Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' preferences over singular type of user-item interactions. Many practical recommendation scenarios involve multi-typed user interactive behaviors (e.g., page view, add-to-favorite and purchase), which presents unique challenges that cannot be handled by current recommendation solutions. In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions. To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender systems. Specifically, KHGT is build upon a graph-structured neural architecture to i) capture type-specific behavior semantics; ii) explicitly discriminate which types of user-item interactions are more important in assisting the forecasting task on the target behavior. Additionally, we further integrate the multi-modal graph attention layer with temporal encoding strategy, to empower the learned embeddings be reflective of both dedicated multiplex user-item and item-item collaborative relations, as well as the underlying interaction dynamics. Extensive experiments conducted on three real-world datasets show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings. Our implementation is available in https://github.com/akaxlh/KHGT.

Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters provide a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge or even infinite, which is often the case for (subpixel-accurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the PatchMatch method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. This paper presents a generic and fast computational framework for general multi-labeling problems called PatchMatch Filter (PMF). We explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., PatchMatch-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact superpixels, we also propose superpixel-based novel search strategies that generalize and improve the original PatchMatch method. Further motivated to improve the regularization strength, we propose a simple yet effective cross-scale consistency constraint, which handles labeling estimation for large low-textured regions more reliably than a single-scale PMF algorithm. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve top-tier correspondence accuracy but run much faster than other related competing methods, often giving over 10-100 times speedup.

Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: DAISY descriptors, filter-based efficient flow inference, and the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.

Accurate rendering of 3D spatial audio for interactive virtual auditory displays requires the use of personalized head-related transfer functions (HRTFs). We present a new approach to compute personalized HRTFs for any individual using a method that combines state-of-the-art image-based 3D modeling with an efficient numerical simulation pipeline. Our 3D modeling framework enables capture of the listener's head and torso using consumer-grade digital cameras to estimate a high-resolution non-parametric surface representation of the head, including the extended vicinity of the listener's ear. We leverage sparse structure from motion and dense surface reconstruction techniques to generate a 3D mesh. This mesh is used as input to a numeric sound propagation solver, which uses acoustic reciprocity and Kirchhoff surface integral representation to efficiently compute an individual's personalized HRTF. The overall computation takes tens of minutes on multi-core desktop machine. We have used our approach to compute the personalized HRTFs of few individuals, and we present our preliminary evaluation here. To the best of our knowledge, this is the first commodity technique that can be used to compute personalized HRTFs in a lab or home setting.

Estimating dense correspondence or depth information from a pair of stereoscopic images is a fundamental problem in computer vision, which finds a range of important applications. Despite intensive past research efforts in this topic, it still remains challenging to recover the depth information both reliably and efficiently, especially when the input images contain weakly textured regions or are captured under uncontrolled, real-life conditions. Striking a desired balance between computational efficiency and estimation quality, a hybrid minimum spanning tree-based stereo matching method is proposed in this paper. Our method performs efficient nonlocal cost aggregation at pixel-level and region-level, and then adaptively fuses the resulting costs together to leverage their respective strength in handling large textureless regions and fine depth discontinuities. Experiments on the standard Middlebury stereo benchmark show that the proposed stereo method outperforms all prior local and nonlocal aggregation-based methods, achieving particularly noticeable improvements for low texture regions. To further demonstrate the effectiveness of the proposed stereo method, also motivated by the increasing desire to generate expressive depth-induced photo effects, this paper is tasked next to address the emerging application of interactive depth-of-field rendering given a real-world stereo image pair. To this end, we propose an accurate thin-lens model for synthetic depth-of-field rendering, which considers the user-stroke placement and camera-specific parameters and performs the pixel-adapted Gaussian blurring in a principled way. Taking ~1.5 s to process a pair of 640×360 images in the off-line step, our system named Scribble2focus allows users to interactively select in-focus regions by simple strokes using the touch screen and returns the synthetically refocused images instantly to the user.

Though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge, which is often the case for (sub pixel-accurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the Patch Match method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. This paper presents a generic and fast computational framework for general multi-labeling problems called Patch Match Filter (PMF). For the very first time, we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., Based-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact super pixels, we also propose super pixel-based novel search strategies that generalize and improve the original Patch Match method. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve state-of-the-art correspondence accuracy but run much faster than other competing methods, often giving over 10-times speedup for large label space cases.

This paper presents an Immersive Telepresence system for Entertainment and Meetings (ITEM). The system aims to provide a radically new video communication experience by seamlessly merging participants into the same virtual space to allow a natural interaction among them and shared collaborative contents. With the goal to make a scalable, flexible system for various business solutions as well as easily accessible by massive consumers, we address the challenges in the whole pipeline of media processing, communication, and displaying in our design and realization of such a system. Extensive experiments show the developed system runs reliably and comfortably in real time with a minimal setup requirement (e.g., a webcam, a laptop/desktop connected to the public Internet) for tele-immersive video communication. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by ITEM.

We present a video stylization and sharing system named QuickToon, which supports generating a variety of useful and pleasing visual effects and allows easy use in video chat or sharing via social networking services (SNS). Based on our highly efficient edge-preserving smoothing filter, non-photo-realistic video rendering effects can be generated on the fly such as skin beautification, cartoon-like rendition, object outline, pencil sketch, and color stroke. Without requiring any GPUs that would otherwise seriously limits portability, the QuickToon system runs comfortably in real time for VGA- or HD-sized videos on general processors such as CPUs. The system can take in video frames either from a live webcam feed or any photos/videos from the local store, while the transformed imagery can be used in a live Skype video call, or saved locally, or uploaded and shared to SNS with one click. We demonstrate with concrete examples the functionality of this system, and underline its utility in video communication and photo/video sharing.