Optimizing Ranking Measures for Compact Binary Code Learning

Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest---multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.Comment: Appearing in Proc. European Conference on Computer Vision 201

SIFT-ME: A New Feature for Human Activity Recognition

Action representation for robust human activity recognition is still a challenging problem. This thesis proposed a new feature for human activity recognition named SIFT-Motion Estimation (SIFT-ME). SIFT-ME is derived from SIFT correspondences in a sequence of video frames and adds tracking information to describe human body motion. This feature is an extension of SIFT and is used to represent both translation and rotation in plane rotation for the key features. Compare with other features, SIFT-ME is new as it uses rotation of key features to describe action and it robust to the environment changes. Because SIFT-ME is derived from SIFT correspondences, it is invariant to noise, illumination, and small view angle change. It is also invariant to horizontal motion direction due to the embedded tracking information. For action recognition, we use Gaussian Mixture Model to learn motion patterns of several human actions (e.g., walking, running, turning, etc) described by SIFT-ME features. Then, we utilize the maximum log-likelihood criterion to classify actions. As a result, an average recognition rate of 96.6% was achieved using a dataset of 261 videos comprised of six actions performed by seven subjects. Multiple comparisons with existing implementations including optical flow, 2D SIFT and 3D SIFT were performed. The SIFT-ME approach outperforms the other approaches which demonstrate that SIFT-ME is a robust method for human activity recognition

Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation

Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering the smoothness-based methods have achieved promising progress, in this paper, we advocate applying the consistency constraint under various perturbations to effectively regularize unlabeled 3D points. Specifically, we propose a novel DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate that our model can effectively leverage the unlabeled 3D points and achieve significant performance gains on both datasets, setting new state-of-the-art performance for weakly supervised point cloud segmentation.Comment: ECCV 202

Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations. Our proposed method combines bottom-up and top-down cues for novel class detection. Within our approach, we introduce a bottom-up mechanism based on the well-trained fully supervised object detector (i.e. Faster RCNN) as an object region estimator for web images by recognizing the common objectiveness shared by base and novel classes. With the estimated regions on the web images, we then utilize the top-down attention cues as the guidance for region classification. Furthermore, we propose a residual feature refinement (RFR) block to tackle the domain mismatch between web domain and the target domain. We demonstrate our proposed method on PASCAL VOC dataset with three different novel/base splits. Without any target-domain novel-class images and annotations, our proposed webly supervised object detection model is able to achieve promising performance for novel classes. Moreover, we also conduct transfer learning experiments on large scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance

国际贸易中的价值链与国家优势

众所周知，国际贸易能带来生产力的高增长率，促进一国经济发展，因为贸易被视为技术知识传播的一个重要渠道。更重要的是贸易如何影响新技术传播这个问题。近年来，人们做了很多关注中间产品的研究。译者单位：厦门大学经济学院国际经济与贸易系(361005