mtybilly/Instruct2DS huggingface dataset:
Image Generation From Layout
Authors: Lili Meng,  Weidong Yin,  Leonid Sigal Bo Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation; we call it Layout2Im. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse generation. The proposed Layout2Im model significantly outperforms the previous state of the art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our method's ability to generate complex and diverse images with multiple objects.
Multimodal Explanations by Predicting Counterfactuality in Videos
Authors: Kentaro Takemoto,  Sho Inayoshi,  Tatsuya Harada Atsushi Kanehira
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not categorized to a specific class with combinations of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a post-hoc manner. The optimization problem, which appears during training/inference, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the action recognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.
Learning to Explain With Complemental Examples
Authors: Tatsuya Harada Atsushi Kanehira
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper addresses the generation of explanations with visual examples. Given an input sample, we build a system that not only classifies it to a specific category, but also outputs linguistic explanations and a set of visual examples that render the decision interpretable. Focusing especially on the complementarity of the multimodal information, i.e., linguistic and visual examples, we attempt to achieve it by maximizing the interaction information, which provides a natural definition of complementarity from an information theoretical viewpoint. We propose a novel framework to generate complemental explanations, on which the joint distribution of the variables to explain, and those to be explained is parameterized by three different neural networks: predictor, linguistic explainer, and example selector. Explanation models are trained collaboratively to maximize the interaction information to ensure the generated explanation are complemental to each other for the target. The results of experiments conducted on several datasets demonstrate the effectiveness of the proposed method.
HAQ: Hardware-Aware Automated Quantization With Mixed Precision
Authors: Zhijian Liu,  Yujun Lin,  Ji Lin,  Song Han Kuan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. There are plenty of specialized hardware for neural networks, but little research has been done for specialized neural network optimization for a particular hardware architecture. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design.
Content Authentication for Neural Imaging Pipelines: End-To-End Optimization of Photo Provenance in Complex Distribution Channels
Authors: Nasir Memon Pawel Korus
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition. Such analysis becomes unreliable after heavy post-processing, such as down-sampling and re-compression applied upon distribution in the Web. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel. We demonstrate that neural imaging pipelines can be trained to replace the internals of digital cameras, and jointly optimized for high-fidelity photo development and reliable provenance analysis. In our experiments, the proposed approach increased image manipulation detection accuracy from 45% to over 90%. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.
Inverse Procedural Modeling of Knitwear
Authors: Sebastian Merzbach,  Jonathan Klein,  Thomas Schulze,  Michael Weinmann,  Reinhard Klein Elena Trunz
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: The analysis and modeling of cloth has received a lot of attention in recent years. While recent approaches are focused on woven cloth, we present a novel practical approach for the inference of more complex knitwear structures as well as the respective knitting instructions from only a single image without attached annotations. Knitwear is produced by repeating instances of the same pattern, consisting of grid-like arrangements of a small set of basic stitch types. Our framework addresses the identification and localization of the occurring stitch types, which is challenging due to huge appearance variations. The resulting coarsely localized stitch types are used to infer the underlying grid structure as well as for the extraction of the knitting instruction of pattern repeats, taking into account principles of Gestalt theory. Finally, the derived instructions allow the reproduction of the knitting structures, either as renderings or by actual knitting, as demonstrated in several examples.
Estimating 3D Motion and Forces of Person-Object Interactions From Monocular Video
Authors: Jiri Sedlar,  Justin Carpentier,  Ivan Laptev,  Nicolas Mansard,  Josef Sivic Zongmian Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person and the object, contact positions, and forces and torques actuated by the human limbs. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of their interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization.  Third, we validate our approach on a recent MoCap dataset with ground truth contact forces and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
Authors: Chen Feng Li Ding
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that "training" these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at https://ai4ce.github.io/DeepMapping/.
End-To-End Interpretable Neural Motion Planner
Authors: Wenjie Luo,  Simon Suo,  Abbas Sadat,  Bin Yang,  Sergio Casas,  Raquel Urtasun Wenyuan Zeng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces  interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car  can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured  in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model
Authors: Erik Nijkamp,  Xiaolin Fang,  Mitch Hill,  Song-Chun Zhu,  Ying Nian Wu Tian Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper proposes the divergence triangle as a framework for joint training of a generator model, energy-based model and inference model. The divergence triangle is a compact and symmetric (anti-symmetric) objective function that seamlessly integrates variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence in a unified probabilistic formulation. This unification makes the processes of sampling, inference, and energy evaluation readily available without the need for costly Markov chain Monte Carlo methods. Our experiments demonstrate that the divergence triangle is capable of learning (1) an energy-based model with well-formed energy landscape, (2) direct sampling in the form of a generator network, and (3) feed-forward inference that faithfully reconstructs observed as well as synthesized data.
Image Deformation Meta-Networks for One-Shot Learning
Authors: Yanwei Fu,  Yu-Xiong Wang,  Lin Ma,  Wei Liu,  Martial Hebert Zitian Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Humans can robustly learn novel visual concepts even when images undergo various deformations and loose certain information. Mimicking the same behavior and synthesizing deformed instances of new concepts may help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images may not be visually realistic, they still maintain critical semantic information and contribute significantly to formulating classifier decision boundaries. Inspired by the recent progress of meta-learning, we combine a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner. The deformation sub-network learns to deform images by fusing a pair of images --- a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used one-shot learning benchmarks (miniImageNet and ImageNet 1K Challenge datasets), which significantly outperform state-of-the-art approaches.
Online High Rank Matrix Completion
Authors: Madeleine Udell Jicong Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new model for high rank matrix completion (HRMC), together with batch and online methods to fit the model and out-of-sample extension to complete new data. The method works by (implicitly) mapping the data into a high dimensional polynomial feature space using the kernel trick; importantly, the data occupies a low dimensional subspace in this feature space, even when the original data matrix is of full-rank. The online method can handle streaming or sequential data and adapt to non-stationary latent structure, and enjoys much lower space and time complexity than previous methods for HRMC. For example, the time complexity is reduced from O(n^3) to O(r^3), where n is the number of data points, r is the matrix rank in the feature space, and r<< n. We also provide guidance on sampling rate required for these methods to succeed. Experimental results on synthetic data and motion data validate the performance of the proposed methods.
Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds
Authors: Bernardo R. Pires,  Martial Hebert,  Srinivasa G. Narasimhan Tiancheng Zhi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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.
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging
Authors: Cusuh Ham,  Charles C. Kemp,  James Hays Samarth Brahmbhatt
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel dataset of contact maps for household objects that captures the rich hand-object contact that occurs during grasping, enabled by use of a thermal camera. Participants in our study grasped 3D printed objects with a post-grasp functional intent. ContactDB includes 3750 3D meshes of 50 household objects textured with contact maps and 375K frames of synchronized RGB-D+thermal images. To the best of our knowledge, this is the first large-scale dataset that records detailed contact maps for human grasps. Analysis of this data shows the influence of functional intent and object size on grasping, the tendency to touch/avoid 'active areas', and the high frequency of palm and proximal finger contact. Finally, we train state-of-the art image translation and 3D convolution algorithms to predict diverse contact patterns from object shape. Data, code and models are available at https://contactdb.cc.gatech.edu.
Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling
Authors: Jiantao Zhou,  Xianwei Zheng,  Jinyu Tian,  Yuan Yan Tang Yuanman Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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: Ajad Chhatkuli,  Danda Pani Paudel,  Luc Van Gool Thomas Probst
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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.
Self-Calibrating Deep Photometric Stereo Networks
Authors: Kai Han,  Boxin Shi,  Yasuyuki Matsushita,  Kwan-Yee K. Wong Guanying Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distributions, our method is able to determine both shape and light directions of a scene with unknown arbitrary reflectances observed under unknown varying light directions. To achieve this goal, we propose a two-stage deep learning architecture, called SDPS-Net, which can effectively take advantage of intermediate supervision, resulting in reduced learning difficulty compared to a single-stage model. Experiments on both synthetic and real datasets show that our proposed approach significantly outperforms previous uncalibrated photometric stereo methods.
Argoverse: 3D Tracking and Forecasting With Rich Maps
Authors: John Lambert,  Patsorn Sangkloy,  Jagjeet Singh,  Slawomir Bak,  Andrew Hartnett,  De Wang,  Peter Carr,  Simon Lucey,  Deva Ramanan,  James Hays Ming-Fang Chang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: We present Argoverse, a dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps. The sensor data consists of 360 degree images from 7 cameras with overlapping fields of view, forward-facing stereo imagery, 3D point clouds from long range LiDAR, and 6-DOF pose.  Our 290km of mapped lanes contain rich geometric and semantic metadata which are not currently available in any public dataset. All data is released under a Creative Commons license at Argoverse.org. In baseline experiments, we use map information such as lane direction, driveable area, and ground height to improve the accuracy of 3D object tracking. We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark. Motion forecasting experiments ranging in complexity from classical methods (k-NN) to LSTMs demonstrate that using detailed vector maps with lane-level information substantially reduces prediction error. Our tracking and forecasting experiments represent only a superficial exploration of the potential of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.
Side Window Filtering
Authors: Yuanhao Gong,  Guoping Qiu Hui Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Local windows are routinely used in computer vision and almost without exception the center of the window is aligned with the pixels being processed. We show that this conventional wisdom is not universally applicable. When a pixel is on an edge, placing the center of the window on the pixel is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we propose a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed. The SWF technique is surprisingly simple yet theoretically rooted and very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under the SWF framework. Extensive analysis and experiments show that implementing the SWF principle can significantly improve their edge preserving capabilities and achieve state of the art performances in applications such as image smoothing, denoising, enhancement, structure-preserving texture-removing, mutual-structure extraction, and HDR tone mapping. In addition to image filtering, we further show that the SWF principle can be extended to other applications involving the use of a local window. Using colorization by optimization as an example, we demonstrate that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation. Given the ubiquity of window based operations in computer vision, the new SWF technique is likely to benefit many more applications.
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search
Authors: Laurens van der Maaten,  Zeki Yalniz,  Yixuan Li,  Dhruv Mahajan Abhimanyu Dubey
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image manifold in the sense that there exists no physical process that could have produced the adversarial image. This hypothesis suggests that a successful defense mechanism against adversarial images should aim to project the images back onto the image manifold. We study such defense mechanisms, which approximate the projection onto the unknown image manifold by a nearest-neighbor search against a web-scale image database containing tens of billions of images. Empirical evaluations of this defense strategy on ImageNet suggest that it very effective in attack settings in which the adversary does not have access to the image database. We also propose two novel attack methods to break nearest-neighbor defense settings and show conditions under which nearest-neighbor defense fails. We perform a series of ablation experiments, which suggest that there is a trade-off between robustness and accuracy between as we use features from deeper in the network, that a large index size (hundreds of millions) is crucial to get good performance, and that careful construction of database is crucial for robustness against nearest-neighbor attacks.
Incremental Object Learning From Contiguous Views
Authors: Samarth Mishra,  Ngoc Anh Thai,  Nikhil Dhanda,  Ahmad Humayun,  Chen Yu,  Linda B. Smith,  James M. Rehg Stefan Stojanov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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: Chi Zhan,  Yu-Kun Lai,  Ming-Ming Cheng,  Jufeng Yang Xiaoping Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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.
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Authors: Milind Naphade,  Ming-Yu Liu,  Xiaodong Yang,  Stan Birchfield,  Shuo Wang,  Ratnesh Kumar,  David Anastasiu,  Jenq-Neng Hwang Zheng Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.
Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence
Authors: Michael Chan,  Paul Pu Liang,  Edmund Tong,  Louis-Philippe Morency Amir Zadeh
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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).
UPSNet: A Unified Panoptic Segmentation Network
Authors: Renjie Liao,  Hengshuang Zhao,  Rui Hu,  Min Bai,  Ersin Yumer,  Raquel Urtasun Yuwen Xiong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network, we first design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously. More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification. It first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolving the conflicts between semantic and instance segmentation. Besides, it handles the challenge caused by the varying number of instances and permits back propagation to the bottom modules in an end-to-end manner. Extensive experimental results on Cityscapes, COCO and our internal dataset demonstrate that our UPSNet achieves state-of-the-art performance with much faster inference. Code has been made available at: https://github.com/uber-research/UPSNet
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Authors: Thanh Nguyen,  Binh-Son Hua,  Gemma Roig,  Sai-Kit Yeung Quang-Hieu Pham
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Deep learning techniques have become the to-go models for most vision-related tasks on 2D images. However, their power has not been fully realised on several tasks in 3D space, e.g., 3D scene understanding. In this work, we jointly address the problems of semantic and instance segmentation of 3D point clouds. Specifically, we develop a multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings. We then propose a multi-value conditional random field model to incorporate the semantic and instance labels and formulate the problem of semantic and instance segmentation as jointly optimising labels in the field model. The proposed method is thoroughly evaluated and compared with existing methods on different indoor scene datasets including S3DIS and SceneNN. Experimental results showed the robustness of the proposed joint semantic-instance segmentation scheme over its single components. Our method also achieved state-of-the-art performance on semantic segmentation.
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
Authors: Bert De Brabandere,  Marc Proesmans,  Luc Van Gool Davy Neven
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Current state-of-the-art instance segmentation methods are not suited for real-time applications like autonomous driving, which require fast execution times at high accuracy. Although the currently dominant proposal-based methods have high accuracy, they are slow and generate masks at a fixed and low resolution. Proposal-free methods, by contrast, can generate masks at high resolution and are often faster, but fail to reach the same accuracy as the proposal-based methods. In this work we propose a new clustering loss function for proposal-free instance segmentation. The loss function pulls the spatial embeddings of pixels belonging to the same instance together and jointly learns an instance-specific clustering bandwidth, maximizing the intersection-over-union of the resulting instance mask. When combined with a fast architecture, the network can perform instance segmentation in real-time while maintaining a high accuracy. We evaluate our method on the challenging Cityscapes benchmark and achieve top results (5% improvement over Mask R-CNN) at more than 10 fps on 2MP images.
DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection
Authors: Yen-Yu Lin,  Yung-Yu Chuang Kuang-Jui Hsu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: 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/
Improving Semantic Segmentation via Video Propagation and Label Relaxation
Authors: Karan Sapra,  Fitsum A. Reda,  Kevin J. Shih,  Shawn Newsam,  Andrew Tao,  Bryan Catanzaro Yi Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction models' ability to predict future frames in order to also predict future labels. A joint propagation strategy is also proposed to alleviate mis-alignments in synthesized samples. We demonstrate that training segmentation models on datasets augmented by the synthesized samples leads to significant improvements in accuracy. Furthermore, we introduce a novel boundary label relaxation technique that makes training robust to annotation noise and propagation artifacts along object boundaries. Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our single model, without model ensembles, achieves 72.8% mIoU on the KITTI semantic segmentation test set, which surpasses the winning entry of the ROB challenge 2018.
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
Authors: Xin Wang,  Joseph E. Gonzalez Samvit Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame. The modularity of the update branch, where feature subnetworks of varying layer depth can be inserted (e.g. ResNet-18 to ResNet-101), enables operation over a new, state-of-the-art accuracy-throughput trade-off spectrum. Over this curve, Accel models achieve both higher accuracy and faster inference times than the closest comparable single-frame segmentation networks. In general, Accel significantly outperforms previous work on efficient semantic video segmentation, correcting warping-related error that compounds on datasets with complex dynamics. Accel is end-to-end trainable and highly modular: the reference network, the optical flow network, and the update network can each be selected independently, depending on application requirements, and then jointly fine-tuned. The result is a robust, general system for fast, high-accuracy semantic segmentation on video.
Shape2Motion: Joint Analysis of Motion Parts and Attributes From 3D Shapes
Authors: Bin Zhou,  Yahao Shi,  Xiaowu Chen,  Qinping Zhao,  Kai Xu Xiaogang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input. The problem is significantly different from those tackled in the existing works which assume the availability of either a pre-existing shape segmentation or multiple 3D models in different motion states. To that end, we develop Shape2Motion which takes a single 3D point cloud as input, and jointly computes a mobility-oriented segmentation and the associated motion attributes. Shape2Motion is comprised of two deep neural networks designed for mobility proposal generation and mobility optimization, respectively. The key contribution of these networks is the novel motion-driven features and losses used in both motion part segmentation and motion attribute estimation. This is based on the observation that the movement of a functional part preserves the shape structure. We evaluate Shape2Motion with a newly proposed benchmark for mobility analysis of 3D shapes. Results demonstrate that our method achieves the state-of-the-art performance both in terms of motion part segmentation and motion attribute estimation.
Semantic Correlation Promoted Shape-Variant Context for Segmentation
Authors: Xudong Jiang,  Bing Shuai,  Ai Qun Liu,  Gang Wang Henghui Ding
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (Main Conference Track), 2019

Abstract: Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this work, we propose to generate a scale- and shape-variant semantic mask for each pixel to confine its contextual region. To this end, we first propose a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, we propose a shape-variant convolution, of which the receptive field is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. Furthermore, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. Without bells and whistles, the proposed segmentation network achieves new state-of-the-arts consistently on the six public segmentation datasets.