pwc-archive/papers-with-abstracts huggingface dataset:
Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds
Authors: Tiancheng Zhi,  Bernardo R. Pires,  Martial Hebert,  Srinivasa G. Narasimhan
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation

Hundreds of materials, such as drugs, explosives, makeup, food additives, are in the form of powder. Recognizing such powders is important for security checks, criminal identification, drug control, and quality assessment. However, powder recognition has drawn little attention in the computer vision community. Powders are hard to distinguish: they are amorphous, appear matte, have little color or texture variation and blend with surfaces they are deposited on in complex ways. To address these challenges, we present the first comprehensive dataset and approach for powder recognition using multi-spectral imaging. By using Shortwave Infrared (SWIR) multi-spectral imaging together with visible light (RGB) and Near Infrared (NIR), powders can be discriminated with reasonable accuracy. We present a method to select discriminative spectral bands to significantly reduce acquisition time while improving recognition accuracy. We propose a blending model to synthesize images of powders of various thickness deposited on a wide range of surfaces. Incorporating band selection and image synthesis, we conduct fine-grained recognition of 100 powders on complex backgrounds, and achieve 60% 70% accuracy on recognition with known powder location, and over 40% mean IoU without known location.
Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling
Authors: Yuanman Li,  Jiantao Zhou,  Xianwei Zheng,  Jinyu Tian,  Yuan Yan Tang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Face Clustering

Most of the existing subspace clustering (SC) frameworks assume that the noise contaminating the data is generated by an independent and identically distributed (i.i.d.) source, where the Gaussianity is often imposed. Though these assumptions greatly simplify the underlying problems, they do not hold in many real-world applications. For instance, in face clustering, the noise is usually caused by random occlusions, local variations and unconstrained illuminations, which is essentially structural and hence satisfies neither the i.i.d. property nor the Gaussianity.  In this work, we propose an independent and piecewise identically distributed (i.p.i.d.) noise model, where the i.i.d. property only holds locally. We demonstrate that the i.p.i.d. model better characterizes the noise encountered in practical scenarios, and accommodates the traditional i.i.d. model as a special case. Assisted by this generalized noise model, we design an information theoretic learning (ITL) framework for robust SC through a novel minimum weighted error entropy (MWEE) criterion. Extensive experimental results show that our proposed SC scheme significantly outperforms the state-of-the-art competing algorithms.
What Correspondences Reveal About Unknown Camera and Motion Models?
Authors: Thomas Probst,  Ajad Chhatkuli,  Danda Pani Paudel,  Luc Van Gool
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vocal Bursts Type Prediction

In two-view geometry, camera models and motion types are used as key knowledge along with the image point correspondences in order to solve several key problems of 3D vision. Problems such as Structure-from-Motion (SfM) and camera self-calibration are tackled under the assumptions of a specific camera projection model and motion type. However, these key assumptions may not be always justified, i.e.., we may often know neither the camera model nor the motion type beforehand. In that context, one can extract only the point correspondences between images. From such correspondences, recovering two-view relationship --expressed by the unknown camera model and motion type-- remains to be an unsolved problem. In this paper, we tackle this problem in two steps. First, we propose a method that computes the correct two-view relationship in the presence of noise and outliers. Later, we study different possibilities to disambiguate the obtained relationships into camera model and motion type. By extensive experiments on both synthetic and real data, we verify our theory and assumptions in practical settings.
Incremental Object Learning From Contiguous Views
Authors: Stefan Stojanov,  Samarth Mishra,  Ngoc Anh Thai,  Nikhil Dhanda,  Ahmad Humayun,  Chen Yu,  Linda B. Smith,  James M. Rehg
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Incremental Learning, Object

In this work, we present CRIB (Continual Recognition Inspired by Babies), a synthetic incremental object learning environment that can produce data that models visual imagery produced by object exploration in early infancy. CRIB is coupled with a new 3D object dataset, Toys-200, that contains 200 unique toy-like object instances, and is also compatible with existing 3D datasets. Through extensive empirical evaluation of state-of-the-art incremental learning algorithms, we find the novel empirical result that repetition can significantly ameliorate the effects of catastrophic forgetting. Furthermore, we find that in certain cases repetition allows for performance approaching that of batch learning algorithms. Finally, we propose an unsupervised incremental learning task with intriguing baseline results.
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
Authors: Xiaoping Wu,  Chi Zhan,  Yu-Kun Lai,  Ming-Ming Cheng,  Jufeng Yang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Fine-Grained Image Classification, General Classification, object-detection, Object Detection

Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification  task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In  addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upperlevel category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of interand intra- class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/xpwu95/IP102.
Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence
Authors: Amir Zadeh,  Michael Chan,  Paul Pu Liang,  Edmund Tong,  Louis-Philippe Morency
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Visual Reasoning

As intelligent systems increasingly blend into our everyday life, artificial social intelligence becomes a prominent area of research. Intelligent systems must be socially intelligent in order to comprehend human intents and maintain a rich level of interaction with humans. Human language offers a unique unconstrained approach to probe through questions and reason through answers about social situations. This unconstrained approach extends previous attempts to model social intelligence through numeric supervision (e.g. sentiment and emotions labels). In this paper, we introduce Social-IQ, a unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers. Although humans can reason about social situations with very high accuracy (95.08%), existing state-of-the-art computational models struggle on this task. As a result, Social-IQ brings novel challenges that will spark future research in social intelligence modeling, visual reasoning, and multimodal question answering (QA).
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection
Authors: Kuang-Jui Hsu,  Yen-Yu Lin,  Yung-Yu Chuang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Co-Salient Object Detection, Saliency Detection, Segmentation

In this paper, we address a new task called instance co-segmentation. Given a set of images jointly covering object instances of a specific category, instance co-segmentation aims to identify all of these instances and segment each of them, i.e. generating one mask for each instance. This task is important since instance-level segmentation is preferable for humans and many vision applications. It is also challenging because no pixel-wise annotated training data are available and the number of instances in each image is unknown. We solve this task by dividing it into two sub-tasks, co-peak search and instance mask segmentation. In the former sub-task, we develop a CNN-based network to detect the co-peaks as well as co-saliency maps for a pair of images. A co-peak has two endpoints, one in each image, that are local maxima in the response maps and similar to each other. Thereby, the two endpoints are potentially covered by a pair of instances of the same category. In the latter subtask, we design a ranking function that takes the detected co-peaks and co-saliency maps as inputs and can select the object proposals to produce the final results. Our method for instance co-segmentation and its variant for object colocalization are evaluated on four datasets, and achieve favorable performance against the state-of-the-art methods. The source codes and the collected datasets are available at https://github.com/KuangJuiHsu/DeepCO3/
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network
Authors: Wenda Zhao,  Bowen Zheng,  Qiuhua Lin,  Huchuan Lu
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Defocus Blur Detection, Defocus Estimation, Diversity

Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross-ensemble network. Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross-negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods.
Efficient Parameter-Free Clustering Using First Neighbor Relations
Authors: Saquib Sarfraz,  Vivek Sharma,  Rainer Stiefelhagen
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering

We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data. The main proposition is that the first neighbor of each sample is all one needs to discover large chains and finding the groups in the data. In contrast to most existing clustering algorithms our method does not require any hyper-parameters, distance thresholds and/or the need to specify the number of clusters. The proposed algorithm belongs to the family of hierarchical agglomerative methods. The technique has a very low computational overhead, is easily scalable and applicable to large practical problems. Evaluation on well known datasets from different domains ranging between 1077 and 8.1 million samples shows substantial performance gains when compared to the existing clustering techniques.
Learning Personalized Modular Network Guided by Structured Knowledge
Authors: Xiaodan Liang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decision Making, Reinforcement Learning, Semantic Segmentation

The dominant deep learning approaches use a "one-size-fits-all" paradigm with the hope that underlying characteristics of diverse inputs can be captured via a fixed structure. They also overlook the importance of explicitly modeling feature hierarchy. However, complex real-world tasks often require discovering diverse reasoning paths for different inputs to achieve satisfying predictions, especially for challenging large-scale recognition tasks with complex label relations. In this paper, we treat the structured commonsense knowledge (e.g. concept hierarchy) as the guidance of customizing more powerful and explainable network structures for distinct inputs, leading to dynamic and individualized inference paths. Give an off-the-shelf large network configuration, the proposed Personalized Modular Network (PMN) is learned by selectively activating a sequence of network modules where each of them is designated to recognize particular levels of structured knowledge. Learning semantic configurations and activation of modules to align well with structured knowledge can be regarded as a decision-making procedure, which is solved by a new graph-based reinforcement learning algorithm. Experiments on three semantic segmentation tasks and classification tasks show our PMN can achieve superior performance with the reduced number of network modules while discovering personalized and explainable module configurations for each input.
A Flexible Convolutional Solver for Fast Style Transfers
Authors: Gilles Puy,  Patrick Perez
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Rolling Shutter Correction, Style Transfer, Video Style Transfer

We propose a new flexible deep convolutional neural network (convnet) to perform fast neural style transfers. Our network is trained to solve approximately, but rapidly, the artistic style transfer problem of [Gatys et al.] for arbritary styles. While solutions already exist, our network is uniquely flexible by design: it can be manipulated at runtime to enforce new constraints on the final output. As examples, we show that it can be modified to perform tasks such as fast photorealistic style transfer, or fast video style transfer with short term consistency, with no retraining. This flexibility stems from the proposed architecture which is obtained by unrolling the gradient descent algorithm used in [Gatys et al.]. Regularisations added to [Gatys et al.] to solve a new task can be reported on-the-fly in our network, even after training.
Cross Domain Model Compression by Structurally Weight Sharing
Authors: Shangqian Gao,  Cheng Deng,  Heng Huang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Graph Embedding, model, Model Compression, Scene Recognition, Temporal Action Localization

Regular model compression methods focus on RGB input. While cross domain tasks demand more DNN models, each domain often needs its own model. Consequently, for such tasks, the storage cost, memory footprint and computation cost increase dramatically compared to single RGB input. Moreover, the distinct appearance and special structure in cross domain tasks make it difficult to directly apply regular compression methods on it. In this paper, thus, we propose a new robust cross domain model compression method. Specifically, the proposed method compress cross domain models by structurally weight sharing, which is achieved by regularizing the models with graph embedding at training time. Due to the channel wise weights sharing, the proposed method can reduce computation cost without specially designed algorithm. In the experiments, the proposed method achieves state of the art results on two diverse tasks: action recognition and RGB-D scene recognition.
Transferable AutoML by Model Sharing Over Grouped Datasets
Authors: Chao Xue,  Junchi Yan,  Rong Yan,  Stephen M. Chu,  Yonggang Hu,  Yonghua Lin
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: AutoML, BIG-bench Machine Learning, Clustering, image-classification, Image Classification, model

Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that leverages previously trained models to speed up the search process for new tasks and datasets. Our approach involves a novel meta-feature extraction technique based on the performance of benchmark models, and a dynamic dataset clustering algorithm based on Markov process and statistical hypothesis test. As such multiple models can share a common structure while with different learned parameters. The transferable AutoML can either be applied to search from scratch, search from predesigned models, or transfer from basic cells according to the difficulties of the given datasets. The experimental results on image classification show notable speedup in overall search time for multiple datasets with negligible loss in accuracy.
Deep Incremental Hashing Network for Efficient Image Retrieval
Authors: Dayan Wu,  Qi Dai,  Jing Liu,  Bo Li,  Weiping Wang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Hashing, Image Retrieval, Retrieval

Hashing has shown great potential in large-scale image retrieval due to its storage and computation efficiency, especially the recent deep supervised hashing methods. To achieve promising performance, deep supervised hashing methods require a large amount of training data from different classes. However, when images of new categories emerge, existing deep hashing methods have to retrain the CNN model and generate hash codes for all the database images again, which is impractical for large-scale retrieval system. In this paper, we propose a novel deep hashing framework, called Deep Incremental Hashing Network (DIHN), for learning hash codes in an incremental manner. DIHN learns the hash codes for the new coming images directly, while keeping the old ones unchanged. Simultaneously, a deep hash function for query set is learned by preserving the similarities between training points. Extensive experiments on two widely used image retrieval benchmarks demonstrate that the proposed DIHN framework can significantly decrease the training time while keeping the state-of-the-art retrieval accuracy.
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Authors: Jorg Wagner,  Jan Mathias Kohler,  Tobias Gindele,  Leon Hetzel,  Jakob Thaddaus Wiedemer,  Sven Behnke
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: valid

To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.
SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360deg Images
Authors: Yeonkun Lee,  Jaeseok Jeong,  Jongseob Yun,  Wonjune Cho,  Kuk-Jin Yoon
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Semantic Segmentation

    Omni-directional  cameras  have  many  advantages  overconventional cameras in that they have a much wider field-of-view (FOV). Accordingly, several approaches have beenproposed recently to apply convolutional neural networks(CNNs) to omni-directional images for various visual tasks.However, most of them use image representations defined inthe Euclidean space after transforming the omni-directionalviews originally formed in the non-Euclidean space.  Thistransformation leads to shape distortion due to nonuniformspatial resolving power and the loss of continuity.   Theseeffects make existing convolution kernels experience diffi-culties in extracting meaningful information.     This paper presents a novel method to resolve such prob-lems  of  applying  CNNs  to  omni-directional  images.   Theproposed  method  utilizes  a  spherical  polyhedron  to  rep-resent omni-directional views.  This method minimizes thevariance of the spatial resolving power on the sphere sur-face,  and  includes  new  convolution  and  pooling  methodsfor the proposed representation.  The proposed method canalso be adopted by any existing CNN-based methods.  Thefeasibility of the proposed method is demonstrated throughclassification,  detection,  and semantic segmentation taskswith synthetic and real datasets.