mtybilly/Instruct2DS huggingface dataset:
QUARCH: A New Quasi-Affine Reconstruction Stratum From Vague Relative Camera Orientation Knowledge
Authors: Devesh Adlakha, Adlane Habed, Fabio Morbidi, Cedric Demonceaux, Michel de Mathelin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a new quasi-affine reconstruction of a scene and its application to camera self-calibration. We refer to this reconstruction as QUARCH (QUasi-Affine Reconstruction with respect to Camera centers and the Hodographs of horopters). A QUARCH can be obtained by solving a semidefinite programming problem when, (i) the images have been captured by a moving camera with constant intrinsic parameters, and (ii) a vague knowledge of the relative orientation (under or over 120 degrees) between camera pairs is available. The resulting reconstruction comes close enough to an affine one allowing thus an easy upgrade of the QUARCH to its affine and metric counterparts. We also present a constrained Levenberg-Marquardt method for nonlinear optimization subject to Linear Matrix Inequality (LMI) constraints so as to ensure that the QUARCH LMIs are satisfied during optimization. Experiments with synthetic and real data show the benefits of QUARCH in reliably obtaining a metric reconstruction.
Homography From Two Orientation- and Scale-Covariant Features
Authors: Daniel Barath, Zuzana Kukelova
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper proposes a geometric interpretation of the angles and scales which the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks. Using these formulas, two new constraints on homography estimation are introduced. Exploiting the derived equations, a solver for estimating the homography from the minimal number of two correspondences is proposed. Also, it is shown how the normalization of the point correspondences affects the rotation and scale parameters, thus achieving numerically stable results. Due to requiring merely two feature pairs, robust estimators, e.g. RANSAC, do significantly fewer iterations than by using the four-point algorithm. When using covariant features, e.g. SIFT, this additional information is given at no cost. The method is tested in a synthetic environment and on publicly available real-world datasets.
Hiding Video in Audio via Reversible Generative Models
Authors: Hyukryul Yang, Hao Ouyang, Vladlen Koltun, Qifeng Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a method for hiding video content inside audio files while preserving the perceptual fidelity of the cover audio. This is a form of cross-modal steganography and is particularly challenging due to the high bitrate of video. Our scheme uses recent advances in flow-based generative models, which enable mapping audio to latent codes such that nearby codes correspond to perceptually similar signals. We show that compressed video data can be concealed in the latent codes of audio sequences while preserving the fidelity of both the hidden video and the cover audio. We can embed 128x128 video inside same-duration audio, or higher-resolution video inside longer audio sequences. Quantitative experiments show that our approach outperforms relevant baselines in steganographic capacity and fidelity.
GSLAM: A General SLAM Framework and Benchmark
Authors: Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: SLAM technology has recently seen many successes and attracted the attention of high-technological companies. However, how to unify the interface of existing or emerging algorithms, and effectively perform benchmark about the speed, robustness and portability are still problems. In this paper, we propose a novel SLAM platform named GSLAM, which not only provides evaluation functionality, but also supplies useful toolkit for researchers to quickly develop their SLAM systems. Our core contribution is an universal, cross-platform and full open-source SLAM interface for both research and commercial usage, which is aimed to handle interactions with input dataset, SLAM implementation, visualization and applications in an unified framework. Through this platform, users can implement their own functions for better performance with plugin form and further boost the application to practical usage of the SLAM.
Elaborate Monocular Point and Line SLAM With Robust Initialization
Authors: Sang Jun Lee, Sung Soo Hwang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper presents a monocular indirect SLAM system which performs robust initialization and accurate localization. For initialization, we utilize a matrix factorization-based method. Matrix factorization-based methods require that extracted feature points must be tracked in all used frames. Since consistent tracking is difficult in challenging environments, a geometric interpolation that utilizes epipolar geometry is proposed. For localization, 3D lines are utilized. We propose the use of Plu cker line coordinates to represent geometric information of lines. We also propose orthonormal representation of Plu cker line coordinates and Jacobians of lines for better optimization. Experimental results show that the proposed initialization generates consistent and robust map in linear time with fast convergence even in challenging scenes. And localization using proposed line representations is faster, more accurate and memory efficient than other state-of-the-art methods.
Adaptive Density Map Generation for Crowd Counting
Authors: Jia Wan, Antoni Chan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Crowd counting is an important topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map generation), e.g., by convolving with a Gaussian kernel. Second, deep learning models are designed to predict a density map from an input image (density map estimation). Most research efforts have concentrated on the density map estimation problem, while the problem of density map generation has not been adequately explored. In particular, the density map could be considered as an intermediate representation used to train a crowd counting network. In the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we first show the impact of different density maps and that better ground-truth density maps can be obtained by refining the existing ones using a learned refinement network, which is jointly trained with the counter. Then, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for a counter. The counter and generator are trained jointly within an end-to-end framework. The experiment results on popular counting datasets confirm the effectiveness of the proposed learnable density map representations.
Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval
Authors: Xingxu Yao, Dongyu She, Sicheng Zhao, Jie Liang, Yu-Kun Lai, Jufeng Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Images play a crucial role for people to express their opinions online due to the increasing popularity of social networks. While an affective image retrieval system is useful for obtaining visual contents with desired emotions from a massive repository, the abstract and subjective characteristics make the task challenging. To address the problem, this paper introduces an Attention-aware Polarity Sensitive Embedding (APSE) network to learn affective representations in an end-to-end manner. First, to automatically discover and model the informative regions of interest, we develop a hierarchical attention mechanism, in which both polarity- and emotion-specific attended representations are aggregated for discriminative feature embedding. Second, we present a weighted emotion-pair loss to take the inter- and intra-polarity relationships of the emotional labels into consideration. Guided by attention module, we weight the sample pairs adaptively which further improves the performance of feature embedding. Extensive experiments on four popular benchmark datasets show that the proposed method performs favorably against the state-of-the-art approaches.
Zero-Shot Emotion Recognition via Affective Structural Embedding
Authors: Chi Zhan, Dongyu She, Sicheng Zhao, Ming-Ming Cheng, Jufeng Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Image emotion recognition attracts much attention in recent years due to its wide applications. It aims to classify the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories, such as Ekman's six basic emotions. However, with the development of psychological theories, emotion categories become increasingly diverse, fine-grained, and difficult to collect samples. In this paper, we investigate zero-shot learning (ZSL) problem in the emotion recognition task, which tries to recognize the new unseen emotions. Specifically, we propose a novel affective-structural embedding framework, utilizing mid-level semantic representation, i.e., adjective-noun pairs (ANP) features, to construct an affective embedding space. By doing this, the learned intermediate space can narrow the semantic gap between low-level visual and high-level semantic features. In addition, we introduce an affective adversarial constraint to retain the discriminative capacity of visual features and the affective structural information of semantic features during training process. Our method is evaluated on five widely used affective datasets and the perimental results show the proposed algorithm outperforms the state-of-the-art approaches.
FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On
Authors: Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, Jian Yin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively.
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation
Authors: Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose an interactive GAN-based sketch-to-image translation method that helps novice users easily create images of simple objects. The user starts with a sparse sketch and a desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. This enables a feedback loop, where the user can edit the sketch based on the network's recommendations, while the network is able to better synthesize the image that the user might have in mind. In order to use a single model for a wide array of object classes, we introduce a gating-based approach for class conditioning, which allows us to generate distinct classes without feature mixing, from a single generator network.
Attention-Based Autism Spectrum Disorder Screening With Privileged Modality
Authors: Shi Chen, Qi Zhao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper presents a novel framework for automatic and quantitative screening of autism spectrum disorder (ASD). It is motivated to address two issues in the current clinical settings: 1) short of clinical resources with the prevalence of ASD (1.7% in the United States), and 2) subjectivity of ASD screening. This work differentiates itself with three unique features: first, it proposes an ASD screening with privileged modality framework that integrates information from two behavioral modalities during training and improves the performance on each single modality at testing. The proposed framework does not require overlap in subjects between the modalities. Second, it develops the first computational model to classify people with ASD using a photo-taking task where subjects freely explore their environment in a more ecological setting. Photo-taking reveals attentional preference of subjects, differentiating people with ASD from healthy people, and is also easy to implement in real-world clinical settings without requiring advanced diagnostic instruments. Third, this study for the first time takes advantage of the temporal information in eye movements while viewing images, encoding more detailed behavioral differences between ASD people and healthy controls. Experiments show that our ASD screening models can achieve superior performance, outperforming the previous state-of-the-art methods by a considerable margin. Moreover, our framework using diverse modalities demonstrates performance improvement on both the photo-taking and image-viewing tasks, providing a general paradigm that takes in multiple sources of behavioral data for a more accurate ASD screening. The framework is also applicable to various scenarios where one-to-one pairwise relationship is difficult to obtain across different modalities.
Image Aesthetic Assessment Based on Pairwise Comparison  A Unified Approach to Score Regression, Binary Classification, and Personalization
Authors: Jun-Tae Lee, Chang-Su Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose a unified approach to three tasks of aesthetic score regression, binary aesthetic classification, and personalized aesthetics. First, we develop a comparator to estimate the ratio of aesthetic scores for two images. Then, we construct a pairwise comparison matrix for multiple reference images and an input image, and predict the aesthetic score of the input via the eigenvalue decomposition of the matrix. By varying the reference images, the proposed algorithm can be used for binary aesthetic classification and personalized aesthetics, as well as generic score regression. Experimental results demonstrate that the proposed unified algorithm provides the state-of-the-art performances in all three tasks of image aesthetics.
Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
Authors: Zhenyu Wu, Karthik Suresh, Priya Narayanan, Hongyu Xu, Heesung Kwon, Zhangyang Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc. Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. Fortunately, UAVs will record meta-data that depict those varying attributes, which are either freely available along with the UAV images, or can be easily obtained. We propose to utilize those free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. We demonstrate the effectiveness of our proposed algorithm, by showing state-of-the- art performance (single model) on two existing UAV-based object detection benchmarks. The code is available at https://github.com/TAMU-VITA/UAV-NDFT.
Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search
Authors: Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated security concern of DNN is from its malicious input, a.k.a adversarial example. Nevertheless, the security challenge of DNN's parameters is not well explored yet. In this work, we are the first to propose a novel DNN weight attack methodology called Bit-Flip Attack (BFA) which can crush a neural network through maliciously flipping extremely small amount of bits within its weight storage memory system (i.e., DRAM). The bit-flip operations could be conducted through well-known Row-Hammer attack, while our main contribution is to develop an algorithm to identify the most vulnerable bits of DNN weight parameters (stored in memory as binary bits), that could maximize the accuracy degradation with a minimum number of bit-flips. Our proposed BFA utilizes a Progressive Bit Search (PBS) method which combines gradient ranking and progressive search to identify the most vulnerable bit to be flipped. With the aid of PBS, we can successfully attack a ResNet-18 fully malfunction (i.e., top-1 accuracy degrade from 69.8% to 0.1%) only through 13 bit-flips out of 93 million bits, while randomly flipping 100 bits merely degrades the accuracy by less than 1%. Code is released at: https://github.com/elliothe/Neural_Network_Weight_Attack
Employing Deep Part-Object Relationships for Salient Object Detection
Authors: Yi Liu, Qiang Zhang, Dingwen Zhang, Jungong Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Despite Convolutional Neural Networks (CNNs) based methods have been successful in detecting salient objects, their underlying mechanism that decides the salient intensity of each image part separately cannot avoid inconsistency of parts within the same salient object. This would ultimately result in an incomplete shape of the detected salient object. To solve this problem, we dig into part-object relationships and take the unprecedented attempt to employ these relationships endowed by the Capsule Network (CapsNet) for salient object detection. The entire salient object detection system is built directly on a Two-Stream Part-Object Assignment Network (TSPOANet) consisting of three algorithmic steps. In the first step, the learned deep feature maps of the input image are transformed to a group of primary capsules. In the second step, we feed the primary capsules into two identical streams, within each of which low-level capsules (parts) will be assigned to their familiar high-level capsules (object) via a locally connected routing. In the final step, the two streams are integrated in the form of a fully connected layer, where the relevant parts can be clustered together to form a complete salient object. Experimental results demonstrate the superiority of the proposed salient object detection network over the state-of-the-art methods.
Self-Supervised Deep Depth Denoising
Authors: Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce self-supervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at https://github.com/VCL3D/DeepDepthDenoising
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
Authors: Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communication bits to conserve power, and the cloud upon receiving the edge inputs returns a classification label. To deal with fine-grained classification, we adopt the perspective of sequential fixation with a foveated field-of-view to model cloud-edge interactions. We propose a novel deep reinforcement learning-based foveation model, DRIFT, that sequentially generates and recognizes mixed-acuity images. Training of DRIFT requires only image-level category labels and encourages fixations to contain task-relevant information, while maintaining data efficiency. Specifically, we train a foveation actor network with a novel Deep Deterministic Policy Gradient by Conditioned Critic and Coaching(DDPGC3) algorithm. In addition, we propose to shape the reward to provide informative feedback after each fixation to better guide RL training. We demonstrate the effectiveness of DRIFT on this task by evaluating on five fine-grained classification benchmark datasets, and show that the proposed approach achieves state-of-the-art performance with over 3X reduction in transmitted pixels.
Layout-Induced Video Representation for Recognizing Agent-in-Place Actions
Authors: Ruichi Yu, Hongcheng Wang, Ang Li, Jingxiao Zheng, Vlad I. Morariu, Larry S. Davis
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We address scene layout modeling for recognizing agent-in-place actions, which are actions associated with agents who perform them and the places where they occur, in the context of outdoor home surveillance. We introduce a novel representation to model the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training scenes to unseen scenes in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places to explicitly model scene layout. LIVR partitions the semantic features of a scene into different places to force the network to learn generic place-based feature descriptions which are independent of specific scene layouts; then, LIVR dynamically aggregates features based on connectivities of places in each specific scene to model its layout. We introduce a new Agent-in-Place Action (APA) dataset(The dataset is pending legal review and will be released upon the acceptance of this paper.) to show that our method allows neural network models to generalize significantly better to unseen scenes.
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence
Authors: Trong-Nguyen Nguyen, Jean Meunier
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.
Exploring Randomly Wired Neural Networks for Image Recognition
Authors: Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets and DenseNets is due in large part to their innovative wiring plans. Now, neural architecture search (NAS) studies are exploring the joint optimization of wiring and operation types, however, the space of possible wirings is constrained and still driven by manual design despite being searched. In this paper, we explore a more diverse set of connectivity patterns through the lens of randomly wired neural networks. To do this, we first define the concept of a stochastic network generator that encapsulates the entire network generation process. Encapsulation provides a unified view of NAS and randomly wired networks. Then, we use three classical random graph models to generate randomly wired graphs for networks. The results are surprising: several variants of these random generators yield network instances that have competitive accuracy on the ImageNet benchmark. These results suggest that new efforts focusing on designing better network generators may lead to new breakthroughs by exploring less constrained search spaces with more room for novel design.
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation
Authors: Xin Chen, Lingxi Xie, Jun Wu, Qi Tian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search. However, these approaches often report lower accuracy in evaluating the searched architecture or transferring it to another dataset. This is arguably due to the large gap between the architecture depths in search and evaluation scenarios. In this paper, we present an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. This brings two issues, namely, heavier computational overheads and weaker search stability, which we solve using search space approximation and regularization, respectively. With a significantly reduced search time ( 7 hours on a single GPU), our approach achieves state-of-the-art performance on both the proxy dataset (CIFAR10 or CIFAR100) and the target dataset (ImageNet). Code is available at https://github.com/chenxin061/pdarts
Multinomial Distribution Learning for Effective Neural Architecture Search
Authors: Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, Qi Tian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Architectures obtained by Neural Architecture Search (NAS) have achieved highly competitive performance in various computer vision tasks. However, the prohibitive computation demand of forward-backward propagation in deep neural networks and searching algorithms makes it difficult to apply NAS in practice. In this paper, we propose a Multinomial Distribution Learning for extremely effective NAS, which considers the search space as a joint multinomial distribution, i.e., the operation between two nodes is sampled from this distribution, and the optimal network structure is obtained by the operations with the most likely probability in this distribution. Therefore, NAS can be transformed to a multinomial distribution learning problem, i.e., the distribution is optimized to have a high expectation of the performance. Besides, a hypothesis that the performance ranking is consistent in every training epoch is proposed and demonstrated to further accelerate the learning process. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of our method. On CIFAR-10, the structure searched by our method achieves 2.55% test error, while being 6.0x (only 4 GPU hours on GTX1080Ti) faster compared with state-of-the-art NAS algorithms. On ImageNet, our model achieves 74% top1 accuracy under MobileNet settings (MobileNet V1/V2), while being 1.2x faster with measured GPU latency. Test code with pre-trained models are available at https: //github.com/tanglang96/MDENAS
Searching for MobileNetV3
Authors: Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate compared to a MobileNetV2 model with comparable latency. MobileNetV3-Large detection is over 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 34% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.
Data-Free Quantization Through Weight Equalization and Bias Correction
Authors: Markus Nagel, Mart van Baalen, Tijmen Blankevoort, Max Welling
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning hardware. However, quantizing models to run in 8-bit is a non-trivial task, frequently leading to either significant performance reduction or engineering time spent on training a network to be amenable to quantization. Our approach relies on equalizing the weight ranges in the network by making use of a scale-equivariance property of activation functions. In addition the method corrects biases in the error that are introduced during quantization. This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call. For common architectures, such as the MobileNet family, we achieve state-of-the-art quantized model performance. We further show that the method also extends to other computer vision architectures and tasks such as semantic segmentation and object detection.
A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays
Authors: Laurie Bose, Jianing Chen, Stephen J. Carey, Piotr Dudek, Walterio Mayol-Cuevas
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elements, each capable of light capture, data storage, program execution, and communication with neighboring elements. This allows images to be stored and manipulated directly at the point of light capture, rather than having to transfer images to external processing hardware. Our CNN approach divides this array up into 4x4 blocks of processing elements, essentially trading-off image resolution for increased local memory capacity per 4x4 "pixel". We implement parallel operations for image addition, subtraction and bit-shifting images in this 4x4 block format. Using these components we formulate how to perform ternary weight convolutions upon these images, compactly store results of such convolutions, perform max-pooling, and transfer the resulting sub-sampled data to an attached micro-controller. We train ternary weight filter CNNs for digit recognition and a simple tracking task, and demonstrate inference of these networks upon the SCAMP5 PPA system. This work represents a first step towards embedding neural network processing capability directly onto the focal plane of a sensor.
Knowledge Distillation via Route Constrained Optimization
Authors: Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Junjie Yan, Xiaolin Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a higher lower bound of congruence loss. In this work, we consider the knowledge distillation from the perspective of curriculum learning by teacher's routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace. RCO achieves 84.3% accuracy on one-to-million task with only 0.8 M parameters, which push the SOTA by a large margin.
Distillation-Based Training for Multi-Exit Architectures
Authors: Mary Phuong, Christoph H. Lampert
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Multi-exit architectures, in which a stack of processing layers is interleaved with early output layers, allow the processing of a test example to stop early and thus save computation time and/or energy. In this work, we propose a new training procedure for multi-exit architectures based on the principle of knowledge distillation. The method encourages early exits to mimic later, more accurate exits, by matching their probability outputs. Experiments on CIFAR100 and ImageNet show that distillation-based training significantly improves the accuracy of early exits while maintaining state-of-the-art accuracy for late ones. The method is particularly beneficial when training data is limited and also allows a straight-forward extension to semi-supervised learning, i.e. make use also of unlabeled data at training time. Moreover, it takes only a few lines to implement and imposes almost no computational overhead at training time, and none at all at test time.
Similarity-Preserving Knowledge Distillation
Authors: Frederick Tung, Greg Mori
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network compression, a high-capacity teacher is distilled to train a compact student; in privileged learning, a teacher trained with privileged data is distilled to train a student without access to that data. The distillation loss determines how a teacher's knowledge is captured and transferred to the student. In this paper, we propose a new form of knowledge distillation loss that is inspired by the observation that semantically similar inputs tend to elicit similar activation patterns in a trained network. Similarity-preserving knowledge distillation guides the training of a student network such that input pairs that produce similar (dissimilar) activations in the teacher network produce similar (dissimilar) activations in the student network. In contrast to previous distillation methods, the student is not required to mimic the representation space of the teacher, but rather to preserve the pairwise similarities in its own representation space. Experiments on three public datasets demonstrate the potential of our approach.
Many Task Learning With Task Routing
Authors: Gjorgji Strezoski, Nanne van Noord, Marcel Worring
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this paper, we introduce a method which applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks. To distinguish from regular MTL, we introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. Our method dubbed Task Routing (TR) is encapsulated in a layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario successfully fits hundreds of classification tasks in one model. We evaluate on 5 datasets and the Visual Decathlon (VD) challenge against strong baselines and state-of-the-art approaches.
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
Authors: Felix J.S. Bragman, Ryutaro Tanno, Sebastien Ourselin, Daniel C. Alexander, Jorge Cardoso
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture. The number of possible sharing patterns are combinatorial in the depth of the network and the number of tasks, and thus hand-crafting an architecture, purely based on the human intuitions of task relationships can be time-consuming and suboptimal. In this paper, we present a probabilistic approach to learning task-specific and shared representations in CNNs for multi-task learning. Specifically, we propose "stochastic filter groups" (SFG), a mechanism to assign convolution kernels in each layer to "specialist" and "generalist" groups, which are specific to and shared across different tasks, respectively. The SFG modules determine the connectivity between layers and the structures of task-specific and shared representations in the network. We employ variational inference to learn the posterior distribution over the possible grouping of kernels and network parameters. Experiments demonstrate the proposed method generalises across multiple tasks and shows improved performance over baseline methods.
Transferability and Hardness of Supervised Classification Tasks
Authors: Anh T. Tran, Cuong V. Nguyen, Tal Hassner
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables and exploring their statistics. When transferring from a source to a target task, we consider the conditional entropy between two such variables (i.e., label assignments of the two tasks). We show analytically and empirically that this value is related to the loss of the transferred model. We further show how to use this value to estimate task hardness. We test our claims extensively on three large scale data sets---CelebA (40 tasks), Animals with Attributes 2 (85 tasks), and Caltech-UCSD Birds 200 (312 tasks)---together representing 437 classification tasks. We provide results showing that our hardness and transferability estimates are strongly correlated with empirical hardness and transferability. As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for highly transferable attributes.
Moment Matching for Multi-Source Domain Adaptation
Authors: Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We make three major contributions towards addressing this problem. First, we collect and annotate by far the largest UDA dataset, called DomainNet, which contains six domains and about 0.6 million images distributed among 345 categories, addressing the gap in data availability for multi-source UDA research. Second, we propose a new deep learning approach, Moment Matching for Multi-Source Domain Adaptation (M3SDA), which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Third, we provide new theoretical insights specifically for moment matching approaches in both single and multiple source domain adaptation. Extensive experiments are conducted to demonstrate the power of our new dataset in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. Dataset and Code are available at http://ai.bu.edu/M3SDA/
Unsupervised Domain Adaptation via Regularized Conditional Alignment
Authors: Safa Cicek, Stefano Soatto
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domains are aligned, but the labels as well. We propose a novel objective function that encourages the class-conditional distributions to have disjoint support in feature space. We further exploit adversarial regularization to improve the performance of the classifier on the domain for which no annotated data is available.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Authors: Ruijia Xu, Guanbin Li, Jihan Yang, Liang Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions. Previous works may not effectively uncover the underlying reasons that would lead to the drastic model degradation on the target task. In this paper, we empirically reveal that the erratic discrimination of the target domain mainly stems from its much smaller feature norms with respect to that of the source domain. To this end, we propose a novel parameter-free Adaptive Feature Norm approach. We demonstrate that progressively adapting the feature norms of the two domains to a large range of values can result in significant transfer gains, implying that those task-specific features with larger norms are more transferable. Our method successfully unifies the computation of both standard and partial domain adaptation with more robustness against the negative transfer issue. Without bells and whistles but a few lines of code, our method substantially lifts the performance on the target task and exceeds state-of-the-arts by a large margin (11.5% on Office-Home and 17.1% on VisDA2017). We hope our simple yet effective approach will shed some light on the future research of transfer learning. Code is available at https://github.com/jihanyang/AFN.