OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive disentanglement for authorship attribution
Authors: Zhiqiang Hu, Thao Thanh Nguyen, Yujia Hu, Chia-Yu Hung, Ming Shan Hee, Chun-Wei Seah, Roy Ka-Wei Lee
Venue: Conference WWW 2024
Year: 2024

Authorship Attribution (AA) seeks to determine the authorship of texts by examining distinctive writing styles. Although current AA methods have shown promising results, they often underperform in scenarios with significant topic shifts. This limitation arises from their inability to effectively separate topical content from the author's stylistic elements. Furthermore, most studies have focused on individual-level AA, overlooking the potential of regional-level AA to uncover linguistic patterns influenced by cultural and geographical factors. To bridge these gaps, this paper introduces ContrastDistAA, a novel framework that leverages contrastive learning and mutual information maximization to disentangle content and stylistic features in latent representations for AA. Our extensive experiments demonstrate that ContrastDistAA surpasses existing state-of-the-art models in both individual and regional-level AA tasks. This breakthrough not only improves the accuracy of authorship attribution but also broadens its applicability to include regional linguistic analysis, making a substantial contribution to the field of computational linguistics.
Diagonal transforms suffice for color constancy
Authors: Graham D. Finlayson, Mark S. Drew, Brian V. Funt
Venue: Conference ICCV 1993
Year: 1993

The main result is to show that under the conditions imposed by the Maloney-Wandell color constancy algorithm, color constancy can be expressed in terms of a simple independent adjustment of the sensor responses, so long as the sensor space is first transformed to a new basis. The overall goal is to present a theoretical analysis connecting many established theories of color constancy. For the case where surface reflectances are two-dimensional and illuminants are three-dimensional, it is proved that perfect color constancy can always be solved for by an independent adjustment of sensor responses, which means that the color constancy transform can be expressed as a diagonal matrix. In addition to purely theoretical arguments, results from simulations of diagonal-matrix-based color constancy, in which the spectra of real illuminants and reflectances along with the human cone sensitivity functions were used, are presented. The simulations demonstrate that when the cone sensor space is transformed to its new basis in the appropriate manner, a diagonal matrix supports close to optimal color constancy.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Optimizing learning in image retrieval
Authors: Yong Rui, Thomas S. Huang
Venue: Conference CVPR 2000
Year: 2000

Combining learning with vision techniques in interactive image retrieval has been an active research topic during the past few years. However, existing learning techniques either are based on heuristics or fail to analyze the working conditions. Furthermore, there is almost no in depth study on how to effectively learn from the users when there are multiple visual features in the retrieval system. To address these limitations, in this paper we present a vigorous optimization formulation of the learning process and solve the problem in a principled way. By using Lagrange multipliers, we have derived explicit solutions, which are both optimal and fast to compute. Extensive comparisons against state-of-the-art techniques have been performed. Experiments were carried out on a large-size heterogeneous image collection consisting of 17,000 images. Retrieval performance was tested under a wide range of conditions. Various evaluation criteria, including precision-recall curve and rank measure, have demonstrated the effectiveness and robustness of the proposed technique.
A direct method for stereo correspondence based on singular value decomposition
Authors: Maurizio Pilu
Venue: Conference CVPR 1997
Year: 1997

This paper proposes a new algorithm for matching point features across pairs of images. Despite the well-known combinatorial complexity of the problem, this work shows that an acceptably good solution can be obtained directly by singular value decomposition of an appropriate correspondence strength matrix. The approach draws from the method proposed previously but, besides suggesting its usefulness for stereo matching, in this work a correlation-weighted proximity function is used as correspondence strength to specifically cater for real images.
ROC Confidence Bands: An Empirical Evaluation
Authors: Sofus A. Macskassy, Foster J. Provost, Saharon Rosset
Venue: Conference ICML 2005
Year: 2005

This paper is about constructing confidence bands around ROC curves. We first introduce to the machine learning community three band-generating methods from the medical field, and evaluate how well they perform. Such confidence bands represent the region where the "true" ROC curve is expected to reside, with the designated confidence level. To assess the containment of the bands we begin with a synthetic world where we know the true ROC curve---specifically, where the class-conditional model scores are normally distributed. The only method that attains reasonable containment out-of-the-box produces non-parametric, "fixed-width" bands (FWBs). Next we move to a context more appropriate for machine learning evaluations: bands that with a certain confidence level will bound the performance of the model on future data. We introduce a correction to account for the larger uncertainty, and the widened FWBs continue to have reasonable containment. Finally, we assess the bands on 10 relatively large benchmark data sets. We conclude by recommending these FWBs, noting that being non-parametric they are especially attractive for machine learning studies, where the score distributions (1) clearly are not normal, and (2) even for the same data set vary substantially from learning method to learning method.
On-line density-based appearance modeling for object tracking
Authors: Bohyung Han, Larry S. Davis
Venue: Conference ICCV 2005
Year: 2005

Object tracking is a challenging problem in real-time computer vision due to variations of lighting condition, pose, scale, and view-point over time. However, it is exceptionally difficult to model appearance with respect to all of those variations in advance; instead, on-line update algorithms are employed to adapt to these changes. We present a new on-line appearance modeling technique which is based on sequential density approximation. This technique provides accurate and compact representations using Gaussian mixtures, in which the number of Gaussians is automatically determined. This procedure is performed in linear time at each time step, which we prove by amortized analysis. Features for each pixel and rectangular region are modeled together by the proposed sequential density approximation algorithm, and the target model is updated in scale robustly. We show the performance of our method by simulations and tracking in natural videos
Shape prior segmentation of multiple objects with graph cuts
Authors: Nhat Vu, B. S. Manjunath
Venue: Conference CVPR 2008
Year: 2008

We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.
Sparse dictionary-based representation and recognition of action attributes
Authors: Qiang Qiu 0002, Zhuolin Jiang, Rama Chellappa
Venue: Conference ICCV 2011
Year: 2011

We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and class distribution for each dictionary item. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence we can describe an action video by a set of compact and discriminative action attributes. More importantly, we can recognize modeled action categories in a sparse feature space, which can be generalized to unseen and unmodeled action categories. Experimental results demonstrate the effectiveness of our approach in action recognition applications.
In defense of soft-assignment coding
Authors: Lingqiao Liu, Lei Wang 0001, Xinwang Liu 0002
Venue: Conference ICCV 2011
Year: 2011

In object recognition, soft-assignment coding enjoys computational efficiency and conceptual simplicity. However, its classification performance is inferior to the newly developed sparse or local coding schemes. It would be highly desirable if its classification performance could become comparable to the state-of-the-art, leading to a coding scheme which perfectly combines computational efficiency and classification performance. To achieve this, we revisit soft-assignment coding from two key aspects: classification performance and probabilistic interpretation. For the first aspect, we argue that the inferiority of soft-assignment coding is due to its neglect of the underlying manifold structure of local features. To remedy this, we propose a simple modification to localize the soft-assignment coding, which surprisingly achieves comparable or even better performance than existing sparse or local coding schemes while maintaining its computational advantage. For the second aspect, based on our probabilistic interpretation of the soft-assignment coding, we give a probabilistic explanation to the magic max-pooling operation, which has successfully been used by sparse or local coding schemes but still poorly understood. This probability explanation motivates us to develop a new mix-order max-pooling operation which further improves the classification performance of the proposed coding scheme. As experimentally demonstrated, the localized soft-assignment coding achieves the state-of-the-art classification performance with the highest computational efficiency among the existing coding schemes.
New features and insights for pedestrian detection
Authors: Stefan Walk, Nikodem Majer, Konrad Schindler, Bernt Schiele
Venue: Conference CVPR 2010
Year: 2010

Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly - even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce a new feature, self-similarity on color channels, which consistently improves detection performance both for static images and for video sequences, across different datasets. In combination with HOG, these two features outperform the state-of-the-art by up to 20%. Finally, we report two insights concerning detector evaluations, which apply to classifier-based object detection in general. First, we show that a commonly under-estimated detail of training, the number of bootstrapping rounds, has a drastic influence on the relative (and absolute) performance of different feature/classifier combinations. Second, we discuss important intricacies of detector evaluation and show that current benchmarking protocols lack crucial details, which can distort evaluations.
Global stereo matching leveraged by sparse ground control points
Authors: Liang Wang 0002, Ruigang Yang
Venue: Conference CVPR 2011
Year: 2011

We present a novel global stereo model that makes use of constraints from points with known depths, i.e., the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel GCPs-based regularization term is naturally integrated into our global optimization framework in a principled way using the Bayes rule. The optimal solution of the inference problem can be approximated via existing energy minimization techniques such as graph cuts used in this paper. Our generic probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate the information from multiple sensors. Quantitative evaluations demonstrate the effectiveness of the proposed formulation for regularizing the ill-posed stereo matching problem and improving reconstruction accuracy.
SIGKDD Demo: Sensors and software to allow computational entomology, an emerging application of data mining
Authors: Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton
Venue: Conference KDD 2011
Year: 2011

The history of humankind is intimately connected to insects. Insect borne diseases kill a million people and destroy tens of billions of dollars worth of crops annually. However, at the same time, beneficial insects pollinate the majority of crop species, and it has been estimated that approximately one third of all food consumed by humans is directly pollinated by bees alone. Given the importance of insects in human affairs, it is somewhat surprising that computer science has not had a larger impact in entomology. We believe that recent advances in sensor technology are beginning change this, and a new field of Computational Entomology will emerge. We will demonstrate an inexpensive sensor that allows us to capture data from flying insects, and the software that allows us to analyze the data. Moreover, we will distribute both the sensors and software for free, to parties willing to take part in a crowdsourcing project on insect classification.
Detection of Gradual Transitions through Temporal Slice Analysis
Authors: Chong-Wah Ngo, Ting-Chuen Pong, Roland T. Chin
Venue: Conference CVPR 1999
Year: 1999

In this paper, we present approaches for detecting camera cuts, wipes and dissolves based on the analysis of spatio-temporal slices obtained from videos. These slices are composed of spatially and temporally coherent regions which can be perceived as shots. In the proposed methods, camera breaks are located by performing color-texture segmentation and statistical analysis on these video slices. In addition to detecting camera breaks, our methods can classify the detected breaks as camera cuts, wipes and dissolves in an efficient manner.
Action recognition with multiscale spatio-temporal contexts
Authors: Jiang Wang 0001, Zhuoyuan Chen, Ying Wu 0001
Venue: Conference CVPR 2011
Year: 2011

The popular bag of words approach for action recognition is based on the classifying quantized local features density. This approach focuses excessively on the local features but discards all information about the interactions among them. Local features themselves may not be discriminative enough, but combined with their contexts, they can be very useful for the recognition of some actions. In this paper, we present a novel representation that captures contextual interactions between interest points, based on the density of all features observed in each interest point's mutliscale spatio-temporal contextual domain. We demonstrate that augmenting local features with our contextual feature significantly improves the recognition performance.
Very sparse random projections
Authors: Ping Li, Trevor Hastie, Kenneth Ward Church
Venue: Conference KDD 2006
Year: 2006

There has been considerable interest in random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space. Let A in Rn x D be our n points in D dimensions. The method multiplies A by a random matrix R in RD x k, reducing the D dimensions down to just k for speeding up the computation. R typically consists of entries of standard normal N(0,1). It is well known that random projections preserve pairwise distances (in the expectation). Achlioptas proposed sparse random projections by replacing the N(0,1) entries in R with entries in -1,0,1 with probabilities 1/6, 2/3, 1/6, achieving a threefold speedup in processing time.We recommend using R of entries in -1,0,1 with probabilities 1/2√D, 1-1√D, 1/2√D for achieving a significant √D-fold speedup, with little loss in accuracy.
Local business ambience characterization through mobile audio sensing
Authors: He Wang, Dimitrios Lymberopoulos, Jie Liu
Venue: Conference WWW 2014
Year: 2014

Local search users today decide what business to visit solely based on distance information, and business ratings that can be sparse or stale. We believe that when users search for local businesses, such as bars or restaurants, they need to know more about the ambience of each business, such as how crowded it is, how loud and of what type the music it plays is, as well as how loud the human chatter in the business is. Unfortunately, this information doesn't exist today. In this paper, we propose to automatically crowdsource such rich, local business ambience metadata through real user check-in events. Every time a user checks into a business, the phone is in user's hands, and the phone's sensors can sense the business environment. We leverage the phone's microphone during this time to infer the occupancy and human chatter levels, the music type, as well as the music and noise levels in the business. As people check-in to businesses throughout the day, business metadata can be automatically updated over time, enabling a new generation of local search experience. Using approximately 150 audio traces collected from real businesses of various types over a period of 3 months, we show that by properly extracting the temporal and frequency signatures of the audio signal, it is feasible to train models that can simultaneously infer occupancy, human chatter, music, and noise levels in a business, with higher than 79% accuracy.
A shock grammar for recognition
Authors: Kaleem Siddiqi, Benjamin B. Kimia
Venue: Conference CVPR 1996
Year: 1996

We confront the theoretical and practical difficulties of computing a representation for two-dimensional shape, based on shocks or singularities that arise as the shape's boundary is deformed. First, we develop subpixel local detectors for finding and classifying shocks. Second, to show that shock patterns are not arbitrary but obey the rules of a grammar, and in addition satisfy specific topological and geometric constraints. Shock hypotheses that violate the grammar or are topologically or geometrically invalid are pruned to enforce global consistency. Survivors are organized into a hierarchical graph of shock groups computed in the reaction-diffusion space, where diffusion plays a role of regularization to determine the significance of each shock group. The shock groups can be functionally related to the object's parts, protrusions and bends, and the representation is suited to recognition: several examples illustrate its stability with rotations, scale changes, occlusion and movement of parts, even at very low resolutions.
Boosting Ordinal Features for Accurate and Fast Iris Recognition
Authors: Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong
Venue: Conference CVPR 2008
Year: 2008

In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a dasiacascadepsila, by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.
Generalized ordering constraints for multilabel optimization
Authors: Evgeny Strekalovskiy, Daniel Cremers
Venue: Conference ICCV 2011
Year: 2011

We propose a novel framework for imposing label ordering constraints in multilabel optimization. In particular, label jumps can be penalized differently depending on the jump direction. In contrast to the recently proposed MRF-based approaches, the proposed method arises from the viewpoint of spatially continuous optimization. It unifies and generalizes previous approaches to label ordering constraints: Firstly, it provides a common solution to three different problems which are otherwise solved by three separate approaches [4, 10, 14]. We provide an exact characterization of the penalization functions expressible with our approach. Secondly, we show that it naturally extends to three and higher dimensions of the image domain. Thirdly, it allows novel applications, such as the convex shape prior. Despite this generality, our model is easily adjustable to various label layouts and is also easy to implement. On a number of experiments we show that it works quite well, producing solutions comparable and superior to those obtained with previous approaches.
Dynamic scene shape reconstruction using a single structured light pattern
Authors: Hiroshi Kawasaki, Ryo Furukawa 0001, Ryusuke Sagawa, Yasushi Yagi
Venue: Conference CVPR 2008
Year: 2008

3D acquisition techniques to measure dynamic scenes and deformable objects with little texture are extensively researched for applications like the motion capturing of human facial expression. To allow such measurement, several techniques using structured light have been proposed. These techniques can be largely categorized into two types. The first involves techniques to temporally encode positional information of a projector’s pixels using multiple projected patterns, and the second involves techniques to spatially encode positional information into areas or color spaces. Although the former allows dense reconstruction with a sufficient number of patterns, it has difficulty in scanning objects in rapid motion. The latter technique uses only a single pattern, so this problem can be resolved, however, it often uses complex patterns or color intensities, which are weak to noise, shape distortions, or textures. Thus, it remains an open problem to achieve dense and stable 3D acquisition in real cases. In this paper, we propose a technique to achieve dense shape reconstruction that requires only a single-frame image of a grid pattern. The proposed technique also has the advantage of being robust in terms of image processing.