Wasserstein Distance Based Domain Adaptation for Object Detection

In this paper, we present an adversarial unsupervised domain adaptation framework for object detection. Prior approaches utilize adversarial training based on cross entropy between the source and target domain distributions to learn a shared feature mapping that minimizes the domain gap. Here, we minimize the Wasserstein distance between the two distributions instead of cross entropy or Jensen-Shannon divergence to improve the stability of domain adaptation in high-dimensional feature spaces that are inherent to object detection task. Additionally, we remove the exact consistency constraint of the shared feature mapping between the source and target domains, so that the target feature mapping can be optimized independently, which is necessary in the case of significant domain gap. We empirically show that the proposed framework can mitigate domain shift in different scenarios, and provide improved target domain object detection performance.

Towards multi-sequence MR image recovery from undersampled k-space data

Undersampled MR image recovery has been widely studied for accelerated MR acquisition. However, it has been mostly studied under a single sequence scenario, despite the fact that multi-sequence MR scan is common in practice. In this paper, we aim to optimize multi-sequence MR image recovery from undersampled k-space data under an overall time constraint while considering the difference in acquisition time for various sequences. We first formulate it as a constrained optimization problem and then show that finding the optimal sampling strategy for all sequences and the best recovery model at the same time is combinatorial and hence computationally prohibitive. To solve this problem, we propose a blind recovery model that simultaneously recovers multiple sequences, and an efficient approach to find proper combination of sampling strategy and recovery model. Our experiments demonstrate that the proposed method outperforms sequence-wise recovery, and sheds light on how to decide the undersampling strategy for sequences within an overall time budget.

Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement

We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality. Previous methods for slice interpolation only consider data from pairs of adjacent 2D slices. The possibility of fusing information from the direction orthogonal to the 2D slices remains unexplored. Our approach performs MSR in both sagittal and coronal directions, which provides an initial estimate for slice interpolation. The interpolated slices are then fused and refined in the axial direction for improved consistency. Since MSR consists of only 2D operations, it is more feasible in terms of GPU memory consumption and requires fewer training samples compared to 3D CNNs. Our experiments demonstrate that the proposed method outperforms traditional linear interpolation and baseline 2D/3D CNN-based approaches. We conclude by showcasing the method's practical utility in estimating brain volumes from under-sampled brain MR scans through semantic segmentation.

Landmark Detection in Low Resolution Faces with Semi-Supervised Learning

Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images. This deters the performance of algorithms relying on quality landmarks, for example, face recognition. To the best of our knowledge, there does not exist any dataset consisting of low resolution face images along with their annotated landmarks, making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks on low resolution images by learning them from labeled high resolution images. The objective of this work is to show that predicting landmarks directly on low resolution images is more effective than the current practice of aligning images after rescaling or superresolution. In a two-step process, the proposed approach first learns to generate low resolution images by modeling the distribution of target low resolution images. In the second stage, the roles of generated images and real low resolution images are switched and the model learns to predict landmarks for real low resolution images from generated low resolution images. With extensive experimentation, we study the impact of each of the design choices and also show that prediction of landmarks directly on low resolution images improves the performance of important tasks such as face recognition in low resolution images.

DuDoNet: Dual Domain Network for CT Metal Artifact Reduction

Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.

Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

Recent research has resulted in many new techniques that are able to capture the special properties of hyperspectral data for hyperspectral image analysis, with hyperspectral image classification as one of the most active tasks. Time-frequency methods decompose spectra into multi-spectral bands, while hierarchical methods like neural networks incorporate spatial information across scales and model multiple levels of dependencies between spectral features. The Fourier scattering transform is an amalgamation of time-frequency representations with neural network architectures, both of which have recently been proven to provide significant advances in spectral-spatial classification. We test the proposed three dimensional Fourier scattering method on four standard hyperspectral datasets, and present results that indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art spectral-spatial classification methods.

A Dual Path ModelWith Adaptive Attention For Vehicle Re-Identification

In recent years, attention models have been extensively used for person and vehicle re-identification. Most reidentification methods are designed to focus attention at key-point locations. However, depending on the orientation the contribution of each key-point varies. In this paper, we present a novel dual path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention to the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art.

Uncertainty Modeling of Contextual-Connection between Tracklets for Unconstrained Video-based Face Recognition

Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from high-quality faces to low-quality ones through contextual connections, which are constructed based on context such as body appearance. However, previous methods have often propagated erroneous information due to lack of uncertainty modeling of the noisy contextual connections. In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph. UGG explicitly models the uncertainty of the contextual connections by adaptively updating the weights of the edge gates according to the identity distributions of the nodes during inference. UGG is a generic graphical model that can be applied at only inference time or with end-to-end training. We demonstrate the effectiveness of UGG with state-of-the-art results in the recently released challenging Cast Search in Movies and IARPA Janus Surveillance Video Benchmark dataset.

Detecting Human-Object Interactions via Functional Generalization

We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner. The proposed model is simple and uses the visual features of the human, relative spatial orientation of the human and the object, and the knowledge that functionally similar objects take part in similar interactions with humans. We provide extensive experimental validation for our approach and demonstrate state-of-the-art results for HOI detection. On the HICO-Det dataset our method achieves a gain of over 7% absolute points in mean average precision (mAP) over published literature and even a gain of over 2.5% absolute mAP over contemporary work. We also show that our approach leads to significant performance gains for zero-shot HOI detection in the seen object setting. We further demonstrate that using a generic object detector, our model can generalize to interactions involving previously unseen objects.

3DRegNet: A Deep Neural Network for 3D Point Registration

We present 3DRegNet, a deep learning algorithm for the registration of 3D scans. With the recent emergence of inexpensive 3D commodity sensors, it would be beneficial to develop a learning based 3D registration algorithm. Given a set of 3D point correspondences, we build a deep neural network using deep residual layers and convolutional layers to achieve two tasks: (1) classification of the point correspondences into correct/incorrect ones, and (2) regression of the motion parameters that can align the scans into a common reference frame. 3DRegNet has several advantages over classical methods. First, since 3DRegNet works on point correspondences and not on the original scans, our approach is significantly faster than many conventional approaches. Second, we show that the algorithm can be extended for multi-view scenarios, i.e., simultaneous handling of the registration for more than two scans. In contrast to pose regression networks that employ four variables to represent rotation using quaternions, we use Lie algebra to represent the rotation using only three variables. Extensive experiments on two challenging datasets (i.e. ICL-NUIM and SUN3D) demonstrate that we outperform other methods and achieve state-of-the-art results. The code will be made available.