OpenRaiser/Intern-Atlas huggingface dataset:
Explanation as a defense of recommendation
Authors: Aobo Yang, Nan Wang, Hongbo Deng, Hongning Wang
Venue: Conference WSDM 2021
Year: 2021

Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.
Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
Authors: Yurou Zhao, Yiding Sun, Ruidong Han, Fei Jiang, Lu Guan, Xiang Li, Wei Lin, Weizhi Ma, Jiaxin Mao
Venue: Conference CIKM 2024
Year: 2024

Providing natural language-based explanations to justify recommendations helps to improve users' satisfaction and gain users' trust. However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, the generated explanations are often not aligned with the predicted ratings or some important features of the recommended items, and thus, are suboptimal in helping users make informed decision on the recommendation platform. To tackle this problem, we propose a flexible model-agnostic method named MMI (Maximizing Mutual Information) framework to enhance the alignment between the generated natural language explanations and the predicted rating/important item features. Specifically, we propose to use mutual information (MI) as a measure for the alignment and train a neural MI estimator. Then, we treat a well-trained explanation generation model as the backbone model and further fine-tune it through reinforcement learning with guidance from the MI estimator, which rewards a generated explanation that is more aligned with the predicted rating or a pre-defined feature of the recommended item. Experiments on three datasets demonstrate that our MMI framework can boost different backbone models, enabling them to outperform existing baselines in terms of alignment with predicted ratings and item features. Additionally, user studies verify that MI-enhanced explanations indeed facilitate users' decisions and are favorable compared with other baselines due to their better alignment properties.
Clenshaw Graph Neural Networks
Authors: Yuhe Guo, Zhewei Wei
Venue: Conference KDD 2023
Year: 2023

Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational spatial methods for learning graph representations. Polynomial filters, which have an advantage on heterophilous graphs, are motivated differently from the spectral perspective of graph convolutions. Recent spatial GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. However, current residual connections do not effectively harness the full potential of polynomial filters, which are commonly employed in the spectral domain of GNNs.
Vector valued active contours
Authors: Guillermo Sapiro
Venue: Conference CVPR 1996
Year: 1996

A framework for object segmentation in vector-valued images is presented in this paper. The first scheme proposed is based on geometric active contours moving towards the objects to be detected in the vector-valued image. Objects boundaries are obtained as geodesics or minimal weighted distance curves in a Riemannian space. The metric in this space is given by a definition of edges in vector-valued images. The curve flow corresponding to the proposed active contours holds formal existence, uniqueness, stability, and correctness results. The technique is applicable for example to color and texture images. The scheme automatically handles changes in the deforming curve topology. We conclude the paper presenting an extension of the color active contours which leads to a possible image flow for vector-valued image segmentation. The algorithm is based on moving each one of the image level-sets according to the proposed color active contours. This extension also shows the relation of the color geodesic active contours with a number of partial-differential-equations based image processing algorithms as anisotropic diffusion and shock filters.
Focus: Searching for multi-colored objects in a diverse image database
Authors: Madirakshi Das, Edward M. Riseman, Bruce A. Draper
Venue: Conference CVPR 1997
Year: 1997

We describe a new multi-phase, color-based image retrieval system, FOCUS (Fast Object Color-based qUery System), with an online user interface which is capable of identifying multi-colored query objects in an image in the presence of significant, interfering backgrounds. The query object may occur in arbitrary sizes, orientations and locations in the database images. The color features used to describe an image have been developed based on the need for speed in matching and ease of computation on complex images while maintaining the scale and rotation invariance properties. The first phase matches the color content of an image computed as the peaks in the color histogram of the image, with the query object colors using an efficient indexing mechanism. The second phase matches the spatial relationships between color regions in the image with the query using a spatial proximity graph (SPG) structure designed for the purpose. The method is fast and has low storage overhead. Test results with multi-colored query objects from artificial and natural domains show that FOCUS is quite effective in handling interfering backgrounds and large variations in scale. The experimental results on a database of diverse images highlights the capabilities of the system.
Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach
Authors: Aleix M. Martínez
Venue: Conference CVPR 2000
Year: 2000

New face recognition approaches are needed, because although much progress has been recently achieved in the field (e.g. within the eigenspace domain), still many problems are to be robustly solved. Two of these problems are occlusions and the imprecise localization of faces (which ultimately imply a failure in identification). While little has been done to account for the first problem, almost nothing has been proposed to account for the second. This paper presents a probabilistic approach that attempts to solve both problems while using an eigenspace representation. To resolve the localization problem, we need to find the subspace (within the feature space, e.g. eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into n local regions which are analyzed in isolation. In contrast with other previous approaches, where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. Our method has proven to be superior to a local voting PCA on a set of 2600 face images.
Undoing Paper Curl Distortion Using Applicable Surfaces
Authors: Maurizio Pilu
Venue: Conference CVPR 2001
Year: 2001

The work presented is concerned with the reconstruction of the original undistorted image of a curled document or book when captured face-up by a camera with the support of sparse depth measurements. A novel method based on physically modeling paper deformation with an applicable surface is proposed and a relaxation algorithm is described that allows us to fit it to noisy data and then flatten it in order to produce the final undistorted image. The promising results obtained confirm that the use of applicable surfaces is the right way to address this problem.
An integrated framework for image segmentation and perceptual grouping
Authors: Zhuowen Tu
Venue: Conference ICCV 2005
Year: 2005

This paper presents an efficient algorithm for image segmentation and a framework for perceptual grouping. It makes an attempt to provide one way of combining bottom-up and top-down approaches. In image segmentation, it generalizes the Swendsen-Wang cut algorithm (SWC) by Barbu and Zhu (2003) to make both 2-way and m-way cuts, and includes topology change processes (graph repartitioning and boundary diffusion). The method directly works at a low temperature without using annealing. We show that it is much faster than the DDMCMC approach (Tu and Zhu, 2002) and more robust than the SWC method. The results are demonstrated on the Berkeley data set. In perceptual grouping, it integrates discriminative model learning/computing, a belief propagation algorithm (BP) by Yedidia et al. (2000), and SWC into a three-layer computing framework. These methods are realized as different levels of approximation to an "ideal" generative model. We demonstrate the algorithm on the problem of human body configuration.
View-invariant alignment and matching of video sequences
Authors: Cen Rao, Alexei Gritai, Mubarak Shah, Tanveer Fathima Syeda-Mahmood
Venue: Conference ICCV 2003
Year: 2003

In this paper, we propose a novel method to establish temporal correspondence between the frames of two videos. 3D epipolar geometry is used to eliminate the distortion generated by the projection from 3D to 2D. Although the fundamental matrix contains the extrinsic property of the projective geometry between views, it is sensitive to noise. Therefore, we propose the use of a rank constraint of corresponding points in two views to measure the similarity between trajectories. This rank constraint shows more robustness and avoids computation of the fundamental matrix. A dynamic programming approach using the similarity measurement is proposed to find the nonlinear time-warping function for videos containing human activities. In this way, videos of different individuals taken at different times and from distinct viewpoints can be synchronized. A temporal pyramid of trajectories is applied to improve the accuracy of the view-invariant dynamic time-warping approach. We show various applications of this approach such as video synthesis, human action recognition, and computer aider training. Compared to state-of-the-art techniques, our method shows a great improvement.
The effect of pixel-level fusion on object tracking in multi-sensor surveillance video
Authors: Nedeljko Cvejic, Stavri G. Nikolov, Henry D. Knowles, Artur Loza, Alin Achim, David R. Bull, Cedric Nishan Canagarajah
Venue: Conference CVPR 2007
Year: 2007

This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been accomplished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR sequences, whereas the VIZ video showed the worst tracking performance due to higher levels of clutter. However, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to accurately assess how fusion affects the performance of the tracker.
GoCAD: GPU-assisted online content-adaptive display power saving for mobile devices in Internet streaming
Authors: Yao Liu, Mengbai Xiao, Ming Zhang, Xin Li, Mian Dong, Zhan Ma, Zhenhua Li, Songqing Chen
Venue: Conference WWW 2016
Year: 2016

During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile device's GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.
Face detection, pose estimation, and landmark localization in the wild
Authors: Xiangxin Zhu, Deva Ramanan
Venue: Conference CVPR 2012
Year: 2012

We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com).
On the information bottleneck theory of deep learning
Authors: Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox
Venue: Conference ICLR 2018
Year: 2018

Abstract The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior. In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of an initial fitting phase and a subsequent compression phase; second, that the compression phase is causally related to the excellent generalization performance of deep networks; and third, that the compression phase occurs due to the diffusion-like behavior of stochastic gradient descent. Here we show that none of these claims hold true in the general case, and instead reflect assumptions made to compute a finite mutual information metric in deterministic networks. When computed using simple binning, we demonstrate through a combination of analytical results and simulation that the information plane trajectory observed in prior work is predominantly a function of the neural nonlinearity employed: double-sided saturating nonlinearities like yield a compression phase as neural activations enter the saturation regime, but linear activation functions and single-sided saturating nonlinearities like the widely used ReLU in fact do not. Moreover, we find that there is no evident causal connection between compression and generalization: networks that do not compress are still capable of generalization, and vice versa. Next, we show that the compression phase, when it exists, does not arise from stochasticity in training by demonstrating that we can replicate the IB findings using full batch gradient descent rather than stochastic gradient descent. Finally, we show that when an input domain consists of a subset of task-relevant and task-irrelevant information, hidden representations do compress the task-irrelevant information, although the overall information about the input may monotonically increase with training time, and that this compression happens concurrently with the fitting process rather than during a subsequent compression period.
Efficient search for association rules
Authors: Geoffrey I. Webb
Venue: Conference KDD 2000
Year: 2000

This paper argues that for some applications direct search for association rules can be more e cient than the tw o stage process of the Apriori algorithm which rst nds large itemsets whic hare then used to iden tify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequen titemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented b y other customer information. An algorithm is presented that is computationally e cient for association rule analyses during which the n um ber of rules to be found can be constrained and all data can be maintained in memory.
Geospatial Mapping and Navigation of the Web
Authors: Kevin S. McCurley
Venue: Conference WWW 2001
Year: 2001

Web pages may be organized, indexed, searched, and navigated along several different feature dimensions. We investigate different approaches to discovering geographic context for web pages, and describe a navigational tool for browsing web resources by geographic proximity.
Mining, indexing, and querying historical spatiotemporal data
Authors: Nikos Mamoulis, Huiping Cao, George Kollios, Marios Hadjieleftheriou, Yufei Tao, David W. Cheung
Venue: Conference KDD 2004
Year: 2004

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.
Hidden Sentiment Association in Chinese Web Opinion Mining
Authors: Qi Su, Xinying Xu, Honglei Guo, Zhili Guo, Xian Wu, Xiaoxun Zhang, Bin Swen, Zhong Su
Venue: Conference WWW 2008
Year: 2008

The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features as opposed to the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without the explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.
Social contextual recommendation
Authors: Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, Shiqiang Yang
Venue: Conference CIKM 2012
Year: 2012

Exponential growth of information generated by online social networks demands effective recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social context has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. In this paper, we investigate social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online item adoption and recommendation. Then we propose a novel probabilistic matrix factorization method to fuse them in latent spaces. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network datasets in China. The empirical result and analysis on these two large datasets demonstrate that our method significantly outperform the existing approaches.