mtybilly/Instruct2DS huggingface dataset:
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Authors: Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, Chunhong Pan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.
AMP: Adaptive Masked Proxies for Few-Shot Segmentation
Authors: Mennatullah Siam, Boris N. Oreshkin, Martin Jagersand
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-5^i dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at https://github.com/MSiam/AdaptiveMaskedProxies.
Universal Semi-Supervised Semantic Segmentation
Authors: Tarun Kalluri, Girish Varma, Manmohan Chandraker, C.V. Jawahar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limits the quantity of labels available for training in any domain, while deployment is easier if a single model works well across domains. In this paper, we pose the novel problem of universal semi-supervised semantic segmentation and propose a solution framework, to meet the dual needs of lower annotation and deployment costs. In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed, (ii) unlabeled data is used, (iii) performance is improved, (iv) only a few labels are needed and (v) label spaces may differ. To address this, we minimize supervised as well as within and cross-domain unsupervised losses, introducing a novel feature alignment objective based on pixel-aware entropy regularization for the latter. We demonstrate quantitative advantages over other approaches on several combinations of segmentation datasets across different geographies (Germany, England, India) and environments (outdoors, indoors), as well as qualitative insights on the aligned representations.
Accelerate Learning of Deep Hashing With Gradient Attention
Authors: Long-Kai Huang, Jianda Chen, Sinno Jialin Pan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recent years have witnessed the success of learning to hash in fast large-scale image retrieval. As deep learning has shown its superior performance on many computer vision applications, recent designs of learning-based hashing models have been moving from shallow ones to deep architectures. However, based on our analysis, we find that gradient descent based algorithms used in deep hashing models would potentially cause hash codes of a pair of training instances to be updated towards the directions of each other simultaneously during optimization. In the worst case, the paired hash codes switch their directions after update, and consequently, their corresponding distance in the Hamming space remain unchanged. This makes the overall learning process highly inefficient. To address this issue, we propose a new deep hashing model integrated with a novel gradient attention mechanism. Extensive experimental results on three benchmark datasets show that our proposed algorithm is able to accelerate the learning process and obtain competitive retrieval performance compared with state-of-the-art deep hashing models.
SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval
Authors: Qing-Yuan Jiang, Yi He, Gen Li, Jian Lin, Lei Li, Wu-Jun Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: With the explosive growth of video data in real applications, near-duplicate video retrieval (NDVR) has become indispensable and challenging, especially for short videos. However, all existing NDVR datasets are introduced for long videos. Furthermore, most of them are small-scale and lack of diversity due to the high cost of collecting and labeling near-duplicate videos. In this paper, we introduce a large-scale short video dataset, called SVD, for the NDVR task. SVD contains over 500,000 short videos and over 30,000 labeled videos of near-duplicates. We use multiple video mining techniques to construct positive/negative pairs. Furthermore, we design temporal and spatial transformations to mimic user-attack behavior in real applications for constructing more difficult variants of SVD. Experiments show that existing state-of-the-art NDVR methods, including real-value based and hashing based methods, fail to achieve satisfactory performance on this challenging dataset. The release of SVD dataset will foster research and system engineering in the NDVR area. The SVD dataset is available at https://svdbase.github.io.
Block Annotation: Better Image Annotation With Sub-Image Decomposition
Authors: Hubert Lin, Paul Upchurch, Kavita Bala
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Image datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are annotated. However, full-image annotations are expensive, with experts spending up to 90 minutes per image. We propose block sub-image annotation as a replacement for full-image annotation. Despite the attention cost of frequent task switching, we find that block annotations can be crowdsourced at higher quality compared to full-image annotation with equal monetary cost using existing annotation tools developed for full-image annotation. Surprisingly, we find that 50% pixels annotated with blocks allows semantic segmentation to achieve equivalent performance to 100% pixels annotated. Furthermore, as little as 12% of pixels annotated allows performance as high as 98% of the performance with dense annotation. In weakly-supervised settings, block annotation outperforms existing methods by 3-4% (absolute) given equivalent annotation time. To recover the necessary global structure for applications such as characterizing spatial context and affordance relationships, we propose an effective method to inpaint block-annotated images with high-quality labels without additional human effort. As such, fewer annotations can also be used for these applications compared to full-image annotation.
Probabilistic Deep Ordinal Regression Based on Gaussian Processes
Authors: Yanzhu Liu, Fan Wang, Adams Wai Kin Kong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: With excellent representation power for complex data, deep neural networks (DNNs) based approaches are state-of-the-art for ordinal regression problem which aims to classify instances into ordinal categories. However, DNNs are not able to capture uncertainties and produce probabilistic interpretations. As a probabilistic model, Gaussian Processes (GPs) on the other hand offers uncertainty information, which is nonetheless lack of scalability for large datasets. This paper adapts traditional GPs regression for ordinal regression problem by using both conjugate and non-conjugate ordinal likelihood. Based on that, it proposes a deep neural network with a GPs layer on the top, which is trained end-to-end by the stochastic gradient descent method for both neural network parameters and GPs parameters. The parameters in the ordinal likelihood function are learned as neural network parameters so that the proposed framework is able to produce fitted likelihood functions for training sets and make probabilistic predictions for test points. Experimental results on three real-world benchmarks -- image aesthetics rating, historical image grading and age group estimation -- demonstrate that in terms of mean absolute error, the proposed approach outperforms state-of-the-art ordinal regression approaches and provides the confidence for predictions.
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
Authors: Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables -such as gender- in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network - and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.
Teacher Guided Architecture Search
Authors: Pouya Bashivan, Mark Tensen, James J. DiCarlo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Much of the recent improvement in neural networks for computer vision has resulted from discovery of new networks architectures. Most prior work has used the performance of candidate models following limited training to automatically guide the search in a feasible way. Could further gains in computational efficiency be achieved by guiding the search via measurements of a high performing network with unknown detailed architecture (e.g. the primate visual system)? As one step toward this goal, we use representational similarity analysis to evaluate the similarity of internal activations of candidate networks with those of a (fixed, high performing) teacher network. We show that adopting this evaluation metric could produce up to an order of magnitude in search efficiency over performance-guided methods. Our approach finds a convolutional cell structure with similar performance as was previously found using other methods but at a total computational cost that is two orders of magnitude lower than Neural Architecture Search (NAS) and more than four times lower than progressive neural architecture search (PNAS). We further show that measurements from only  300 neurons from primate visual system provides enough signal to find a network with an Imagenet top-1 error that is significantly lower than that achieved by performance-guided architecture search alone. These results suggest that representational matching can be used to accelerate network architecture search in cases where one has access to some or all of the internal representations of a teacher network of interest, such as the brain's sensory processing networks.
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
Authors: David Smith, Matthew Loper, Xiaochen Hu, Paris Mavroidis, Javier Romero
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Current methods for body shape estimation either lack detail or require many images. They are usually architecturally complex and computationally expensive. We propose FACSIMILE (FAX), a method that estimates a detailed body from a single photo, lowering the bar for creating virtual representations of humans. Our approach is easy to implement and fast to execute, making it easily deployable. FAX uses an image-translation network which recovers geometry at the original resolution of the image. Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision. We evaluate our approach both qualitatively and quantitatively, and compare with a state-of-the-art method.
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild
Authors: Yu Rong, Ziwei Liu, Cheng Li, Kaidi Cao, Chen Change Loy
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Though much progress has been achieved in single-image 3D human recovery, estimating 3D model for in-the-wild images remains a formidable challenge. The reason lies in the fact that obtaining high-quality 3D annotations for in-the-wild images is an extremely hard task that consumes enormous amount of resources and manpower. To tackle this problem, previous methods adopt a hybrid training strategy that exploits multiple heterogeneous types of annotations including 3D and 2D while leaving the efficacy of each annotation not thoroughly investigated. In this work, we aim to perform a comprehensive study on cost and effectiveness trade-off between different annotations. Specifically, we focus on the challenging task of in-the-wild 3D human recovery from single images when paired 3D annotations are not fully available. Through extensive experiments, we obtain several observations: 1) 3D annotations are efficient, whereas traditional 2D annotations such as 2D keypoints and body part segmentation are less competent in guiding 3D human recovery. 2) Dense Correspondence such as DensePose is effective. When there are no paired in-the-wild 3D annotations available, the model exploiting dense correspondence can achieve 92% of the performance compared to a model trained with paired 3D data. We show that incorporating dense correspondence into in-the-wild 3D human recovery is promising and competitive due to its high efficiency and relatively low annotating cost. Our model trained with dense correspondence can serve as a strong reference for future research.
Human Mesh Recovery From Monocular Images via a Skeleton-Disentangled Representation
Authors: Yu Sun, Yun Ye, Wu Liu, Wenpeng Gao, Yili Fu, Tao Mei
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We describe an end-to-end method for recovering 3D human body mesh from single images and monocular videos. Different from the existing methods try to obtain all the complex 3D pose, shape, and camera parameters from one coupling feature, we propose a skeleton-disentangling based framework, which divides this task into multi-level spatial and temporal granularity in a decoupling manner. In spatial, we propose an effective and pluggable "disentangling the skeleton from the details" (DSD) module. It reduces the complexity and decouples the skeleton, which lays a good foundation for temporal modeling. In temporal, the self-attention based temporal convolution network is proposed to efficiently exploit the short and long-term temporal cues. Furthermore, an unsupervised adversarial training strategy, temporal shuffles and order recovery, is designed to promote the learning of motion dynamics. The proposed method outperforms the state-of-the-art 3D human mesh recovery methods by 15.4% MPJPE and 23.8% PA-MPJPE on Human3.6M. State-of-the-art results are also achieved on the 3D pose in the wild (3DPW) dataset without any fine-tuning. Especially, ablation studies demonstrate that skeleton-disentangled representation is crucial for better temporal modeling and generalization.
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images "In the Wild"
Authors: Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, Michael J. Black
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered species in Africa, with only a few thousand individuals left. Capturing the shape and pose of these animals can provide biologists and conservationists with information about animal health and behavior. In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other. To overcome these challenges, we integrate the recent SMAL animal model into a network-based regression pipeline, which we train end-to-end on synthetically generated images with pose, shape, and background variation. Going beyond state-of-the-art methods for human shape and pose estimation, our method learns a shape space for zebras during training. Learning such a shape space from images using only a photometric loss is novel, and the approach can be used to learn shape in other settings with limited 3D supervision. Moreover, we couple 3D pose and shape prediction with the task of texture synthesis, obtaining a full texture map of the animal from a single image. We show that the predicted texture map allows a novel per-instance unsupervised optimization over the network features. This method, SMALST (SMAL with learned Shape and Texture) goes beyond previous work, which assumed manual keypoints and/or segmentation, to regress directly from pixels to 3D animal shape, pose and texture.
Object-Driven Multi-Layer Scene Decomposition From a Single Image
Authors: Helisa Dhamo, Nassir Navab, Federico Tombari
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a method that tackles the challenge of predicting color and depth behind the visible content of an image. Our approach aims at building up a Layered Depth Image (LDI) from a single RGB input, which is an efficient representation that arranges the scene in layers, including originally occluded regions. Unlike previous work, we enable an adaptive scheme for the number of layers and incorporate semantic encoding for better hallucination of partly occluded objects. Additionally, our approach is object-driven, which especially boosts the accuracy for the occluded intermediate objects. The framework consists of two steps. First, we individually complete each object in terms of color and depth, while estimating the scene layout. Second, we rebuild the scene based on the regressed layers and enforce the recomposed image to resemble the structure of the original input. The learned representation enables various applications, such as 3D photography and diminished reality, all from a single RGB image.
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
Authors: Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Deep learning based 3D reconstruction techniques have recently achieved impressive results. However, while state-of-the-art methods are able to output complex 3D geometry, it is not clear how to extend these results to time-varying topologies. Approaches treating each time step individually lack continuity and exhibit slow inference, while traditional 4D reconstruction methods often utilize a template model or discretize the 4D space at fixed resolution. In this work, we present Occupancy Flow, a novel spatio-temporal representation of time-varying 3D geometry with implicit correspondences. Towards this goal, we learn a temporally and spatially continuous vector field which assigns a motion vector to every point in space and time. In order to perform dense 4D reconstruction from images or sparse point clouds, we combine our method with a continuous 3D representation. Implicitly, our model yields correspondences over time, thus enabling fast inference while providing a sound physical description of the temporal dynamics. We show that our method can be used for interpolation and reconstruction tasks, and demonstrate the accuracy of the learned correspondences. We believe that Occupancy Flow is a promising new 4D representation which will be useful for a variety of spatio-temporal reconstruction tasks.
Joint Monocular 3D Vehicle Detection and Tracking
Authors: Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krahenbuhl, Trevor Darrell, Fisher Yu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper, we propose a novel online framework for 3D vehicle detection and tracking from monocular videos. The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform. Our method leverages 3D box depth-ordering matching for robust instance association and utilizes 3D trajectory prediction for re-identification of occluded vehicles. We also design a motion learning module based on an LSTM for more accurate long-term motion extrapolation. Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods.
Fingerspelling Recognition in the Wild With Iterative Visual Attention
Authors: Bowen Shi, Aurora Martinez Del Rio, Jonathan Keane, Diane Brentari, Greg Shakhnarovich, Karen Livescu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It out-performs prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.
PointAE: Point Auto-Encoder for 3D Statistical Shape and Texture Modelling
Authors: Hang Dai, Ling Shao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: The outcome of standard statistical shape modelling is a vector space representation of objects. Any convex combination of vectors of a set of object class examples generates a real and valid example. In this paper, we propose a Point Auto-Encoder (PointAE) with skip-connection, attention blocks for 3D statistical shape modelling directly on 3D points. The proposed PointAE is able to refine the correspondence with a correspondence refinement block. The data with refined correspondence can be fed to the PointAE again and bootstrap the constructed statistical models. Instead of two seperate models, PointAE can simultaneously model the shape and texture variation. The extensive evaluation in three open-sourced datasets demonstrates that the proposed method achieves better performance in representation ability of the shape variations.
Multi-Garment Net: Learning to Dress 3D People From Images
Authors: Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, Gerard Pons-Moll
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video. Several experiments demonstrate that this representation allows higher level of control when compared to single mesh or voxel representations of shape. Our model allows to predict garment geometry, relate it to the body shape, and transfer it to new body shapes and poses. To train MGN, we leverage a digital wardrobe containing 712 digital garments in correspondence, obtained with a novel method to register a set of clothing templates to a dataset of real 3D scans of people in different clothing and poses. Garments from the digital wardrobe, or predicted by MGN, can be used to dress any body shape in arbitrary poses. We will make publicly available the digital wardrobe, the MGN model, and code to dress SMPL with the garments at https://virtualhumans.mpi-inf.mpg.de/mgn
Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds
Authors: Haiyong Jiang, Jianfei Cai, Jianmin Zheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This work addresses the problem of 3D human shape reconstruction from point clouds. Considering that human shapes are of high dimensions and with large articulations, we adopt the state-of-the-art parametric human body model, SMPL, to reduce the dimension of learning space and generate smooth and valid reconstruction. However, SMPL parameters, especially pose parameters, are not easy to learn because of ambiguity and locality of the pose representation. Thus, we propose to incorporate skeleton awareness into the deep learning based regression of SMPL parameters for 3D human shape reconstruction. Our basic idea is to use the state-of-the-art technique PointNet++ to extract point features, and then map point features to skeleton joint features and finally to SMPL parameters for the reconstruction from point clouds. Particularly, we develop an end-to-end framework, where we propose a graph aggregation module to augment PointNet++ by extracting better point features, an attention module to better map unordered point features into ordered skeleton joint features, and a skeleton graph module to extract better joint features for SMPL parameter regression. The entire framework network is first trained in an end-to-end manner on synthesized dataset, and then online fine-tuned on unseen dataset with unsupervised loss to bridges gaps between training and testing. The experiments on multiple datasets show that our method is on par with the state-of-the-art solution.
AMASS: Archive of Motion Capture As Surface Shapes
Authors: Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset. To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization. We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model. Here we use SMPL [Loper et al., 2015], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh. The method works for arbitrary marker sets, while recovering soft-tissue dynamics and realistic hand motion. We evaluate MoSh++ and tune its hyperparameters using a new dataset of 4D body scans that are jointly recorded with markerbased mocap. The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning. Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11000 motions, and will be publicly available to the research community.
Person-in-WiFi: Fine-Grained Person Perception Using WiFi
Authors: Fei Wang, Sanping Zhou, Stanislav Panev, Jinsong Han, Dong Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Fine-grained person perception such as body segmentation and pose estimation has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars (e.g. RF-Pose), and LiDARs. These solutions require 2D images, depth maps or 3D point clouds of person bodies as input. In this paper, we take one step forward to show that fine-grained person perception is possible even with 1D sensors: WiFi antennas. Specifically, we used two sets of WiFi antennas to acquire signals, i.e., one transmitter set and one receiver set. Each set contains three antennas horizontally lined-up as a regular household WiFi router. The WiFi signal generated by a transmitter antenna, penetrates through and reflects on human bodies, furniture, and walls, and then superposes at a receiver antenna as 1D signal samples. We developed a deep learning approach that uses annotations on 2D images, takes the received 1D WiFi signals as input, and performs body segmentation and pose estimation in an end-to-end manner. To our knowledge, our solution is the first work based on off-the-shelf WiFi antennas and standard IEEE 802.11n WiFi signals. Demonstrating comparable results to image-based solutions, our WiFi-based person perception solution is cheaper and more ubiquitous than radars and LiDARs, while invariant to illumination and has little privacy concern comparing to cameras.
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
Authors: Keqiang Sun, Wayne Wu, Tinghao Liu, Shuo Yang, Quan Wang, Qiang Zhou, Zuochang Ye, Chen Qian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recently, facial landmark detection algorithms have achieved remarkable performance on static images. However, these algorithms are neither accurate nor stable in motion-blurred videos. The missing of structure information makes it difficult for state-of-the-art facial landmark detection algorithms to yield good results. In this paper, we propose a framework named FAB that takes advantage of structure consistency in the temporal dimension for facial landmark detection in motion-blurred videos. A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior. This allows our framework to work as a virtuous circle. On one hand, the geometry prior helps our structure-aware deblurring network generates high quality deblurred images which lead to better landmark detection results. On the other hand, better landmark detection results help structure predictor generate better geometry prior for the next frame. Moreover, it is a flexible video-based framework that can incorporate any static image-based methods to provide a performance boost on video datasets. Extensive experiments on Blurred-300VW, the proposed Real-world Motion Blur (RWMB) datasets and 300VW demonstrate the superior performance to the state-of-the-art methods. Datasets and model will be publicly available at  https://github.com/KeqiangSun/FAB  https://github.com/KeqiangSun/FAB .
Attentional Feature-Pair Relation Networks for Accurate Face Recognition
Authors: Bong-Nam Kang, Yonghyun Kim, Bongjin Jun, Daijin Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.
Action Recognition With Spatial-Temporal Discriminative Filter Banks
Authors: Brais Martinez, Davide Modolo, Yuanjun Xiong, Joseph Tighe
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Action recognition has seen a dramatic performance improvement in the last few years. Most of the current state-of-the-art literature either aims at improving performance through changes to the backbone CNN network, or exploring different trade-offs between computational efficiency and performance, again through altering the backbone network. However, almost all of these works maintain the same last layers of the network, which simply consist of a global average pooling followed by a fully connected layer. In this work we focus on how to improve the representation capacity of the network, but rather than altering the backbone, we focus on improving the last layers of the network, where changes have low impact in terms of computational cost. In particular, we hypothesize that current architectures have poor sensitivity to finer details and we exploit recent advances in the fine-grained recognition literature to improve our model in this aspect. With the proposed approach, we obtain state-of-the-art performance on Kinetics-400 and Something-Something-V1, the two major large-scale action recognition benchmarks.
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
Authors: Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, Dima Damen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i.e. the combination of modalities within a range of temporal offsets. We train the architecture with three modalities -- RGB, Flow and Audio -- and combine them with mid-level fusion alongside sparse temporal sampling of fused representations. In contrast with previous works, modalities are fused before temporal aggregation, with shared modality fusion weights over time. Our proposed architecture is trained end-to-end, outperforming individual modalities as well as late-fusion of modalities. We demonstrate the importance of audio in egocentric vision, on per-class basis, for identifying actions as well as interacting objects. Our method achieves state of the art results on both the seen and unseen test sets of the largest egocentric dataset: EPIC-Kitchens, on all metrics using the public leaderboard.
Weakly-Supervised Action Localization With Background Modeling
Authors: Phuc Xuan Nguyen, Deva Ramanan, Charless C. Fowlkes
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground and background frames who's appearance is explicitly modeled. Most prior work ignores the background, but we show that modeling it allows our system to learn a richer notions of actions and their temporal extents. Second, we combine bottom-up, class-agnostic attention modules with top-down, class-specific activation maps, using the latter as form of self-supervision for the former. Doing so allows our model to learn a more accurate model of attention without explicit temporal supervision. These modifications lead to  10% AP@IoU=0.5 improvement over existing systems on THUMOS14. Our proposed weakly-supervised system outperforms the recent state-of-the-art by at least 4.3% AP@IoU=0.5. Finally, we demonstrate that weakly-supervised learning can be used to aggressively scale-up learning to in-the-wild, uncurated Instagram videos (where relevant frames and videos are automatically selected through attentional processing). This allows our weakly supervised approach to even outperform fully-supervised methods for action detection at some overlap thresholds.
Grouped Spatial-Temporal Aggregation for Efficient Action Recognition
Authors: Chenxu Luo, Alan L. Yuille
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Temporal reasoning is an important aspect of video analysis. 3D CNN shows good performance by exploring spatial-temporal features jointly in an unconstrained way, but it also increases the computational cost a lot. Previous works try to reduce the complexity by decoupling the spatial and temporal filters. In this paper, we propose a novel decomposition method that decomposes the feature channels into spatial and temporal groups in parallel. This decomposition can make two groups focus on static and dynamic cues separately. We call this grouped spatial-temporal aggregation (GST). This decomposition is more parameter-efficient and enables us to quantitatively analyze the contributions of spatial and temporal features in different layers. We verify our model on several action recognition tasks that require temporal reasoning and show its effectiveness.
Temporal Structure Mining for Weakly Supervised Action Detection
Authors: Tan Yu, Zhou Ren, Yuncheng Li, Enxu Yan, Ning Xu, Junsong Yuan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Different from the fully-supervised action detection problem that is dependent on expensive frame-level annotations, weakly supervised action detection (WSAD) only needs video-level annotations, making it more practical for real-world applications. Existing WSAD methods detect action instances by scoring each video segment (a stack of frames) individually. Most of them fail to model the temporal relations among video segments and cannot effectively characterize action instances possessing latent temporal structure. To alleviate this problem in WSAD, we propose the temporal structure mining (TSM) approach. In TSM, each action instance is modeled as a multi-phase process and phase evolving within an action instance, i.e., the temporal structure, is exploited. Meanwhile, the video background is modeled by a background phase, which separates different action instances in an untrimmed video. In this framework, phase filters are used to calculate the confidence scores of the presence of an action's phases in each segment. Since in the WSAD task, frame-level annotations are not available and thus phase filters cannot be trained directly. To tackle the challenge, we treat each segment's phase as a hidden variable. We use segments' confidence scores from each phase filter to construct a table and determine hidden variables, i.e., phases of segments, by a maximal circulant path discovery along the table. Experiments conducted on three benchmark datasets demonstrate the state-of-the-art performance of the proposed TSM.
Temporal Recurrent Networks for Online Action Detection
Authors: Mingze Xu, Mingfei Gao, Yi-Ting Chen, Larry S. Davis, David J. Crandall
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, the Temporal Recurrent Network (TRN), to model greater temporal context of each frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art.
StartNet: Online Detection of Action Start in Untrimmed Videos
Authors: Mingfei Gao, Mingze Xu, Larry S. Davis, Richard Socher, Caiming Xiong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos. Previous methods aim to localize action starts by learning feature representations that can directly separate the start point from its preceding background. It is challenging due to the subtle appearance difference near the action starts and the lack of training data. Instead, StartNet decomposes ODAS into two stages: action classification (using ClsNet) and start point localization (using LocNet). ClsNet focuses on per-frame labeling and predicts action score distributions online. Based on the predicted action scores of the past and current frames, LocNet conducts class-agnostic start detection by optimizing long-term localization rewards using policy gradient methods. The proposed framework is validated on two large-scale datasets, THUMOS'14 and ActivityNet. The experimental results show that StartNet significantly outperforms the state-of-the-art by 15%-30% p-mAP under the offset tolerance of 1-10 seconds on THUMOS'14, and achieves comparable performance on ActivityNet with 10 times smaller time offset.
Video Classification With Channel-Separated Convolutional Networks
Authors: Du Tran, Heng Wang, Lorenzo Torresani, Matt Feiszli
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Group convolution has been shown to offer great computational savings in various 2D convolutional architectures for image classification. It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks. This paper studies the effects of different design choices in 3D group convolutional networks for video classification. We empirically demonstrate that the amount of channel interactions plays an important role in the accuracy of 3D group convolutional networks. Our experiments suggest two main findings. First, it is a good practice to factorize 3D convolutions by separating channel interactions and spatiotemporal interactions as this leads to improved accuracy and lower computational cost. Second, 3D channel-separated convolutions provide a form of regularization, yielding lower training accuracy but higher test accuracy compared to 3D convolutions. These two empirical findings lead us to design an architecture -- Channel-Separated Convolutional Network (CSN) -- which is simple, efficient, yet accurate. On Sports1M and Kinetics, our CSNs are comparable with or better than the state-of-the-art while being 2-3 times more efficient.
Predicting the Future: A Jointly Learnt Model for Action Anticipation
Authors: Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future. In contrast to current state-of-the-art methods which first learn a model to predict future video features and then perform action anticipation using these features, the proposed framework jointly learns to perform the two tasks, future visual and temporal representation synthesis, and early action anticipation. The joint learning framework ensures that the predicted future embeddings are informative to the action anticipation task. Furthermore, through extensive experimental evaluations we demonstrate the utility of using both visual and temporal semantics of the scene, and illustrate how this representation synthesis could be achieved through a recurrent Generative Adversarial Network (GAN) framework. Our model outperforms the current state-of-the-art methods on multiple datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction.