2015 (a) (b) (c) (d) Figure 3: Four sample alignments found by RNNsearch-50. The x-axis and y-axis of each plot correspond to the words in the source sentence (English) and the generated translation (French), respectively. Each pixel shows the weight ↵ij of the annotation of the j-th source word for the i-th target word (see Eq. (6)), in grayscale (0: black, 1: white). (a) an arbitrary sentence. (b–d) three randomly selected samples among the sentences without any unknown words and of length between 10 and 20 words from the test set. One of the motivations behind the proposed approach was the use of a fixed-length context vector in the basic encoder–decoder approach. We conjectured that this limitation may make the basic encoder–decoder approach to underperform with long sentences. In Fig. 2, we see that the perfor- mance of RNNencdec dramatically drops as the length of the sentences increases. On the other hand, both RNNsearch-30 and RNNsearch-50 are more robust to the length of the sentences. RNNsearch- 50, especially, shows no performance deterioration even with sentences of length 50 or more. This superiority of the proposed model over the basic encoder–decoder is further confirmed by the fact that the RNNsearch-30 even outperforms RNNencdec-50 (see Table 1). 6 /FVSBM.BDIJOF5SBOTMBUJPOCZ+PJOUMZ-FBSOJOH UP"MJHOBOE5SBOTMBUF &⒎FDUJWF"QQSPBDIFTUP "UUFOUJPOCBTFE/FVSBM.BDIJOF5SBOTMBUJPO yt ˜ ht ct at ht pt ¯ hs Attention Layer Context vector Local weights Aligned position Figure 3: Local attention model – the model first predicts a single aligned position pt for the current target word. A window centered around the source position pt is then used to compute a context vec- tor ct , a weighted average of the source hidden states in the window. The weights at are inferred refers to the global attention approach in which weights are placed “softly” over all patches in the source image. The hard attention, on the other hand, selects one patch of the image to attend to at a time. While less expensive at inference time, the hard attention model is non-differentiable and re- quires more complicated techniques such as vari- ance reduction or reinforcement learning to train. Our local attention mechanism selectively fo- cuses on a small window of context and is differ- entiable. This approach has an advantage of avoid- ing the expensive computation incurred in the soft attention and at the same time, is easier to train than the hard attention approach. In concrete de- tails, the model first generates an aligned position pt for each target word at time t. The context vec- tor ct is then derived as a weighted average over the set of source hidden states within the window [pt −D, pt +D]; D is empirically selected.8 Unlike Figure 3. Examples of attending to the correct object (white indicates the attended regions, underlines indicated the corre two variants: a “hard” attention mechanism and a “soft” attention mechanism. We also show how one advantage of including attention is the ability to visualize what the model “sees”. Encouraged by recent advances in caption genera- tion and inspired by recent success in employing attention in machine translation (Bahdanau et al., 2014) and object recognition (Ba et al., 2014; Mnih et al., 2014), we investi- gate models that can attend to salient part of an image while generating its caption. 2. Related Work In this section we provide relevant backgroun work on image caption generation and attent several methods have been proposed for gen descriptions. Many of these methods are b rent neural networks and inspired by the suc sequence to sequence training with neural ne chine translation (Cho et al., 2014; Bahdana 4IPX "UUFOEBOE5FMM /FVSBM*NBHF$BQUJPO(FOFSBUJPOXJUI7JTVBM"UUFOUJPO 3FTJEVBM"UUFOUJPO/FUXPSLGPS*NBHF$MBTTJpDBUJPO down sample down sample up sample up sample convolution receptive field Soft Mask Branch Trunk Branch 1 + # $ % '($) * + + , + Figure 3: The receptive field comparison between mask branch and trunk branch. range to [0, 1] after two consecutive 1 ⇥ 1 convolution lay- ers. We also added skip connections between bottom-up and top-down parts to capture information from different scales. The full module is illustrated in Fig.2. The bottom-up top-down structure has been applied to image segmentation and human pose estimation. However, the difference between our structure and the previous one lies in its intention. Our mask branch aims at improving Activation f1( f2( f3( Table 1: Th network with Layer Conv1 Max pool Residual U Attention M Residual U Attention M Residual U Attention M Residual U Average po FC,Softm pa /-1 /-1 $7 $7 จͷओுͰɻɻɻ Stacked Hourglass Networks for Human Pose Estimation Alejandro Newell, Kaiyu Yang, and Jia Deng University of Michigan, Ann Arbor {alnewell,yangky,jiadeng}@umich.edu Abstract. This work introduces a novel convolutional network archi- tecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial re- lationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods. Keywords: Human Pose Estimation Fig. 1. Our network for pose estimation consists of multiple stacked hourglass modules which allow for repeated bottom-up, top-down inference. 1 Introduction A key step toward understanding people in images and video is accurate pose estimation. Given a single RGB image, we wish to determine the precise pixel location of important keypoints of the body. Achieving an understanding of a person’s posture and limb articulation is useful for higher level tasks like ac- tion recognition, and also serves as a fundamental tool in fields such as human- computer interaction and animation. arXiv:1603.06937v2 [cs.CV] 26 Jul 2016 4UBDLFE)PVSHMBTT/FUXPSLT GPS)VNBO1PTF&TUJNBUJPO )JHIXBZ/FUXPSLT 1.1. Notation We use boldface letters for vectors and matrices, and ital- icized capital letters to denote transformation functions. 0 and 1 denote vectors of zeros and ones respectively, and I denotes an identity matrix. The function (x) is defined as (x) = 1 1+e x , x 2 R. 2. Highway Networks A plain feedforward neural network typically consists of L layers where the lth layer (l 2 {1, 2, ..., L}) applies a non- linear transform H (parameterized by W H,l ) on its input x l to produce its output y l . Thus, x 1 is the input to the network and y L is the network’s output. Omitting the layer index and biases for clarity, y = H(x, W H ). (1) H is usually an affine transform followed by a non-linear activation function, but in general it may take other forms. For a highway network, we additionally define two non- linear transforms T(x, W T ) and C(x, W C ) such that y = H(x, W H )· T(x, W T ) + x · C(x, W C ). (2) We refer to T as the transform gate and C as the carry gate, since they express how much of the output is produced by transforming the input and carrying it, respectively. For simplicity, in this paper we set C = 1 T, giving y = H(x, W H )· T(x, W T ) + x · (1 T(x, W T )). (3) The dimensionality of x, y, H(x, W H ) and T(x, W T ) must be the same for Equation (3) to be valid. Note that ple computing units such that the i unit computes yi = Hi (x), a highway network consists of multiple blocks such that the ith block computes a block state Hi (x) and trans- form gate output Ti (x). Finally, it produces the block out- put yi = Hi (x) ⇤ Ti (x) + xi ⇤ (1 Ti (x)), which is con- nected to the next layer. 2.1. Constructing Highway Networks As mentioned earlier, Equation (3) requires that the dimen- sionality of x, y, H(x, W H ) and T(x, W T ) be the same. In cases when it is desirable to change the size of the rep- resentation, one can replace x with ˆ x obtained by suitably sub-sampling or zero-padding x. Another alternative is to use a plain layer (without highways) to change dimension- ality and then continue with stacking highway layers. This is the alternative we use in this study. Convolutional highway layers are constructed similar to fully connected layers. Weight-sharing and local receptive fields are utilized for both H and T transforms. We use zero-padding to ensure that the block state and transform gate feature maps are the same size as the input. 2.2. Training Deep Highway Networks For plain deep networks, training with SGD stalls at the beginning unless a specific weight initialization scheme is used such that the variance of the signals during forward and backward propagation is preserved initially (Glorot & Bengio, 2010; He et al., 2015). This initialization depends on the exact functional form of H. For highway layers, we use the transform gate defined as T(x) = (W T T x+b T ), where W T is the weight matrix and b T the bias vector for the transform gates. This sug- gests a simple initialization scheme which is independent of the nature of H: bT can be initialized with a negative value (e.g. -1, -3 etc.) such that the network is initially biased towards carry behavior. This scheme is strongly in- $7 $7
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