Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training-transformed images are included for training by assuming the same class labels as their original images. In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i.e., the attention region for a classification follows the same transform if the input image is spatially transformed. While the attention regions of CNN classifiers can be derived as an attention heatmap in middle layers of the network, we find that their consistency under many transforms are not preserved. To address this problem, we propose a two-branch network with an original image and its transformed image as inputs and introduce a new attention consistency loss that measures the attention heatmap consistency between two branches. This new loss is then combined with multi-label image classification loss for network training. Experiments on three datasets verify the superiority of the proposed network by achieving new state-of-theart classification performance.

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Abstract-In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a SmallObject-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a realworld conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.

MBS301, a glyco-engineered bispecific anti-human epidermal growth factor receptor 2 (HER2) antibody with a typical IgG1 monoclonal antibody structure, was developed through dual-cell expression and in vitro assembling process. MBS301 consists of two half antibodies engineered from trastuzumab and pertuzumab, respectively. Integrity and purity profiles of MB301 indicated that the heterodimerization of the two half antibodies was successful. The high and similar melting temperatures (Tm1,72.0°C and Tm2, 84.8°C) of MBS301 compared with those of its parental monoclonal antibodies trastuzumab and pertuzumab (in-house made T-mab and P-mab, respectively) revealed its structural compactness. With computer-modeling experiments and Biacore binding and competition kinetics studies, the binding stoichiometry between MBS301 and HER2-ECD was determined to be 1:1 and the two arms of MBS301 were shown to bind to domains II and IV of HER2-ECD antigen simultaneously. MBS301 displayed synergistic bioactivities as the combination of T-mab and P-mab in vitro in multiple cancer cell lines and in vivo in xenograft mouse model studies, and showed more effective activity than T-mab or P-mab used individually. Moreover, fucose-knockout dramatically increased MBS301’s binding affinity to low affinity FcγRIIIa allotype 158F (KD = 2.35 × 10−7M) to near the high affinity level of allotype V158 (KD = 1.17 × 10−7M). This resulted in far more effective ADCC activity of MBS301 than the combination of T-mab and P-mab in killing HER2-positive cancer cells. Hence, a novel fully afucosylated anti-HER2 bispecific antibody with improved antitumor activities was generated and shown to have the potential to be used for treating HER2-positive but trastuzumab-resistant solid tumors.

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We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.

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