2D Human Pose Estimation

Instance Normalization (also known as contrast normalization) is a normalization layer where: $$ y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}}, \quad \mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm}, \quad \sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H (x_{tilm} - \mu_{ti})^2. $$ This prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.

Cycle Consistency Loss is a type of loss used for generative adversarial networks that performs unpaired image-to-image translation. It was introduced with the CycleGAN architecture. For two domains $X$ and $Y$, we want to learn a mapping $G : X \rightarrow Y$ and $F: Y \rightarrow X$. We want to enforce the intuition that these mappings should be reverses of each other and that both mappings should be bijections. Cycle Consistency Loss encourages $F\left(G\left(x\right)\right) \approx x$ and $G\left(F\left(y\right)\right) \approx y$. It reduces the space of possible mapping functions by enforcing forward and backwards consistency: $$ \mathcal{L}\_{cyc}\left(G, F\right) = \mathbb{E}\_{x \sim p\_{data}\left(x\right)}\left[||F\left(G\left(x\right)\right) - x||\_{1}\right] + \mathbb{E}\_{y \sim p\_{data}\left(y\right)}\left[||G\left(F\left(y\right)\right) - y||\_{1}\right] $$

Transformer-XL (meaning extra long) is a Transformer architecture that introduces the notion of recurrence to the deep self-attention network. Instead of computing the hidden states from scratch for each new segment, Transformer-XL reuses the hidden states obtained in previous segments. The reused hidden states serve as memory for the current segment, which builds up a recurrent connection between the segments. As a result, modeling very long-term dependency becomes possible because information can be propagated through the recurrent connections. As an additional contribution, the Transformer-XL uses a new relative positional encoding formulation that generalizes to attention lengths longer than the one observed during training.

Hyperboloid Embeddings (HypE) is a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of its entities and relations as hyperboloids in a Poincaré ball. HypE models the positive first-order queries as geometrical translation (t), intersection ($\cap$), and union ($\cup$). For the problem of KG reasoning in real-world datasets, the proposed HypE model significantly outperforms the state-of-the art results. HypE is also applied to an anomaly detection task on a popular e-commerce website product taxonomy as well as hierarchically organized web articles and demonstrate significant performance improvements compared to existing baseline methods. Finally, HypE embeddings can also be visualized in a Poincaré ball to clearly interpret and comprehend the representation space.

Modules used in GAN's style transfer.

Collaborative Distillation is a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer to reduce the number of convolutional filters. The main idea is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for style transfer models.

Revision Network is a style transfer module that aims to revise the rough stylized image via generating residual details image $r_{c s}$, while the final stylized image is generated by combining $r\_{c s}$ and rough stylized image $\bar{x}\_{c s}$. This procedure ensures that the distribution of global style pattern in $\bar{x}\_{c s}$ is properly kept. Meanwhile, learning to revise local style patterns with residual details image is easier for the Revision Network. As shown in the Figure, the Revision Network is designed as a simple yet effective encoder-decoder architecture, with only one down-sampling and one up-sampling layer. Further, a patch discriminator is used to help Revision Network to capture fine patch textures under adversarial learning setting. The patch discriminator $D$ is defined following SinGAN, where $D$ owns 5 convolution layers and 32 hidden channels. A relatively shallow $D$ is chosen to (1) avoid overfitting since we only have one style image and (2) control the receptive field to ensure D can only capture local patterns.

Represent and associate with a composite of primitive fields.

A Style-based Recalibration Module (SRM) is a module for convolutional neural networks that adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight via channel-independent style integration. By incorporating the relative importance of individual styles into feature maps, SRM is aimed at enhancing the representational ability of a CNN. The overall structure of SRM is illustrated in the Figure to the right. It consists of two main components: style pooling and style integration. The style pooling operator extracts style features from each channel by summarizing feature responses across spatial dimensions. It is followed by the style integration operator, which produces example-specific style weights by utilizing the style features via channel-wise operation. The style weights finally recalibrate the feature maps to either emphasize or suppress their information.

ZoomNet is a 2D human whole-body pose estimation technique. It aims to localize dense landmarks on the entire human body including face, hands, body, and feet. ZoomNet follows the top-down paradigm. Given a human bounding box of each person, ZoomNet first localizes the easy-to-detect body keypoints and estimates the rough position of hands and face. Then it zooms in to focus on the hand/face areas and predicts keypoints using features with higher resolution for accurate localization. Unlike previous approaches which usually assemble multiple networks, ZoomNet has a single network that is end-to-end trainable. It unifies five network heads including the human body pose estimator, hand and face detectors, and hand and face pose estimators into a single network with shared low-level features.

AccoMontage is a model for accompaniment arrangement, a type of music generation task involving intertwined constraints of melody, harmony, texture, and music structure. AccoMontage generates piano accompaniments for folk/pop songs based on a lead sheet (i.e. a melody with chord progression). It first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure deep learning approaches, AccoMontage uses a hybrid pathway, in which rule-based optimization and deep learning are both leveraged.

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Drafting Network is a style transfer module designed to transfer global style patterns in low-resolution, since global patterns can be transferred easier in low resolution due to larger receptive field and less local details. To achieve single style transfer, earlier work trained an encoder-decoder module, where only the content image is used as input. To better combine the style feature and the content feature, the Drafting Network adopts the AdaIN module. The architecture of Drafting Network is shown in the Figure, which includes an encoder, several AdaIN modules and a decoder. (1) The encoder is a pre-trained VGG-19 network, which is fixed during training. Given $\bar{x}\_{c}$ and $\bar{x}\_{s}$, the VGG encoder extracts features in multiple granularity at 2_1, 3_1 and 4_1 layers. (2) Then, we apply feature modulation between the content and style feature using AdaIN modules after 2_1, 3_1 and 4_1 layers, respectively. (3) Finally, in each granularity of decoder, the corresponding feature from the AdaIN module is merged via a skip-connection. Here, skip-connections after AdaIN modules in both low and high levels are leveraged to help to reserve content structure, especially for low-resolution image.

LapStyle, or Laplacian Pyramid Network, is a feed-forward style transfer method. It uses a Drafting Network to transfer global style patterns in low-resolution, and adopts higher resolution Revision Networks to revise local styles in a pyramid manner according to outputs of multi-level Laplacian filtering of the content image. Higher resolution details can be generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Specifically, we first generate image pyramid $\left\(\bar{x}\_{c}, r\_{c}\right\)$ from content image $x\_{c}$ with the help of Laplacian filter. Rough low-resolution stylized image are then generated by the Drafting Network. Then the Revision Network generates stylized detail image in high resolution. Then the final stylized image is generated by aggregating the outputs pyramid. $L, C$ and $A$ in an image represent Laplacian, concatenate and aggregation operation separately.

Review-guided Answer Helpfulness Prediction (RAHP) is a textual inference model for identifying helpful answers in e-commerce. It not only considers the interactions between QA pairs, but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.

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Attribute2Font is a model that automatically creates fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, the model can generate glyph images in accordance with an arbitrary set of font attribute values. A unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. A semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts

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