29 accepted papers
Deep neural networks trained with backpropagation have achieved outstanding performance in vision tasks but remain biologically implausible, computationally demanding, and difficult to interpret. The Forward-Forward (FF) algorithm offers a promising alternative by training each layer independently t
A green-learning-based spoofed speech detector that decides whether an input speech sample is bona fide (genuine) or spoofed in an automatic speaker verification (ASV) system, is proposed in this work. The proposed solution, called the green ASVspoof detector (GAD), adopts Wav2vec (version 2.0) spee…
Inspired by the decision learning process of multilayer per-ceptron (MLP) and decision tree (DT), a new classification model, named the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:…
Geometric transformations including translation, rotation, and scaling are commonly used operations in image processing. Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE). Inspired by the synergy, we propose a new KGE model by leveraging all three op…
Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning embeddings for entities and relations through a simple score function. Yet, a higher-dimensional embedding space is usually required for a bette…
We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC, leverages structured gradients from a pre-trained quasi-robust classifier…
Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian point coordinates can be employed to learn features. When input point clouds are not aligned, the classification performan…
A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work. It has three advantages: 1) low computational complexity and a small model size, 2) high detection performance against a wide range of generative models, and 3) mathematical transp…
Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method.…
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r.t. both p(x|y) and p(y). Since the label is inaccessible in the target domain, the conventional adversarial UDA assumes…
Targeting both computing and coding efficiencies, we propose in this work a novel hierarchical bit-wise differential coding scheme to compress point cloud attributes. The encoder firstly quantizes and organizes the points into an octree structure and, for each internal node, picks its attribute(s) f…
A high quality disparity remapping method that preserves 2D shapes and 3D structures, and adjusts disparities of important objects in stereo image pairs is proposed. It is formulated as a constrained optimization problem, whose solution is challenging, since we need to meet multiple requirements of…
Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data a…
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-gr…
Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities…
Language graph learning is an important task with many applications such as text classification, link prediction and community detection. One of the challenges in this domain is finding an efficient way to learn and encode graph into a low dimensional embedding. In this paper, a novel DeepWalk-assis…
Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation). However, as far as we are aware, there are few methods yet to achieve domain adaptation directly on 3D point cloud da…
Much work in robotics has focused on “humanin-the-loop” learning techniques that improve the efficiency of the learning process. However, these algorithms have made the strong assumption of a cooperating human supervisor that assists the robot. In reality, human observers tend to also act in an adve…
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results [??] with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibil…
A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-lab…
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the dif…
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics. In this work, we propose a depth-aware stereo video retargeting method by imposing the depth fidelity constrain…
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though tr…
A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-difference (JND). It measures the first three JND points of 5-second video of resolution 1080p, 720p, 540p and 360p. Based…
In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral networ…
In this paper, we propose the use of recurrent neural networks (RNNs) to develop an effective solution to two problems in electrocardiogram (ECG)-based biometrics: identification/classification and authentication. Different RNN architectures with various parameter settings were evaluated, including…
Based on the notion of just noticeable differences (JND), a stair quality function (SQF) was recently proposed to model human perception on JPEG images. Furthermore, a k-means clustering algorithm was adopted to aggregate JND data collected from multiple subjects to generate a single SQF. In this wo…
Just noticeable difference (JND) reveals the visibility of our human visual system (HVS), below which changes cannot be perceived by the human. Though dozens of JND estimation models have been introduced during the past decade, how to accurately estimate the JND thresholds for different content regi…
The contour-guided color palette (CCP) is proposed for robust image segmentation. It efficiently integrates contour and color cues of an image. To find representative colors of an image, color samples along long contours between regions, similar in spirit to machine learning methodology that focus o…