Title: MANIGAN: TEXT-GUIDED IMAGE MANIPULATION

Abstract: We propose a novel generative adversarial network for visual attributes manipulation (ManiGAN), which is able to semantically modify the visual attributes of given images using natural language descriptions. The key to our method is to design a novel co-attention module to combine text and image information rather than simply concatenating two features along the channel direction. Also, a detail correction module is proposed to rectify mismatched attributes of the synthetic image, and to reconstruct text-unrelated contents. Finally, we propose a new metric for evaluating manipulation results, in terms of both the generation of text-related attributes and the reconstruction of text-unrelated contents. Extensive experiments on benchmark datasets demonstrate the advantages of our proposed method, regarding the effectiveness of image manipulation and the capability of generating high-quality results.

1. INTRODUCTION:

Image manipulation refers to the task of changing various aspects of given images from low-level colour or texture (Zhang et al., 2016; Gatys et al., 2016) to high-level semantics (Zhu et al., 2016) , and has numerous potential applications in video games, image editing, and computer-aided design. Recently, with the development of deep learning and generative models, automatic image manipulation becomes possible, including image inpainting (Iizuka et al., 2016; Pathak et al., 2016) , image colourisation (Zhang et al., 2016) , style transfer (Gatys et al., 2016; Johnson et al., 2016) , and domain or attribute translation (Lample et al., 2017; Isola et al., 2017) .

However, all the above works mainly focus on specific tasks, and only few studies (Dong et al., 2017; Nam et al., 2018) concentrate on more general and user-friendly image manipulation by using natural language descriptions. Also, as shown in Fig. 1 , current state-of-the-art methods can only generate low-quality images and fail to effectively manipulate given images on more complicated datasets, such as COCO (Lin et al., 2014) . The less effective performance is mainly because (1) simply concatenating text and image cross-domain features along the channel direction, the model may fail to precisely correlate words and corresponding visual attributes, and thus cannot modified specific attributes required in the text, and (2) conditioned only on a global sentence vector, current state-of-the-art methods lack important fine-grained information at the word-level, which prevents an effective manipulation using natural language descriptions.

In this paper, we aim to manipulate given images using natural language descriptions. In particular, we focus on modifying visual attributes (e.g., category, texture, colour, and background) of input images by providing texts that describe desired attributes. To achieve this, we propose a novel generative adversarial network for visual attributes manipulation (ManiGAN), which allows to effectively manipulate given images using natural language descriptions and to produce high-quality results.

The contribution of our proposed method is fourfold: (1) instead of simply concatenating hidden features generated from a natural language description and image features encoded from the input image along the channel direction, we propose a novel co-attention module where both features can collaborate to reconstruct the input image and also keep the synthetic result semantically aligned with the given text description, (2) a detail correction module (DCM) is introduced to rectify mismatched attributes, and to reconstruct text-unrelated contents existing in the input image, (3) a new metric is proposed, which can appropriately reflect the generation of text-related visual attributes and the reconstruction of text-unrelated contents involved in the image manipulation, and (4) extensive experiments on the CUB (Wah et al., 2011) and COCO (Lin et al., 2014) datasets are performed A bird with black eye rings and a black bill, with a red crown and red belly.

Boat, sunset.

Given Text:

Original SISGAN TAGAN Ours

Figure 1 : Examples of image manipulation using natural language descriptions. Current state-of-theart methods only generate low-quality images, and fail to do manipulation on COCO. In contrast, our method allows the input images to be manipulated accurately corresponding to the given text descriptions while preserving text-unrelated contents.

to demonstrate the superiority of our model, which outperforms existing state-of-the-art methods both qualitatively and quantitatively.

2. RELATED WORK:

There are few studies focusing on image manipulation using natural language descriptions. Dong et al. (2017) proposed a GAN-based encoder-decoder architecture to disentangle the semantics of both input images and text descriptions. Nam et al. (2018) implemented a similar architecture, but introduced a text-adaptive discriminator that can provide specific word-level training feedback to the generator. However, both methods are limited in performance due to a less effective text-image concatenation method and a coarse sentence condition.

Our work is also related to conditional image manipulation. Brock et al. (2016) introduced a VAE-GAN hybridisation model to modify natural images by exploring the latent features. Isola et al. (2017) and Zhu et al. (2017) introduced paired and unpaired image-to-image translation methods based on conditional adversarial networks, respectively. However, all these methods focus mainly on image-to-image same-domain translation instead of image manipulation using cross-domain text descriptions.

Recently, text-to-image generation has drawn much attention due to the success of GANs in generating photo-realistic images. Reed et al. (2016) first proposed to use conditional GANs to generate plausible images from given text descriptions. Zhang et al. (2017) stacked multiple GANs to generate high-resolution images from coarse-to fine-scale. Xu et al. (2018) implemented a spatial attention mechanism to explore the fine-grained information at the word-level. However, all aforementioned methods mainly focus on generating new photo-realistic images from texts, and not on manipulating specific visual attributes of given images using natural language descriptions.

3. GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE MANIPULATION:

Let I denote an input image required to be modified, and S denote a text description given by a user. We aim to semantically manipulate the input image I using the given text S , and also keep the visual attributes of the modified image I semantically aligned with S while preserving textunrelated contents existing in I. To achieve this, we first adopt the ControlGAN (Li et al., 2019) , as our basic framework, as it can effectively control text-to-image generation, and manipulate visual attributes of synthetic images. Then, we propose two novel components: (1) co-attention module, and (2) detail correction module to achieve effective image manipulation. We elaborate our model as follow, and the full architecture diagram is shown in Appendix A.

3.1. CO-ATTENTION MODULE:

As shown in Fig. 2 (a), our co-attention module takes two inputs: (1) the hidden features h ∈ R C×H×D , where C is the number of channels, H and D are the height and width of the feature map, respectively, and (2) the regional image features v ∈ R 256×17×17 of the input image I encoded by the GoogleNet (Szegedy et al., 2015) . The activation value h ∈ R C×H×D is given by h

= h W (v) + b(v)

, where W (v) and b(v) are the learned weights and biases dependent on the regional features v, and denotes Hadamard element-wise product. We use W and b to represent the functions that convert the regional features v to scaling and bias values. Then, the activation value h serves as the input for the next stage. We also apply the co-attention module before implementing an image generation network to produce synthetic images; please see Appendix A for more details.

This linear combination form has been widely used in normalisation techniques (Park et al., 2019; Dumoulin et al., 2016; Huang & Belongie, 2017; De Vries et al., 2017) , but, different from them, (1) our co-attention module is only applied at specific positions instead of all normalisation layers, which requires less computational resources, and (2) our co-attention module is designed to incorporate text and image cross-domain information, where W helps the model to focus on text-related visual attributes, while b provides input image information to help to reconstruct text-unrelated contents. Also, we experimentally find that implementing our co-attention module at all normalisation layers fails to produce reasonable images, which indicates that the normalisation techniques may not be suitable for the tasks requiring different domain information. Following Park et al. (2019) , the functions W and b are implemented by a simple two-layer convolutional network, see Fig. 2 (a) .

What has been learned by the co-attention module?:

To better understand what has been learned by our co-attention module, we conduct an ablation study shown in Fig. 3 to evaluate the effectiveness of W and b. As we can see, without W , some visual attributes cannot be perfectly generated (e.g., white belly in row 1 and the red head in row 2), and without b, the text-unrelated contents (e.g., background) are hard to preserve, which verify our assumption that W behaves as an attention function to help the model focus on text-related visual attributes, and b helps to complete missing text-unrelated details existing in the input image. Also, the visualisation of the channel feature maps of W (v) shown in the last three columns of Fig. 3 validates the attention mechanism of W .

3.2. DETAIL CORRECTION MODULE:

The main purpose of our model is to incorporate input images and then generate modified images aligned with given text descriptions. Then, it may inevitably produce some new visual attributes or mismatched contents that are not required in the given texts. To fix this issue, we propose a

The bird has a black bill, a red crown, and a white belly. detail correction module (DCM) to rectify inappropriate attributes, and to reconstruct text-unrelated contents existing in the input images.

The DCM consists of a generator and a discriminator, and is trained alternatively by minimising both objective functions. The generator, shown in Fig. 2 (b), takes three inputs: (1) the last hidden features h last ∈ R C ×H ×D from the main module (we call our model without the DCM as main module), (2) the word features, and (3) visual features v ∈ R 128×128×128 that are extracted from the input image I by the VGG-16 (Simonyan & Zisserman, 2014) pretrained on ImageNet (Russakovsky et al., 2015) . We have also applied GoogleNet (Szegedy et al., 2015) and ResNet (He et al., 2016) for feature extraction, but both do not perform well. Please refer to Appendix D for a detailed description of the detail correction module.

3.3. OBJECTIVE FUNCTIONS:

We train the main module and detail correction module separately, and the generator and discriminator in both modules are trained alternatively by minimising both the generator loss L G and discriminator loss L D .

Generator objective. The loss function for the generator follows those used in ControlGAN (Li et al., 2019) , but we introduce a regularisation term L reg = 1 -1 C I H I W I ||I -I|| to prevent the network achieving identity mapping, which can penalise large perturbations when the generated image becomes the same as the input image.

L G = - 1 2 E I ∼P G [log(D(I ))] unconditional adversarial loss - 1 2 E I ∼P G [log(D(I , S))]

conditional adversarial loss

EQUATION

EQUATION

where the unconditional adversarial loss makes the synthetic image I indistinguishable from the real image I, the conditional adversarial loss aligns the generated image I with the given text description S, L DAMSM (Xu et al., 2018) measures the text-image similarity at the word-level to provide finegrained feedback for image generation, L corre (Li et al., 2019) determines whether words-related visual attributes exist in the image, and L rec (Li et al., 2019) reduces randomness involved in the generation process. λ 1 , λ 2 , λ 3 , and λ 4 are hyperparameters controlling the importance of additional losses. Note that we do not use L rec when we train the detail correction module.

Discriminator objective. The loss function for the discriminator follows those used in Control-GAN (Li et al., 2019) , and the function used to train the discriminator in the detail correction module is the same as the one used in the last stage of the main module.

L D = - 1 2 E I∼Pdata [log(D(I))] - 1 2 E I ∼P G [log(1 -D(I ))] unconditional adversarial loss - 1 2 E I∼Pdata [log(D(I, S))] - 1 2 E I ∼P G [log(1 -D(I , S))]

conditional adversarial loss

EQUATION

where S is a given text description randomly sampled from the dataset, the unconditional adversarial loss determines whether the given image is real, and the conditional adversarial loss reflects the semantic similarity between images and texts.

Analysis. To prevent the model picking the input image as the solution, i.e., the model becomes an identity mapping network, we first introduce a regularisation term L reg to penalise large perturbations when the generated image becomes the same as the input image, and then we stop the training early when the model reaches a stage achieving the best trade-off between the generation of new visual attributes aligned with given text descriptions and the reconstruction of text-unrelated contents existing in the input images. As for when to stop training, it is based on our proposed measurement metric, called manipulative precision (see Fig. 4 ), which is discussed in Sec. 4.

4. EXPERIMENTS:

To evaluate our model, extensive quantitative and qualitative experiments are carried out. Two stateof-the-art approaches on image manipulation using natural language descriptions, SISGAN (Dong et al., 2017) and TAGAN (Nam et al., 2018) , are compared on the CUB birds (Wah et al., 2011) and more complicated COCO (Lin et al., 2014) datasets. Results for these two baselines are reproduced based on the code released by the authors. Please refer to Appendix A, B, and C for a detailed description of our network structures, the datasets, and training configurations.

Quantitative results. As mentioned above, our model can generate high-quality images compared with state-of-the-art methods. To demonstrate this, we adopt the inceptions score (IS) (Salimans et al., 2016) as the quantitative evaluation measure. In our experiments, we evaluate the IS on a large number of manipulated samples generated from mismatched pairs, i.e., randomly chosen input images manipulated by randomly selected text descriptions.

However, as the IS cannot reflect the quality of the content preservation, the L 1 pixel difference (diff) is calculated between the input images and corresponding modified images. Moreover, using the pixel difference alone may falsely report a good reconstruction due to over-training that the model becomes an identity mapping network. To address this issue, we propose a new measurement metric, called manipulative precision (MP), incorporating both the text-image similarity (sim) (Li et al., 2019) and the pixel difference, where the text-image similarity is calculated by performing the cosine similarity on the text features and corresponding image features encoded from the modified images. This is based on the intuition that if the manipulated images are generated from an identity mapping network, then the text-image similarity should be low, as the synthetic images cannot perfectly keep a semantic consistence with given text descriptions. Thus, the measurement metric is defined as MP = (1diff) × sim.

As shown in Table 1 , our method has the highest MP values on both the CUB and COCO datasets compared with the state-of-the-art approaches, which demonstrates that our method can better generate text-related visual attributes, and also reconstruct text-unrelated contents existing in the input images. The model without main module (i.e., only having the DCM) gets the highest IS, the lowest L 1 pixel difference, and low text-image similarity. This is because the model has become a identity mapping network and loses the capability of image manipulation.

Qualitative results. Figs. 5 and 6 show the visual comparison between our ManiGAN, SISGAN (Dong et al., 2017) , and TAGAN (Nam et al., 2018) on the CUB and COCO datasets, respectively. It can be seen that both state-of-the-art methods are only able to produce low-quality results and cannot effectively manipulate input images on the COCO dataset. However, our method is capable to perform an accurate manipulation and keep a highly semantic consistence between synthetic images and given text descriptions, while preserving text-unrelated contents. For example, shown in the last column of Fig. 6 , SISGAN and TAGAN both fail to achieve an effective manipulation, while our model modifies the green grass to dry grass and also maps the cow into a sheep.

Note that as birds can have many detailed descriptions (e.g., colour for different parts), we use a long sentence to manipulate them, while the text descriptions for COCO are more abstract and focus mainly on categories, thus we use words to do manipulation for simplicity, which has the same effect as using long detailed text descriptions.

The effectiveness of the co-attention module. To verify the effectiveness of the co-attention module, we use the concatenation method to replace all co-attention modules, which concatenates hidden features h and regional features v along the channel direction, shown in Figs. 7 and 8 (d) . As we can see that our full model can synthesise an object having exactly the same shape, pose, and position as the one existing in the input image, and also generate new visual attributes aligned with the given text description on the synthetic image. In contrast, as shown in the last two columns of Figs. 7 and 8 (d) , with concatenation method, the model cannot reconstruct birds on the CUB bird dataset, and fails to do manipulation on the COCO dataset.

Text:

This bird is yellow with a yellow belly, and has a yellow beak.

A small bird with a red belly, a red crown, and black wings.

This bird has wings that are brown, and has an orange belly and an orange breast.

A bird that has a red beak, a grey head, and a grey belly. Also, to further validate the effectiveness of the co-attention module, we conduct an ablation study shown in Fig. 8 (c ). It can be seen that our model without co-attention module that we just concatenate text and image features before feeding into the main module, which is used in Dong et al. (2017) and Nam et al. (2018) , fails to produce reasonable images on both datasets. In contrast, our full model can better generate text-required attributes and also reconstruct text-unrelated contents shown in the last column. Table 1 also verifies the effectiveness of our co-attention module, as the values of IS and MP increase significantly when we implement the co-attention module.

Original:

The effectiveness of the detail correction module and main module. As shown in Fig. 8 (f), our model without detail correction module may miss some visual attributes (e.g., the bird missing the tail at row 2, the zebra missing the mouth at row 3), or generate new contents (e.g., new background at row 1, different appearance of bus at row 4), which indicates that the detail correction module can correct inappropriate attributes and reconstruct the text-unrelated contents. Fig. 8 (e) shows that without the main module, our model fails to do image manipulation on both datasets, which just achieves an identity mapping. This is mainly because the model cannot precisely correlate words with corresponding visual attributes, which mostly has been done in the main module. This bird has a light grey belly, dark grey wings and head with a red beak.

This bird has a yellow crown, blue wings and a yellow belly.

Zebra, green grass.

Yellow, green, bus. 

5. CONCLUSION:

We have proposed a novel generative adversarial network for visual attributes manipulation, called ManiGAN, which can semantically manipulate the input images using natural language descriptions. Two novel components are proposed in our model: (1) the co-attention module enables cooperation between hidden features and image features where both features can collaborate to reconstruct the input image and also keep the synthetic result semantically aligned with the given text description, and (2) the detail correction module can rectify mismatched visual attributes of the synthetic result, and also reconstruct text-unrelated contents existing in the input image. Extensive experimental results demonstrate the superiority of our proposed method, in terms of both the effectiveness of image manipulation and the capability of generating high-quality results. 

A ARCHITECTURE DETAILS:

We adopt the ControlGAN (Li et al., 2019) as the basic framework and replace batch normalisation with instance normalisation (Ulyanov et al., 2016) everywhere in the generator network except in the first stage. Basically, the co-attention module can be inserted anywhere in the generator, but we experimentally find that it is best to incorporate the module before upsampling blocks and image generation networks; see Fig. 9 .

B DATASETS:

Our method is evaluated on the CUB birds (Wah et al., 2011) 

C TRAINING DETAILS:

In our setting, we train the detail correction module (DCM) separately from the main module. Once the main module has converged, we train the DCM subsequently and set the main module as the eval mode. There are three stages in the main module, and each stage contains a generator and a discriminator. We train three stages at the same time, and three different-scale images 64×64, 128× 128, 256 × 256 are generated progressively.

The main module is trained for 600 epochs on the CUB dataset and 120 epochs on the COCO dataset using the Adam optimiser (Kingma & Ba, 2014) with the learning rate 0.0002, and β 1 = 0.5, β 2 = 0.999. We do not use any learning rate decay, but for visualising generator output at any given point during the training, we use an exponential running average for the weights of the generator with decay 0.999.

As for the DCM, there is a trade-off between generation of text-related attributes and the reconstruction of text-unrelated contents. Based on the manipulative precision (MP) values (see Fig. 4 ), we find that training 100 epochs for the CUB, and 12 epochs for the COCO to achieve an appropriate balance between generation and reconstruction. The other training setting are the same as in the main module. The hyperparameters λ 1 , λ 2 , λ 3 , and λ 4 are set to 1, 5, 0.5, and 1 for the CUB dataset, and 15, 5, 0.5, and 1 for COCO, respectively.

D ARCHITECTURE OF THE DETAIL CORRECTION MODULE:

First, the visual features v are converted into the same size as the hidden features h last via a convolutional layer F , denoted ṽ = F v , where ṽ ∈ R 128×H ×D . Then, we adopt the spatial attention and channel-wise attention introduced in (Li et al., 2019) to generate spatial attentive word-context features s ∈ R C ×H ×D and channel-wise attentive word-context features c ∈ R C ×H ×D , and concatenate these two features with the hidden features h last along the channel direction to generate new hidden features a ∈ R (3 * C )×H ×D . Next, to incorporate the visual features ṽ , we adopt the co-attention module here, donated ã = a W (ṽ ) + b (ṽ ), where W and b are learned weights and bias dependent on visual features ṽ . Then, the transformed features ã are fed into a series of residual blocks followed by a convolutional layer to generate hidden features e. Before feeding e into a network to generate the output image, we apply the co-attention module on the e again to further strengthen the visual information; see Fig. 2 (b ).

E TREND OF MANIPULATION RESULTS:

We also track the trend of manipulation results over epoch increases, as shown in Fig. 10 . The image is smoothly modified to achieve the best balance between the generation of new visual attributes (e.g., dirt background) and the reconstruction of text-unrelated contents (e.g., the appearance of zebras). However, when the epoch goes larger, the generated visual attributes (e.g., dirt background) aligned with the given text description are erased, and the synthetic image becomes more and more similar to the input image. This verifies the existence of the trade-off between the generation of new visual attributes required in the given text description and the reconstruction of contents existing in the input image. 

F ADDITIONAL RESULTS:

We show additional comparison results between our ManiGAN, SISGAN (Dong et al., 2017) , and TAGAN (Nam et al., 2018) on the CUB (Wah et al., 2011) and COCO (Lin et al., 2014) datasets.

This bird is blue and grey with a red belly.

This bird has wings that are grey and yellow with a yellow belly.

This bird is black in colour, with a red crown and a red beak.

This green bird has a black crown and a green belly.

A bird with brown wings and a yellow body, with a yellow head.

A white bird with grey wings and a red bill, with a white belly.

Original SISGAN TAGAN Ours A small blue bird with an orange crown, with a grey belly.

This bird has a red head, black eye rings, and a yellow belly.

This bird is mostly red with a black beak, and a black tail.

This tiny bird is blue and has a red bill and a red belly.

This bird has a white head, a yellow bill, and a yellow belly.

A white bird with red throat, black eye rings, and grey wings.

Given Text Original SISGAN TAGAN Ours

Figure 12 : Additional results between ManiGAN, SISGAN, and TAGAN on the CUB bird dataset.

:



Figures and Tables:

Figure 2: The architecture of the co-attention module and the generator used in the detail correction module. In (b), CoA denotes the co-attention module.

Figure 3: Ablation studies of the learned W and b. The texts on the top are the given descriptions containing desired visual attributes, and the last three columns are the channel feature maps of W (v).

Figure 4: Text-image similarity, L 1 pixel difference, and manipulative precision values at different epochs on the CUB (top) and COCO (bottom) datasets. We suggest to stop training the DCM module when the model gets the highest MP values shown in the last column.

Figure 5: Qualitative comparison of three methods on the CUB birds dataset.

Figure 7: Analysis of the co-attention module. "Matched" represents the texts matching original images. "Given" represents the texts provided by users. "Concat." denotes that instead of using co-attention, hidden features are concatenated with image features along the channel direction.

Figure 8: Ablation studies. a: given text describing the desired visual attributes; b: input image; c: removing the co-attention module and only concatenating image features and text features before feeding into the main module; d: using concatenation method to replace all co-attention modules; e: removing the main module and just training the DCM only; f: removing the DCM and just training the main module only; g: our full model.

Figure 9: architecture of the ManiGAN. The red dashed box indicates detail correction module, the CoA denotes the co-attention module.

Figure 10: Trend of the manipulation results over epoch increases on the COCO dataset.

Figure 11: Additional results between ManiGAN, SISGAN, and TAGAN on the CUB bird dataset.

Quantitative comparison: inception score (IS), text-image similarity (sim), L 1 pixel difference (diff), and manipulative precision (MP) of state-of-the-art approaches and ManiGAN on the CUB and COCO datasets. "w/o CoA" denotes without co-attention module. "w/ Concat." denotes using concatenation method to combine hidden and image features. "w/o main" denotes without main module. "w/o DCM" denotes without detail correction module. For IS, similarity, and MP, higher is better; for pixel difference, lower is better.

Table content: <table><tr><td/><td/><td/><td colspan="2">CUB</td><td/><td/><td/><td colspan="2">COCO</td></tr><tr><td/><td>Method</td><td>IS</td><td>sim</td><td>diff</td><td>MP</td><td>IS</td><td colspan="2">sim</td><td>diff</td><td>MP</td></tr><tr><td/><td>SISGAN</td><td colspan="8">2.24 .045 .508 .022 3.44 .077 .442 .042</td></tr><tr><td/><td>TAGAN</td><td colspan="8">3.32 .048 .267 .035 3.28 .089 .545 .040</td></tr><tr><td/><td>Ours w/o CoA</td><td colspan="8">4.01 .138 .491 .070 5.26 .121 .537 .056</td></tr><tr><td/><td colspan="9">Ours w/ Concat. 3.81 .135 .512 .065 13.48 .085 .532 .039</td></tr><tr><td/><td>Ours w/o main</td><td colspan="8">8.48 .084 .235 .064 17.59 .080 .169 .066</td></tr><tr><td/><td>Ours w/o DCM</td><td colspan="8">3.84 .123 .447 .068 6.99 .138 .517 .066</td></tr><tr><td/><td>Ours</td><td colspan="8">8.47 .101 .281 .072 14.96 .087 .216 .068</td></tr><tr><td/><td>0.120</td><td/><td>0.500</td><td/><td/><td/><td/><td>0.080</td></tr><tr><td/><td>0.090</td><td/><td>0.375</td><td/><td/><td/><td/><td>0.073</td></tr><tr><td>sim</td><td>0.060</td><td>diff</td><td>0.250</td><td/><td/><td/><td>MP</td><td>0.065</td></tr><tr><td/><td>0.030</td><td/><td>0.125</td><td/><td/><td/><td/><td>0.058</td></tr><tr><td/><td>0.000</td><td/><td>0.000</td><td/><td/><td/><td/><td>0.050</td></tr><tr><td/><td>20 80 140 200 260</td><td/><td/><td colspan="3">20 80 140 200 260</td><td/><td/><td>20 80 140 200 260</td></tr><tr><td/><td>epochs</td><td/><td/><td/><td>epochs</td><td/><td/><td/><td>epochs</td></tr><tr><td/><td>0.130</td><td/><td>0.700</td><td/><td/><td/><td/><td>0.070</td></tr><tr><td/><td>0.098</td><td/><td>0.525</td><td/><td/><td/><td/><td>0.053</td></tr><tr><td>sim</td><td>0.065</td><td>diff</td><td>0.350</td><td/><td/><td/><td>MP</td><td>0.035</td></tr><tr><td/><td>0.033</td><td/><td>0.175</td><td/><td/><td/><td/><td>0.018</td></tr><tr><td/><td>0.000</td><td/><td>0.000</td><td/><td/><td/><td/><td>0.000</td></tr><tr><td/><td colspan="2">2 4 6 8 10 12 14 16 18 20</td><td/><td colspan="3">2 4 6 8 10 12 14 16 18 20</td><td/><td/><td>2 4 6 8 10 12 14 16 18 20</td></tr><tr><td/><td>epochs</td><td/><td/><td/><td>epochs</td><td/><td/><td/><td>epochs</td></tr></table>

and the MS COCO(Lin et al., 2014) datasets. The CUB dataset contains 8,855 training images and 2,933 test images, and each image has 10 corresponding text descriptions. As for the COCO dataset, it contains 82,783 training images and 40,504 validation images, and each image has 5 corresponding text descriptions. We preprocess this two datasets based on the methods introduced inZhang et al. (2017).

Table content: <table/>

Peer review (iclr2020):
The paper proposed a very effective approach to modify an input image according to natural language description. The method is modified based on ControlGAN with two novel modules. The 1st is Co-Attention Module (CoA) which is well-motivated and the ablation study clearly shows the effect of this module. The second is the Detail Correction Module (DCM), which is only briefly described in the main paper with the most part described in the supplementary material. The effect of DCM is briefly shown in Fig. 8. The qualitative and quantitative results a re very impressive compared to SISGAN and TAGAN. In the ablation study, a few examples also show the ControlGAN backbone architecture without either CoA or DCM generates results worse than the proposed method. However, I wonder what*s the *quantitative* comparison with ControlGAN backbone architecture without either CoA or DCM, or even both modules. In Fig.7, why oncat., Matched and Concat., Given generate structurally very different images? Is this typically happening? In Fig. 8, without CoA means CoA is removed from both the main architecture and DCM? In yes, what is used instead of CoA? In general, the paper is tackling a hard and interesting task and the results are very impressive. My only concern is how *quantitative* improvement is from CoA or DCM as compared to the backbone ControlGAN. Hence, I recommend for weak accept.