CVPR 2015 论文列表

This paper aims to highlight vision related tasks centered around "car", which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, "CompCars", that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillancenature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html

In this paper, we present a novel partial signature matching method using graphical models. Shape context features are extracted from the contour of signatures to capture local variations, and K-means clustering is used to build a visual vocabulary from a set of reference signatures. To describe the signatures, supervised latent Dirichlet allocation is used to learn the latent distributions of the salient regions over the visual vocabulary and hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. Our work is evaluated on three datasets derived from the DS-I Tobacco signature dataset with clean signatures and the DS-II UMD dataset with signatures with different degradations. The results show the effectiveness of the approach for both the partial and full signature matching.

The manipulation of panoramic/wide-angle images is usually achieved via image warping. Though various techniques have been developed for preserving shapes and straight lines for warping, these are not sufficient for panoramic/wide-angle images. The image projections will turn the straight lines into curved "geodesic lines", and it is fundamentally impossible to keep all these lines straight. In this work, we propose a geodesic-preserving method for content-aware image warping. An energy term is introduced to preserve the geodesic appearance of the geodesic lines, and can be used with shape-preserving terms. Our method is demonstrated in various applications, including rectangling panoramas, resizing panoramic/wide-angle images, and wide-angle image manipulation. An extension to ellipse preservation for general images is also presented.

The estimation of correspondences between two images resp. point sets is a core problem in computer vision. One way to formulate the problem is graph matching leading to the quadratic assignment problem which is NP-hard. Several so called second order methods have been proposed to solve this problem. In recent years hypergraph matching leading to a third order problem became popular as it allows for better integration of geometric information. For most of these third order algorithms no theoretical guarantees are known. In this paper we propose a general framework for tensor block coordinate ascent methods for hypergraph matching. We propose two algorithms which both come along with the guarantee of monotonic ascent in the matching score on the set of discrete assignment matrices. In the experiments we show that our new algorithms outperform previous work both in terms of achieving better matching scores and matching accuracy. This holds in particular for very challenging settings where one has a high number of outliers and other forms of noise.

This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach.

Effective computational tools for shape analysis are needed in many areas of science and engineering. We address this and propose a new fast iterative algorithm to compute the elastic geodesic distance between shapes of closed planar curves. The original algorithm for this has cubic time complexity with respect to the number of nodes per curve. Hence it is not suitable for large shape data sets. We aim for large-scale shape analysis and thus propose an iterative algorithm based on the original one but with quadratic time complexity. In practice, we observe subquadratic, almost linear running times, and that our algorithm scales very well with large numbers of nodes. The key to our algorithm is the decoupling of the optimization for the starting point and rotation from that of the reparametrization, and the development of fast dynamic programming and iterative nonlinear constrained optimization algorithms that work in tandem to compute optimal reparametrizations fast.

Joint object recognition and pose estimation solely from range images is an important task e.g. in robotics applications and in automated manufacturing environments. The lack of color information and limitations of current commodity depth sensors make this task a challenging computer vision problem, and a standard random sampling based approach is prohibitively time-consuming. We propose to address this difficult problem by generating promising inlier sets for pose estimation by early rejection of clear outliers with the help of local belief propagation (or dynamic programming). By exploiting data-parallelism our method is fast, and we also do not rely on a computationally expensive training phase. We demonstrate state-of-the art performance on a standard dataset and illustrate our approach on challenging real sequences.

We present a new deep network layer called ``Dynamic Convolutional Layer" which is a generalization of the convolutional layer. The conventional convolutional layer uses filters that are learned during training and are held constant during testing. In contrast, the dynamic convolutional layer uses filters that will vary from input to input during testing. This is achieved by learning a function that maps the input to the filters. We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines.

In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkits. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.

Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the MPII Movie Description dataset (MPII-MD) contains a parallel corpus of over 68K sentences and video snippets from 94 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are far more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production.

In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a constrained polynomial optimization, for which a monotonically convergent sequence of tractable convex relaxations can be obtained by exploiting recent developments in sparse polynomial optimization. Further, these results allow for deriving conditions certifying that a finite order relaxation has converged to a solution. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples where the proposed algorithm is shown to outperform existing approaches.

Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hierarchical representation, where each level of the hierarchy represents objects at a different level of granularity. The hierarchical representation prevents performance loss, which is often caused by the increase in the number of parameters (as we consider more tasks to model), and the joint modeling enables resolving ambiguities that exist in independent modeling of these tasks. We augment PASCAL 3D+ dataset with annotations for these tasks and show that our hierarchical model is effective in joint modeling of object detection, 3D pose estimation, and sub-category recognition.

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.

We investigate the problem of estimating the 3D shape of an object, given a set of 2D landmarks in a single image. To alleviate the reconstruction ambiguity, a widely-used approach is to confine the unknown 3D shape within a shape space built upon existing shapes. While this approach has proven to be successful in various applications, a challenging issue remains, i.e., the joint estimation of shape parameters and camera-pose parameters requires to solve a nonconvex optimization problem. The existing methods often adopt an alternating minimization scheme to locally update the parameters, and consequently the solution is sensitive to initialization. In this paper, we propose a convex formulation to address this problem and develop an efficient algorithm to solve the proposed convex program. We demonstrate the exact recovery property of the proposed method, its merits compared to alternative methods, and the applicability in human pose and car shape estimation.

We present a 3D scanning system for deformable objects that uses only a single Kinect sensor. Our work allows considerable amount of nonrigid deformations during scanning, and achieves high quality results without heavily constraining user or camera motion. We do not rely on any prior shape knowledge, enabling general object scanning with freeform deformations. To deal with the drift problem when nonrigidly aligning the input sequence, we automatically detect loop closures, distribute the alignment error over the loop, and finally use a bundle adjustment algorithm to optimize for the latent 3D shape and nonrigid deformation parameters simultaneously. We demonstrate high quality scanning results in some challenging sequences, comparing with state of art nonrigid techniques, as well as ground truth data.

We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.

We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.

Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category. This paper developed a concise deep shape descriptor for the first time to address challenging issues from ever-growing 3D datasets in areas as diverse as engineering, medicine, and biology. Specifically, the proposed approach developed novel techniques to extract concise but geometrically informative shape descriptor, new definitions of Eigen-shape descriptor and Fisher-shape descriptor to guide the training strategy for deep neural network, and deep shape descriptor with discriminative capacity of maximizing the inter-class margin while minimizing the intra-class variance. Our approach addressed the challenges for shape analysis techniques posed by the complexity of 3D model and data representation and geometric structural variations and noise present in 3D models. The experimental results on 3D shape retrieval demonstrate that our proposed deep shape descriptor is superior to other state-of-the-art approaches on handling noise, incompleteness and 3D shape structural variations.