Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

Relational Multi-Task Learning: Modeling Relations between Data and Tasks

A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point's labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point's labels from other auxiliary tasks, and this way improves performance on the new task. Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task. We develop MetaLink, where our key innovation is to build a knowledge graph that connects data points and tasks and thus allows us to leverage labels from auxiliary tasks. The knowledge graph consists of two types of nodes: (1) data nodes, where node features are data embeddings computed by the neural network, and (2) task nodes, with the last layer's weights for each task as node features. The edges in this knowledge graph capture data-task relationships, and the edge label captures the label of a data point on a particular task. Under MetaLink, we reformulate the new task as a link label prediction problem between a data node and a task node. The MetaLink framework provides flexibility to model knowledge transfer from auxiliary task labels to the task of interest. We evaluate MetaLink on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that MetaLink can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC.Comment: ICLR 2022 Spotligh

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is determined by dice rolling. Visit our website http://zhuhao.me/fewre

A Nomographic Methodology for Use in Performance Trade-Off Studies of Parabolic Dish Solar Power Modules

A simple graphical method was developed to undertake technical design trade-off studies for individual parabolic dish models comprising a two-axis tracking parabolic dish with a cavity receiver and power conversion assembly at the focal point. The results of these technical studies are then used in performing the techno-economic analyses required for determining appropriate subsystem sizing. Selected graphs that characterize the performance of subsystems within the module were arranged in the form of a nomogram that would enable an investigator to carry out several design trade-off studies. Key performance parameters encompassed in the nomogram include receiver losses, intercept factor, engine rating, and engine efficiency. Design and operation parameters such as concentrator size, receiver type (open or windowed aperture), receiver aperture size, operating temperature of the receiver and engine, engine partial load characteristics, concentrator slope error, and the type of reflector surface, are also included in the graphical solution. Cost considerations are not included

Modeling Relations Between Event-Related Potential Factors and Broader Versus Narrower Dimensions of Externalizing Psychopathology

The organization of the Hierarchical Taxonomy of Psychopathology (HiTOP) model provides unique opportunities to evaluate whether neural risk measures operate as indicators of broader latent liabilities (e.g., externalizing proneness) or narrower expressions (e.g., antisociality and alcohol abuse). Following this approach, the current study recruited a sample of 182 participants (54% female) who completed measures of externalizing psychopathology (also internalizing) and associated traits. Participants also completed three tasks (Flanker-No Threat, Flanker-Threat, and Go/No-Go tasks) with event-related potential (ERP) measurement. Three variants of two research domain criteria (RDoC)-based neurophysiological indicators—P3 and error-related negativity (ERN)—were extracted from these tasks and used to model two latent ERP factors. Scores on these two ERP factors independently predicted externalizing factor scores when accounting for their covariance with sex—suggesting distinct neural processes contributing to the broad externalizing factor. No predictive relation with the broad internalizing factor was found for either ERP factor. Analyses at the finer-grained level revealed no unique predictive relations of either ERP factor with any specific externalizing symptom variable when accounting for the broad externalizing factor, indicating that ERN and P3 index general liability for problems in this spectrum. Overall, this study provides new insights about neural processes in externalizing psychopathology at broader and narrower levels of the HiTOP hierarchy

Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval

Due to the proliferation of large-scale remote-sensing (RS) archives with multiple annotations, multilabel RS scene classification and retrieval are becoming increasingly popular. Although some recent deep learning-based methods are able to achieve promising results in this context, the lack of research on how to learn embedding spaces under the multilabel assumption often makes these models unable to preserve complex semantic relations pervading aerial scenes, which is an important limitation in RS applications. To fill this gap, we propose a new graph relation network (GRN) for multilabel RS scene categorization. Our GRN is able to model the relations between samples (or scenes) by making use of a graph structure which is fed into network learning. For this purpose, we define a new loss function called scalable neighbor discriminative loss with binary cross entropy (SNDL-BCE) that is able to embed the graph structures through the networks more effectively. The proposed approach can guide deep learning techniques (such as convolutional neural networks) to a more discriminative metric space, where semantically similar RS scenes are closely embedded and dissimilar images are separated from a novel multilabel viewpoint. To achieve this goal, our GRN jointly maximizes a weighted leave-one-out K-nearest neighbors (KNN) score in the training set, where the weight matrix describes the contributions of the nearest neighbors associated with each RS image on its class decision, and the likelihood of the class discrimination in the multilabel scenario. An extensive experimental comparison, conducted on three multilabel RS scene data archives, validates the effectiveness of the proposed GRN in terms of KNN classification and image retrieval. The codes of this article will be made publicly available for reproducible research in the community