OpenRaiser/Intern-Atlas huggingface dataset:
Graph-based audience expansion model for marketing campaigns
Authors: Md. Mostafizur Rahman, Daisuke Kikuta, Yu Hirate, Toyotaro Suzumura
Venue: Conference SIGIR 2024
Year: 2024

Audience Expansion, a technique for identifying new audiences with similar behaviors to the original target or seed users. The major challenges include a heterogeneous user base, intricate marketing campaigns, constraints imposed by sparsity, and limited seed users, which lead to overfitting. In this context, we propose a novel solution named AudienceLinkNet, specifically designed to address the challenges associated with audience expansion in the context of Rakuten's diverse services and its clients. Our approach formulates the audience expansion problem as a graph problem and explores the combination of a Pre-trained Knowledge Graph Embedding Model and a Graph Convolutional Networks (GCNs). It emphasizes the structural retention properties of GCNs, enabling the model to overcome challenges related to cross-service data usage, sparsity and limited seed data. AudienceLinkNet simplifies the targeting process for small and large marketing campaigns and better utilizes demographics and behavioral attributes for targeting. Extensive experiments on our advertising platform, Rakuten AIris Target Prospecting, demonstrate the effectiveness of our audience expansion model. Additionally, we present the limitations of AudienceLinkNet.
Multi-stage deep classifier cascades for open world recognition
Authors: Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao
Venue: Conference CIKM 2019
Year: 2019

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem are far more challenging because: i)~new classes unseen in the training phase can appear when predicting; ii)~discriminative features need to evolve when new classes emerge in real time; and iii)~instances in new classes may not follow the "independent and identically distributed" (iid) assumption. Most existing work only aims to detect the unknown classes and is incapable of continuing to learn newer classes. Although a few methods consider both detecting and including new classes, all are based on the predefined handcrafted features that cannot evolve and are out-of-date for characterizing emerging classes. Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features. The proposed method injects dynamic elements into the system by detecting instances from unknown classes, while at the same time incrementally updating the model to include the new classes. The resulting cascade tree grows by adding a new leaf node classifier once a new class is detected, and the discriminative features are updated via an end-to-end learning strategy. Experiments on two real-world datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
Exploring modular task decomposition in cross-domain named entity recognition
Authors: Xinghua Zhang, Bowen Yu, Yubin Wang, Tingwen Liu, Taoyu Su, Hongbo Xu
Venue: Conference SIGIR 2022
Year: 2022

Cross-domain Named Entity Recognition (NER) aims to transfer knowledge from the source domain to the target, alleviating expensive labeling costs in the target domain. Most prior studies acquire domain-invariant features under the end-to-end sequence-labeling framework where each token is assigned a compositional label (e.g., B-LOC). However, the complexity of cross-domain transfer may be increased over this complicated labeling scheme, which leads to sub-optimal results, especially when there are significantly distinct entity categories across domains. In this paper, we aim to explore the task decomposition in cross-domain NER. Concretely, we suggest a modular learning approach in which two sub-tasks (entity span detection and type classification) are learned by separate functional modules to perform respective cross-domain transfer with corresponding strategies. Compared with the compositional labeling scheme, the label spaces are smaller and closer across domains especially in entity span detection, leading to easier transfer in each sub-task. And then we combine two sub-tasks to achieve the final result with modular interaction mechanism, and deploy the adversarial regularization for generalized and robust learning in low-resource target domains. Extensive experiments over 10 diverse domain pairs demonstrate that the proposed method is superior to state-of-the-art cross-domain NER methods in an end-to-end fashion (about average 6.4% absolute F1 score increase). Further analyses show the effectiveness of modular task decomposition and its great potential in cross-domain NER.
Zebra-crossing detection for the partially sighted
Authors: Stephen Se
Venue: Conference CVPR 2000
Year: 2000

Zebra-crossings are useful road features for outdoor navigation in mobility aids for the partially sighted. In this paper, zebra-crossings are detected by looking for groups of concurrent lines, edges are then partitioned using intensity variation information. In order to tackle the ambiguity of the detection algorithm in distinguishing zebra-crossings and stair-cases, pose information is sought. Three methods are developed to estimate the pose: homography search approach using an a priori model; finding normal using the vanishing line computed from equally-spaced lines and with two vanishing points. These algorithms have been applied to real images with promising results and they are also useful in some other shape from texture applications.
A Multiple Hypothesis Approach to Figure Tracking
Authors: Tat-Jen Cham, James M. Rehg
Venue: Conference CVPR 1999
Year: 1999

This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is represented as a set of modes with piecewise Gaussians characterizing the neighborhood around these modes. The temporal evolution of the probability density is achieved through sampling from the prior distribution, followed by local optimization of the sample positions to obtain updated modes. This method of generating hypotheses from state-space search does not require the use of discrete features unlike classical multiple-hypothesis tracking. The parametric form of the model is suited for high dimensional state-spaces which cannot be efficiently modeled using non-parametric approaches. Results are shown for tracking Fred Astaire in a movie dance sequence.
Articulated body motion capture by annealed particle filtering
Authors: Jonathan Deutscher, Andrew Blake 0001, Ian D. Reid 0001
Venue: Conference CVPR 2000
Year: 2000

The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints: either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, we aim for general tracking without special preparation of objects or restrictive assumptions. The principal contribution of the paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle based on annealing to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently.
Inferring Body Pose without Tracking Body Parts
Authors: Rómer Rosales, Stan Sclaroff
Venue: Conference CVPR 2000
Year: 2000

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e. the projection onto the image plane) of a single articulated body in terms of the position of a predetermined sets of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these generally low level visual features to body configurations. The system estimates different mappings, each one with a specific cluster in the visual feature space. Given a set of body motion sequences for training, unsupervised clustering is obtained via the Expectation Maximization algorithm. For each of the clusters, a function is estimated to build the mapping between low-level features to 2D pose. Given new visual features, a mapping from each cluster is performed to yield a set of possible poses. From this set, the system selects the most likely pose given the learned probability distribution and the visual feature of the proposed approach is characterized using real and artificially generated body postures, showing promising results.
A discriminative framework for modelling object classes
Authors: Alex Holub, Pietro Perona
Venue: Conference CVPR 2005
Year: 2005

Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn models from a set of training examples. Generative models learn their representations by considering data from a single class. Generative models are popular in computer vision for many reasons, including their ability to elegantly incorporate prior knowledge and to handle correspondences between object parts and detected features. However, generative models are often inferior to discriminative models during classification tasks. We study a discriminative approach to learning object categories which maintains the representational power of generative learning, but trains the generative models in a discriminative manner. The discriminatively trained models perform better during classification tasks as a result of selecting discriminative sets of features. We conclude by proposing a multi-class object recognition system which initially trains object classes in a generative manner, identifies subsets of similar classes with high confusion, and finally trains models for these subsets in a discriminative manner to realize gains in classification performance.
Building a dictionary of image fragments
Authors: Zicheng Liao, Ali Farhadi, Yang Wang 0003, Ian Endres, David A. Forsyth
Venue: Conference CVPR 2012
Year: 2012

We show how to build large dictionaries of meaningful image fragments. These fragments could represent objects, objects in a local context, or parts of scenes. Our fragments operate as region-based exemplars, and we show how they can be used for image classification, to localize objects, and to compose new images. While each of these activities has been demonstrated before, each has required manually extracted fragments. Because our method for fragment extraction is automatic it can operate at a large scale. Our method uses recent advances in generic object detection techniques, together with discriminative tests to obtain good, clean fragment sets with extensive diversity. Our fragments are organized by the tags of the source images to build a semantically organized fragment table. A good set of fragment exemplars describes only the object, rather than object+context. Context could help identify an object; but it could also contribute noise, because other objects might appear in the same context. We show a slight improvement in classification performance by two standard exemplar matching methods using our fragment dictionary over such methods using image exemplars. This suggests that knowing the support of an exemplar is valuable. Furthermore, we demonstrate our automatically built fragment dictionary is capable of good localization. Finally, our fragment dictionary supports a keyword based fragment search system, which allows artists to get the fragments they need to make image collages.
Using contours to detect and localize junctions in natural images
Authors: Michael Maire, Pablo Arbeláez, Charless C. Fowlkes, Jitendra Malik
Venue: Conference CVPR 2008
Year: 2008

Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction; contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. This contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.
Scale-invariant contour completion using conditional random fields
Authors: Xiaofeng Ren, Charless C. Fowlkes, Jitendra Malik
Venue: Conference ICCV 2005
Year: 2005

We present a model of curvilinear grouping using piece-wise linear representations of contours and a conditional random field to capture continuity and the frequency of different junction types. Potential completions are generated by building a constrained Delaunay triangulation (CDT) over the set of contours found by a local edge detector. Maximum likelihood parameters for the model are learned from human labeled ground truth. Using held out test data, we measure how the model, by incorporating continuity structure, improves boundary detection over the local edge detector. We also compare performance with a baseline local classifier that operates on pairs of edgels. Both algorithms consistently dominate the low-level boundary detector at all thresholds. To our knowledge, this is the first time that curvilinear continuity has been shown quantitatively useful for a large variety of natural images. Better boundary detection has immediate application in the problem of object detection and recognition.
Large-scale image annotation using visual synset
Authors: David Tsai 0002, Yushi Jing, Yi Liu 0054, Henry A. Rowley, Sergey Ioffe, James M. Rehg
Venue: Conference ICCV 2011
Year: 2011

We address the problem of large-scale annotation of web images. Our approach is based on the concept of visual synset, which is an organization of images which are visually-similar and semantically-related. Each visual synset represents a single prototypical visual concept, and has an associated set of weighted annotations. Linear SVM's are utilized to predict the visual synset membership for unseen image examples, and a weighted voting rule is used to construct a ranked list of predicted annotations from a set of visual synsets. We demonstrate that visual synsets lead to better performance than standard methods on a new annotation database containing more than 200 million images and 300 thousand annotations, which is the largest ever reported.
Speeded-up, relaxed spatial matching
Authors: Giorgos Tolias, Yannis Avrithis
Venue: Conference ICCV 2011
Year: 2011

A wide range of properties and assumptions determine the most appropriate spatial matching model for an application, e.g. recognition, detection, registration, or large scale image retrieval. Most notably, these include discriminative power, geometric invariance, rigidity constraints, mapping constraints, assumptions made on the underlying features or descriptors and, of course, computational complexity. Having image retrieval in mind, we present a very simple model inspired by Hough voting in the transformation space, where votes arise from single feature correspondences. A relaxed matching process allows for multiple matching surfaces or non-rigid objects under one-to-one mapping, yet is linear in the number of correspondences. We apply it to geometry re-ranking in a search engine, yielding superior performance with the same space requirements but a dramatic speed-up compared to the state of the art.
Hierarchical, learning-based automatic liver segmentation
Authors: Haibin Ling, Shaohua Kevin Zhou, Yefeng Zheng 0001, Bogdan Georgescu, Michael Sühling, Dorin Comaniciu
Venue: Conference CVPR 2008
Year: 2008

In this paper we present a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes. We target CT volumes that come from largely diverse sources (e.g., diseased in six different organs) and are generated by different scanning protocols (e.g., contrast and non-contrast, various resolution and position). Three key ingredients are combined to solve the segmentation problem. First, a hierarchical framework is used to efficiently and effectively monitor the accuracy propagation in a coarse-to-fine fashion. Second, two new learning techniques, marginal space learning and steerable features, are applied for robust boundary inference. This enables handling of highly heterogeneous texture pattern. Third, a novel shape space initialization is proposed to improve traditional methods that are limited to similarity transformation. The proposed approach is tested on a challenging dataset containing 174 volumes. Our approach not only produces excellent segmentation accuracy, but also runs about fifty times faster than state-of-the-art solutions [7, 9].
Modelling dynamic scenes by registering multi-view image sequences
Authors: Jean-Philippe Pons, Renaud Keriven, Olivier D. Faugeras
Venue: Conference CVPR 2005
Year: 2005

In this paper, we present a new variational method for multi-view stereovision and non-rigid three-dimensional motion estimation from multiple video sequences. Our method minimizes the prediction error of the shape and motion estimates. Both problems then translate into a generic image registration task. The latter is entrusted to a similarity measure chosen depending on imaging conditions and scene properties. In particular, our method can be made robust to appearance changes due to non-Lambertian materials and illumination changes. It results in a simpler, more flexible, and more efficient implementation than existing deformable surface approaches. The computation time on large datasets does not exceed thirty minutes. Moreover, our method is compliant with a hardware implementation with graphics processor units. Our stereovision algorithm yields very good results on a variety of datasets including specularities and translucency. We have successfully tested our scene flow algorithm on a very challenging multi-view video sequence of a non-rigid scene.
Asymmetrical occlusion handling using graph cut for multi-view stereo
Authors: Yichen Wei, Long Quan
Venue: Conference CVPR 2005
Year: 2005

Occlusion is usually modelled in two images symmetrically in previous stereo algorithms which cannot work for multi-view stereo efficiently. In this paper, we present a novel formulation that handles occlusion using only one depth map in an asymmetrical way. Consequently, multi-view information is efficiently accumulated to achieve high accuracy. The resulting energy function is complex and approximate graph cut based solutions are proposed. Our approach complements the theory and extends the applicability of using graph cut in stereo. The experiments demonstrate that the approach is comparable with the state of the art and potentially more efficient for multi-view stereo.
Eliminating ghosting and exposure artifacts in image mosaics
Authors: Matthew Uyttendaele, Ashley Eden, Richard Szeliski
Venue: Conference CVPR 2001
Year: 2001

As panoramic photography becomes increasingly popular, there is a greater need for high-quality software to automatically create panoramic images. Existing algorithms either produce a rough "stitch" that cannot deal with common artifacts, or require user input. This paper presents methods for dealing with two artifacts that often occur in practice. Our first contribution is a method for dealing with objects that move between different views of a dynamic scene. If such moving objects are left in, they will appear blurry and "ghosted". Treating such regions as nodes in a graph, we use a vertex cover algorithm to selectively remove all but one instance of each object. Our second contribution is a method for continuously adjusting exposure across multiple images in order to eliminate visible shifts in brightness or hue. We compute exposure corrections on a block-by block basis, then smoothly interpolate the parameters using a spline to get spatially continuous exposure adjustment. Our enhancements, combined with previously published techniques for automatic image stitching, result in a high-quality automated stitcher that exhibits far fewer artifacts than existing software.
A segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion
Authors: Zeyun Yu, Chandrajit L. Bajaj
Venue: Conference CVPR 2004
Year: 2004

In this paper we describe a method for skeletonization of gray-scale images without segmentation. Our method is based on anisotropic vector diffusion. The skeleton strength map, calculated from the diffused vector field, provides us a measure of how possible each pixel could be on the skeletons. The final skeletons are traced from the skeleton strength map, which mimics the behavior of edge detection from the edge strength map of the original image. A couple of real or synthesized images will be shown to demonstrate the performance of our algorithm.
Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos
Authors: Abhinav Gupta 0001, Praveen Srinivasan, Jianbo Shi, Larry S. Davis
Venue: Conference CVPR 2009
Year: 2009

Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data.