Improving Neural Relation Extraction with Positive and Unlabeled Learning

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforcement learning to decide whether a sentence is positive to a given relation, and then positive and unlabeled bags are constructed. In contrast to most previous studies, which mainly use selected positive instances only, we make full use of unlabeled instances and propose two new representations for positive and unlabeled bags. These two representations are then combined in an appropriate way to make bag-level prediction. Experimental results on a widely used real-world dataset demonstrate that this new approach indeed achieves significant and consistent improvements as compared to several competitive baselines.Comment: 8 pages, AAAI-202

Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure

USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation

Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification network is the de facto paradigm for seed area generation, but CAMs generated from Convolutional Neural Networks (CNNs) and Transformers are prone to be under- and over-activated, respectively, which makes the strategies to refine CAMs for CNNs usually inappropriate for Transformers, and vice versa. In this paper, we propose a Unified optimization paradigm for Seed Area GEneration (USAGE) for both types of networks, in which the objective function to be optimized consists of two terms: One is a generation loss, which controls the shape of seed areas by a temperature parameter following a deterministic principle for different types of networks; The other is a regularization loss, which ensures the consistency between the seed areas that are generated by self-adaptive network adjustment from different views, to overturn false activation in seed areas. Experimental results show that USAGE consistently improves seed area generation for both CNNs and Transformers by large margins, e.g., outperforming state-of-the-art methods by a mIoU of 4.1% on PASCAL VOC. Moreover, based on the USAGE-generated seed areas on Transformers, we achieve state-of-the-art WSSS results on both PASCAL VOC and MS COCO

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

Mechanical And Thermal Conductive Properties Of Fiber-Reinforced Silica-Alumina Aerogels

We report the formation of Al2O3-SiO2 fiber-reinforced Al2O3-SiO2 aerogels with the content of fibers in the range from 40 wt% to 55 wt% by sol-gel reaction, followed by supercritical drying. The structure and physical properties of fiber-reinforced Al2O3-SiO2 aerogels are studied. We find that the fiber-reinforced Al2O3-SiO2 aerogels can be resistant to the temperature of 1200°C. The integration of fibers significantly improves the mechanical properties of Al2O3-SiO2 aerogels. We find that the bending strength of fiber-reinforced Al2O3-SiO2 aerogels increases 0.431 MPa to 0.755 MPa and the elastic modulus increases from 0.679 MPa to 1.153 MPa, when the content of fibers increases from 40 wt% to 50 wt%. The thermal conductivity of the fiber-reinforced Al2O3-SiO2 aerogels is in the range from 0.0403 W/mK to 0.0545 W/mK, depending on the content of fibers

Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System

This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multi-domain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection prob-lem is treated as sequential planning in-stead of classification, such that confir-mation and clarification interaction mech-anisms are supported. In addition, it is shown that by using a model parameter ty-ing trick, the extensibility of the system can be preserved, where dialogue com-ponents in new domains can be easily plugged in, without re-training the domain selection policy. The experimental results based on human subjects suggest that the proposed model marginally outperforms a non-trivial baseline.

Preparation and Applications of the Cellulose Nanocrystal

Cellulose widely existed in plants and bacteria, which takes important effect on the synthesis of macromolecule polymer material. Because of its great material properties, the cellulose nanocrystal (CNC) showed its necessary prospect in various industrial applications. As a renewable future material, the preparation methods of the CNC were reviewed in this paper. Meanwhile, the important applications of CNC in the field of composites, barrier film, electronics, and energy consumption were also mentioned with brief introductions. The summarized preparations and considerable applications provided operable ideas and methods for the future high-end and eco-friendly functional composites. Suggestions for potential applications were also discussed