nor the masking strategy, has any spatiotemporal inductive bias. To the extreme, if a video has T identical static frames, randomly sampling 1/T of all spacetime patches would reveal most of the static frame. Because slow motion is more likely than fast motion in natural videos, the masking ratio can be very high as we observe empirically. The higher masking ratio leads to a more efficient solution in practice. Following the MAE in [31] that applies the encoder only on visible tokens, a masking ratio of 90% reduces the encoder time and memory complexity to <1/10. Put together with a small decoder [31], the MAE pre-training can achieve a theoretically 7.7⇥ reduction in computation vs. encoding all tokens. In fact, the computation reduction is so large that the data loading time becomes a new bottleneck; even so, we record a 4.1⇥ wall-clock speedup. Such a significant speedup is of great importance for video research that is large-scale and time-consuming. We report strong results on a variety of video recognition datasets. Our MAE pre-training greatly improves generalization performance: on Kinetics-400 [35], it increases the accuracy of ViT-Large [18] by absolute 13% vs. training from scratch, while it takes less wall-clock training time overall (pre-training plus fine-tuning). Our MAE pre-training can outperform its supervised pre-training ."&"T4QBUJPUFNQPSBM-FBSOFST <'FJDIUFOIPGFS /FVS*14> Encoder Decoder … … Target MSE Input ) ( , Figure 1: Audio-MAE for audio self-supervised learning. An audio recording is first transformed into a spectrogram and split into patches. We embed patches and mask out a large subset (80%). An encoder then operates on the visible (20%) patch embeddings. Finally, a decoder processes the order-restored embeddings and mask tokens to reconstruct the input. Audio-MAE is minimizing the mean square error (MSE) on the masked portion of the reconstruction and the input spectrogram. This computational burden has been addressed in different ways. A popular approach is to reduce the sequence length in self-attention. Various ViT-based architectures have been developed to alleviate such issues for image and video understanding. For example, Swin-Transformer [19] only performs local attention within windows that shift across layers. MViT [20] employs pooling attention to construct a hierarchy of Transformers where sequence lengths are downsampled. For self-supervised learning, MAE [1] efficiently encodes only a small portion (25%) of visual patches while the majority of patches is discarded. The simplicity and scalability in MAE make it a promising framework for large-scale self-supervised learning. In this work, we study MAE for sound recognition and the unique challenges of the audio domain. We present Audio-MAE (Fig. 1) as unified and scalable framework for learning self-supervised audio representations. Similar to MAE, it is composed of a pair of a Transformer encoder and decoder. Figure 2: Visualizations on the Kinetics-400 [35] validation set (masking ratio 90%). We show the original video (top), masked video (middle), and MAE output (bottom) for each sample. This model reconstructs the original pixels. The video size is 16⇥224⇥224 and the spacetime patch size is 2⇥16⇥16 (the temporal patch size of 2 is not visualized here). Each sample has 8⇥14⇥14=1568 tokens with 156 being visible. For better visualizations, the known patches in the output are from the original input. Fig. 7 shows more examples. Figure 3: Visualizations of the same pre-trained model in Fig. 2 but with a masking ratio of 95%. Instead of predicting pixels [9, 18, 31, 80], another line of research focuses on the tokenization ."&UIBU-JTUFO <)VBOH /FVS*14> εϖΫτϩάϥϜʹରͯ͠ϚεΫ ಈը૾ʹରͯ͠ϚεΫ <)VBOH /FVS*14>͔ΒҾ༻ <'FJDIUFOIPGFS /FVS*14>͔ΒҾ༻
40 50 60 0 20 40 60 80 accuracy (%) wall-clock time (hours) MAE pre-train 800 epochs fine-tune 100 epochs from scratch 400 epochs w/ MAE from scratch 1-view multi-vie Figure 5: MAE pre-training plus fine-tuning is much more accurate and scratch. Here the x-axis is the wall-clock training time (128 A100 GPUs), a accuracy on Kinetics-400 validation. The table shows the final accuracy. ˠ."&ʴϑΝΠϯνϡʔχϯάֶ͍श࣌ؒͰߴੑೳ <'FJDIUFOIPGFS /FVS*14>͔ΒҾ༻
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ϚϧνϞʔμϧͷ֦ுɿ.VMUJ."&<#BDINBOO &$$7> Transformer encoder Decoder Linear proj. Linear proj. Linear proj. Decoder Decoder Selected input patches Original images Masked targets RGB Depth Semantic ... ... ... MultiMAE pre-training Figure 2. (Left) MultiMAE pre-training: A small subset of randomly sampled patches from Transformer encoder Pre-trained MultiMAE encoder Pre-trained MultiMAE encoder Task- specific head(s) Decoder Linear proj. Linear proj. Linear Decoder Selected input patches Original images Masked targets RGB Depth ntic ... ... ... ... ... ... MultiMAE pre-training Single-modal fine-tuning Multi-modal fine-tuning Task- specific head(s) 3(# Transformer encoder Pre-trained MultiMAE encoder Pre-trained MultiMAE encoder Task- specific head(s) Decoder Linear proj. Linear proj. Linear proj. Decoder Decoder Selected input patches Original images Masked targets RGB Depth Semantic ... ... ... ... ... ... ... ... ... MultiMAE pre-training Single-modal fine-tuning Multi-modal fine-tuning Task- specific head(s) Figure 2. (Left) MultiMAE pre-training: A small subset of randomly sampled patches from multiple modalities (e.g., RGB, depth, and semantic segmentation) is linearly projected to tokens with a fixed dimension and encoded using a Transformer. Task-specific decoders reconstruct the masked-out patches by first performing a cross-attention step from queries to the encoded tokens, followed by a shallow Transformer. The queries consist of mask tokens (in gray), with the task-specific encoded tokens added at their respective positions. (Right) Fine-tuning: By pre-training on multiple modalities, MultiMAE lends itself to fine-tuning on single-modal and multi-modal downstream Transformer encoder Pre-trained MultiMAE encoder Pre-trained MultiMAE encoder Task- specific head(s) Decoder Linear proj. Linear proj. Linear proj. Decoder Decoder Selected input patches Original images Masked targets RGB Depth Semantic ... ... ... ... ... ... ... ... ... MultiMAE pre-training Single-modal fine-tuning Multi-modal fine-tuning Task- specific head(s) Figure 2. (Left) MultiMAE pre-training: A small subset of randomly sampled patches from multiple modalities (e.g., RGB, depth, and semantic segmentation) is linearly projected to tokens with a fixed dimension and encoded using a Transformer. Task-specific decoders reconstruct the masked-out patches by first performing a cross-attention step from queries to the encoded tokens, followed by a shallow 4FNBOUJD %FQUI ༧Ίඞཁͳσʔλ <#BDINBOO &$$7>͔ΒҾ༻ɼҰ෦վม
7BMVFɿઢܗࣹӨޙͷશϞμϦςΟͷτʔΫϯ ϚϧνϞʔμϧͷ֦ுɿ.VMUJ."&<#BDINBOO &$$7> ing implementation de- 15 ation details 15 sification fine-tuning . . . . . . . . . . . . 15 ntation . . . . . . . . 15 stimation . . . . . . . 17 se regression tasks . . 17 egies 17 transfer results 18 on on ImageNet 18 E variants 18 raining time 19 the number of segmentation patches constant, we downsam- ple the semantic segmentation input by a factor of 4 and use patches of size 4⇥4. MultiMAE decoder. We illustrate the MultiMAE decoder in Fig 7. Following MAE [35], each decoder has a linear projection layer to adapt the outputs from the encoder to the decoder dimension. After this linear projection, we add both sine-cosine positional embeddings and learned modal- ity embeddings to the decoder inputs. This is then followed by a cross-attention layer, a MLP, and two Transformer blocks. Figure 7. MultiMAE decoders: Tokens from the MultiMAE en- ,FZ 7BMVF 2VFSZ <#BDINBOO &$$7>͔ΒҾ༻ɼҰ෦վม
Atanov Amir Zamir Swiss Federal Institute of Technology Lausanne (EPFL) https://multimae.epfl.ch Masked inputs MultiMAE predictions Target Semantic Depth RGB Masked inputs MultiMAE predictions Target Masked inputs MultiMAE predictions Target Figure 1. MultiMAE pre-training objective. We randomly select 1/6 of all 16⇥16 image patches from multiple modalities and learn to reconstruct the remaining 5/6 masked patches from them. The figure shows validation examples from ImageNet, where masked inputs (left), predictions (middle), and non-masked images (right) for RGB (top), depth (middle), and semantic segmentation (bottom) are provided. Since we do not compute a loss on non-masked patches, we overlay the input patches on the predictions. શʹϚεΫ͞Εͨ߹ʹ͓͍ͯଞϞʔμϧͷใΛͱʹ༧ଌ͕Մೳ <#BDINBOO &$$7>͔ΒҾ༻
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w ࣝৠཹͷޮՌ ڭࢣ 7J5H ͷύϥϝʔλ ɿԯ ੜె 7J5- ͷύϥϝʔλ ɿԯສ X 85.8 72.8 47.1 63.9 (a) Koleo loss X Table 3: (a) E ect of the KoLeo loss term. (b) E ect o term. Evaluation performed on ImageNet-{1k,A} (classifi (segmentation with linear layer, mIoU) and Oxford-M (im same number of iterations, that is smaller than our final ru search tasks (e.g. retrieval), and the MIM loss improves p (a) Comparison on individual metrics Arch ViT-g/14 ViT-L/14 ViT-L/14 Arch ViT-g/14 ViT-L/14 ViT-L/14 Figure 5: E ectiveness of knowledge distillation. C εΫϥονͱൺͯߴ͍ਫ਼Λୡ ը૾ͷΈͰैདྷ๏ͱൺͯߴ͍ਫ਼Λୡ kNN linear Method Arch. Data Text sup. val val ReaL V2 Weakly supervised CLIP ViT-L/14 WIT-400M X 79.8 84.3 88.1 75.3 CLIP ViT-L/14 336 WIT-400M X 80.5 85.3 88.8 75.8 SWAG ViT-H/14 IG3.6B X 82.6 85.7 88.7 77.6 OpenCLIP ViT-H/14 LAION X 81.7 84.4 88.4 75.5 OpenCLIP ViT-G/14 LAION X 83.2 86.2 89.4 77.2 EVA-CLIP ViT-g/14 customú X 83.5 86.4 89.3 77.4 Self-supervised MAE ViT-H/14 INet-1k 5 49.4 76.6 83.3 64.8 DINO ViT-S/8 INet-1k 5 78.6 79.2 85.5 68.2 SEERv2 RG10B IG2B 5 – 79.8 – – MSN ViT-L/7 INet-1k 5 79.2 80.7 86.0 69.7 EsViT Swin-B/W=14 INet-1k 5 79.4 81.3 87.0 70.4 Mugs ViT-L/16 INet-1k 5 80.2 82.1 86.9 70.8 iBOT ViT-L/16 INet-22k 5 72.9 82.3 87.5 72.4 DINOv2 ViT-S/14 LVD-142M 5 79.0 81.1 86.6 70.9 ViT-B/14 LVD-142M 5 82.1 84.5 88.3 75.1 ViT-L/14 LVD-142M 5 83.5 86.3 89.5 78.0 ViT-g/14 LVD-142M 5 83.5 86.5 89.6 78.4 Table 4: Linear evaluation on ImageNet-1k of frozen pretrained features. We report Top-1 accuracy on the validation set for publicly available models trained on public or private data, and with or without text supervision (text sup.). For reference, we also report the kNN performance on the validation set. We compare across any possible architectures (Arch.), at resolution 224 ◊ 224 unless stated otherwise. The dataset used for training EVA-CLIP is a custom mixture, see paper for details (Fang et al., 2023). <0RVBC BS9JW>͔ΒҾ༻
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<&SJDTTPO $713>ɿܭछྨͷԼྲྀλεΫͰैདྷ๏ΛධՁ ԼྲྀλεΫͷసҠੑͷධՁ *NBHF/FU,ͷਫ਼ͱԼྲྀλεΫͷਫ਼ͷؔ ख๏ʹΑͬͯಘҙɾෆಘҙͳԼྲྀλεΫ͕ҟͳΔ *NBHF/FU,ͱ૬͕ؔͳ͍ԼྲྀλεΫଘࡏ How Well Do Self-Supervised Models Transfer? Linus Ericsson University of Edinburgh [email protected] Henry Gouk University of Edinburgh [email protected] Timothy M. Hospedales University of Edinburgh, Samsung AI Research, Cambridge [email protected] Figure 1. Transfer performance is highly correlated with ImageNet performance for many-shot recognition but increasingly less correlated for few-shot recognition, object detection and dense prediction. On the x-axes we plot ImageNet top-1 accuracy and on the y-axes the average transfer log-odds. The gradients of the regression lines describe the correlation, with confidence intervals in shaded areas. For perfect correlation, the ideal line is a positive slope diagonal. Correlation coefficients (Pearson’s r) are shown in the top left of each plot. Abstract Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 1. Introduction Computer vision in the last decade has been driven by increasingly sophisticated convolutional neural networks (CNNs) and the increasingly large datasets used to train them. Nevertheless, progress in this paradigm is ultimately bottlenecked by the data annotation process. This has moti- iv:2011.13377v2 [cs.CV] 29 Mar 2021 <&SJDTTPO $713>͔ΒҾ༻
$713>ɿରরֶशͷ"VUP"VHNFOU3BOE"VHNFOUͷద༻Λతͱͨ͠ϋΠύϥ୳ࡧ Figure 3: For SVHN, we plot the supervised classification accuracy (y-axis) vs the InfoNCE loss function (left), contrastive top-1 accuracy (middle), and self-supervised linear rotation accuracy (right), for a self-supervised model trained using one of each transformation used by SelfAugment. Neither of the left two training metrics are a consistent measure of the quality of the representations, while the rotation prediction accuracy provides a strong linear relationship. test2007 set. • COCO2014 [14] multi-class image classification Fol- lowing [42], we train linear SVMs [43] on the frozen network and evaluate the accuracy over three end-to-end runs, denoted as COCO-C. instance segmentation, We age pairs), while the right plot shows the rotation prediction accuracy for the image transformations in O evaluated after 100 training epochs. Using high or low values of InfoNCE or contrastive accuracy to select the best transformations would select a mixture of mediocre transformations, missing ରরֶशͷଛࣦɼରরֶशͷਫ਼ɼ֯༧ଌͷਫ਼ͱ47)/ʹର͢ΔઢܗධՁͷؔ <3FFE $713>͔ΒҾ༻ ରরֶशͷଛࣦਫ਼ͱઢܗධՁͷਫ਼ແ૬ؒ ֶशʹ༻͍ͯ͠ͳ͍ϓϨςΩετλεΫʢ֯༧ଌʣ Λࢦඪͱͯ͠ϋΠύϥ୳ࡧ͕Մೳ
4JN$-3ʹ͓͚Δ <$IFO *$.->͔ΒҾ༻ ."&ʹ͓͚Δ Figure 6. Mask sampling strategies determine the pretext task difficulty, influencing reconstruction quality and representations (Table 1f). Here each output is from an MAE trained with the spec- ified masking strategy. Left: random sampling (our default). Mid- dle: block-wise sampling [2] that removes large random blocks. Right: grid-wise sampling that keeps one of every four patches. Images are from the validation set. 100 200 400 800 1600 82 83 84 85 82.3 83.3 84.3 84.9 85.1 fine-tuning epochs (log-scale) 100 200 400 800 1600 60 65 70 75 57.3 64.4 69.7 73.5 75.1 linear probing epochs (log-scale) Figure 7. Training schedules. A longer training schedule gives a noticeable improvement. Here each point is a full training sched- ule. The model is ViT-L with the default setting in Table 1. <)F $713>͔ΒҾ༻
Contrastive Learning of Visual Representations With color distortion. (over all channels) for e. two rows). The image have the same range. ength 1 (+Blur) AutoAug 64.5 61.1 75.4 77.1 ResNet-50 using linear nder varied color distor- 1 P R 3 P 0 R 7RS 5 5 5 5 5 5 5 5 5 5 5 5 5 6 S 5 6 S 5 6 S 5 5 5 5 Figure 7. Linear evaluation of models with varied depth and width. Models in blue dots are ours trained for 100 epochs, models in red stars are ours trained for 1000 epochs, and models in green crosses are supervised ResNets trained for 90 epochs7 (He et al., 2016). 4JN$-3ʹ͓͚ΔʢઢܗධՁʣ <$IFO *$.->͔ΒҾ༻ ."&ʹ͓͚ΔʢϑΝΠϯνϡʔχϯάʣ <)F $713>͔ΒҾ༻ method pre-train data ViT-B ViT-L ViT-H ViT-H448 scratch, our impl. - 82.3 82.6 83.1 - DINO [5] IN1K 82.8 - - - MoCo v3 [9] IN1K 83.2 84.1 - - BEiT [2] IN1K+DALLE 83.2 85.2 - - MAE IN1K 83.6 85.9 86.9 87.8 Table 3. Comparisons with previous results on ImageNet- 1K. The pre-training data is the ImageNet-1K training set (ex- cept the tokenizer in BEiT was pre-trained on 250M DALLE data [50]). All self-supervised methods are evaluated by end-to-end fine-tuning. The ViT models are B/16, L/16, H/14 [16]. The best for each column is underlined. All results are on an image size of 224, except for ViT-H with an extra result on 448. Here our MAE reconstructs normalized pixels and is pre-trained for 1600 epochs. 0 200 400 600 76 78 80 82 84 86 88 ViT-B/16 ViT-L/16 ViT-H/14 MAE, IN1K supervised, IN1K, our impl. supervised, IN1K supervised, JFT300M [16] [16] params (M) Figure 8. MAE pre-training vs. supervised pre-training, evalu- ated by fine-tuning in ImageNet-1K (224 size). We compare with the original ViT results [16] trained in IN1K or JFT300M. 0 1 2 70 75 80 85 73.5 81.0 83.1 8 77.6 79.9 80.8 Figure 9. Par of fine-tuned T Table 1. Tunin Our MAE repr sistently better The MAE epochs for b pre-training t on the same h TPU-v3 core epochs and M Comparison nal ViT pape Our impleme better, but ac Our MAE ize better: th higher-capac ɿFQPDIͷڭࢣ͋Γֶश ɿFQPDIͷࣗݾڭࢣ͋Γֶश ɿ FQPDIͷࣗݾڭࢣ͋Γֶश
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