OpenRaiser/Intern-Atlas huggingface dataset:
DeepGBM: A deep learning framework distilled by GBDT for online prediction tasks
Authors: Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian, Tie-Yan Liu
Venue: Conference KDD 2019
Year: 2019

Online prediction has become one of the most essential tasks in many real-world applications. Two main characteristics of typical online prediction tasks include tabular input space and online data generation. Specifically, tabular input space indicates the existence of both sparse categorical features and dense numerical ones, while online data generation implies continuous task-generated data with potentially dynamic distribution. Consequently, effective learning with tabular input space as well as fast adaption to online data generation become two vital challenges for obtaining the online prediction model. Although Gradient Boosting Decision Tree (GBDT) and Neural Network (NN) have been widely used in practice, either of them yields their own weaknesses. Particularly, GBDT can hardly be adapted to dynamic online data generation, and it tends to be ineffective when facing sparse categorical features; NN, on the other hand, is quite difficult to achieve satisfactory performance when facing dense numerical features. In this paper, we propose a new learning framework, DeepGBM, which integrates the advantages of the both NN and GBDT by using two corresponding NN components: (1) CatNN, focusing on handling sparse categorical features. (2) GBDT2NN, focusing on dense numerical features with distilled knowledge from GBDT. Powered by these two components, DeepGBM can leverage both categorical and numerical features while retaining the ability of efficient online update. Comprehensive experiments on a variety of publicly available datasets have demonstrated that DeepGBM can outperform other well-recognized baselines in various online prediction tasks.
Identifying semantically equivalent object fragments
Authors: Boris Epshtein, Shimon Ullman
Venue: Conference CVPR 2005
Year: 2005

We describe a novel technique for identifying semantically equivalent parts in images belonging to the same object class, (e.g. eyes, license plates, aircraft wings etc.). The visual appearance of such object parts can differ substantially, and therefore, traditional image similarity-based methods are inappropriate for this task. The technique we propose is based on the use of common context. We first retrieve context fragments, which consistently appear together with a given input fragment in a stable geometric relation. We then use the context fragments in new images to infer the most likely position of equivalent parts. Given a set of image examples of objects in a class, the method can automatically learn the part structure of the domain - identify the main parts, and how their appearance changes across objects in the class. Two applications of the proposed algorithm are shown: the detection and identification of object parts and object recognition.
Webcam synopsis: Peeking around the world
Authors: Yael Pritch, Alex Rav-Acha, Avital Gutman, Shmuel Peleg
Venue: Conference ICCV 2007
Year: 2007

The world is covered with millions of Webcams, many transmit everything in their field of view over the Internet 24 hours a day. A Web search finds public webcams in airports, intersections, classrooms, parks, shops, ski resorts, and more. Even more private surveillance cameras cover many private and public facilities. Webcams are an endless resource, but most of the video broadcast will be of little interest due to lack of activity. We propose to generate a short video that will be a synopsis of an endless video streams, generated by webcams or surveillance cameras. We would like to address queries like "I would like to watch in one minute the highlights of this camera broadcast during the past day". The process includes two major phases: (i) An online conversion of the video stream into a database of objects and activities (rather than frames), (ii) A response phase, generating the video synopsis as a response to the user's query. To include maximum information in a short synopsis we simultaneously show activities that may have happened at different times. The synopsis video can also be used as an index into the original video stream.
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Authors: Steven M. Seitz, Brian Curless, James Diebel, Daniel Scharstein, Richard Szeliski
Venue: Conference CVPR 2006
Year: 2006

This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview.
Chain-of-layer: Iteratively prompting large language models for taxonomy induction from limited examples
Authors: Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Zhenwen Liang, Zhihan Zhang, Meng Jiang
Venue: Conference CIKM 2024
Year: 2024

Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making automatic taxonomy construction highly desirable. In this work, we introduce Chain-of-Layer which is an in-context learning framework designed to induct taxonomies from a given set of entities. Chain-of-Layer breaks down the task into selecting relevant candidate entities in each layer and gradually building the taxonomy from top to bottom. To minimize errors, we introduce the Ensemble-based Ranking Filter to reduce the hallucinated content generated at each iteration. Through extensive experiments, we demonstrate that Chain-of-Layer achieves state-of-the-art performance on four real-world benchmarks. Source code available at: https://github.com/qingkaizeng/chain-of-layer.
Discriminative structure learning of hierarchical representations for object detection
Authors: Paul Schnitzspan, Mario Fritz, Stefan Roth 0001, Bernt Schiele
Venue: Conference CVPR 2009
Year: 2009

A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques, we propose a hierarchical multi-feature representation and automatically learn flexible hierarchical object models for a wide variety of object classes. To that end we not only rely on automatic selection of relevant individual features, but go beyond previous work by automatically selecting and modeling complex, long-range feature couplings within this model. To achieve this generality and flexibility our work combines structure learning in conditional random fields and discriminative parameter learning of classifiers using hierarchical features. We adopt an efficient gradient based heuristic for model selection and carry it forward to discriminative, multidimensional selection of features and their couplings for improved detection performance. Experimentally we consistently outperform the currently leading method on all 20 classes of the PASCAL VOC 2007 challenge and achieve the best published results on 16 of 20 classes.
Learning cross-modality similarity for multinomial data
Authors: Yangqing Jia, Mathieu Salzmann, Trevor Darrell
Venue: Conference ICCV 2011
Year: 2011

Many applications involve multiple-modalities such as text and images that describe the problem of interest. In order to leverage the information present in all the modalities, one must model the relationships between them. While some techniques have been proposed to tackle this problem, they either are restricted to words describing visual objects only, or require full correspondences between the different modalities. As a consequence, they are unable to tackle more realistic scenarios where a narrative text is only loosely related to an image, and where only a few image-text pairs are available. In this paper, we propose a model that addresses both these challenges. Our model can be seen as a Markov random field of topic models, which connects the documents based on their similarity. As a consequence, the topics learned with our model are shared across connected documents, thus encoding the relations between different modalities. We demonstrate the effectiveness of our model for image retrieval from a loosely related text.
A unified approach to salient object detection via low rank matrix recovery
Authors: Xiaohui Shen, Ying Wu 0001
Venue: Conference CVPR 2012
Year: 2012

Salient object detection is not a pure low-level, bottom-up process. Higher-level knowledge is important even for task-independent image saliency. We propose a unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects. In our model, an image is represented as a low-rank matrix plus sparse noises in a certain feature space, where the non-salient regions (or background) can be explained by the low-rank matrix, and the salient regions are indicated by the sparse noises. To ensure the validity of this model, a linear transform for the feature space is introduced and needs to be learned. Given an image, its low-level saliency is then extracted by identifying those sparse noises when recovering the low-rank matrix. Furthermore, higher-level knowledge is fused to compose a prior map, and is treated as a prior term in the objective function to improve the performance. Extensive experiments show that our model can comfortably achieves comparable performance to the existing methods even without the help from high-level knowledge. The integration of top-down priors further improves the performance and achieves the state-of-the-art. Moreover, the proposed model can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications.
Superpixels via pseudo-boolean optimization
Authors: Yuhang Zhang 0001, Richard I. Hartley, John Mashford, Stewart Burn
Venue: Conference ICCV 2011
Year: 2011

We propose an algorithm for creating superpixels. The major step in our algorithm is simply minimizing two pseudo-Boolean functions. The processing time of our algorithm on images of moderate size is only half a second. Experiments on a benchmark dataset show that our method produces superpixels of comparable quality with existing algorithms. Last but not least, the speed of our algorithm is independent of the number of superpixels, which is usually the bottle-neck for the traditional algorithms of superpixel creation.
Triangulation made easy
Authors: Peter Lindstrom 0001
Venue: Conference CVPR 2010
Year: 2010

We describe a simple and efficient algorithm for two-view triangulation of 3D points from approximate 2D matches based on minimizing the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> reprojection error. Our iterative algorithm improves on the one by Kanatani et al. by ensuring that in each iteration the epipolar constraint is satisfied. In the case where the two cameras are pointed in the same direction, the method provably converges to an optimal solution in exactly two iterations. For more general camera poses, two iterations are sufficient to achieve convergence to machine precision, which we exploit to devise a fast, non-iterative method. The resulting algorithm amounts to little more than solving a quadratic equation, and involves a fixed, small number of simple matrix-vector operations and no conditional branches. We demonstrate that the method computes solutions that agree to very high precision with those of Hartley and Sturm's original polynomial method, though achieves higher numerical stability and 1-4 orders of magnitude greater speed.
Tracking multiple people under global appearance constraints
Authors: Horesh Ben Shitrit, Jérôme Berclaz, François Fleuret, Pascal Fua
Venue: Conference ICCV 2011
Year: 2011

In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a convex global optimization problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. We validate our approach on three multi-camera sport and pedestrian datasets that contain long and complex sequences. Our algorithm perseveres identities better than state-of-the-art algorithms while keeping similar MOTA scores.
Common visual pattern discovery via spatially coherent correspondences
Authors: Hairong Liu, Shuicheng Yan
Venue: Conference CVPR 2010
Year: 2010

We investigate how to discover all common visual patterns within two sets of feature points. Common visual patterns generally share similar local features as well as similar spatial layout. In this paper these two types of information are integrated and encoded into the edges of a graph whose nodes represent potential correspondences, and the common visual patterns then correspond to those strongly connected subgraphs. All such strongly connected subgraphs correspond to large local maxima of a quadratic function on simplex, which is an approximate measure of the average intra-cluster affinity score of these subgraphs. We find all large local maxima of this function, thus discover all common visual patterns and recover the correct correspondences, using replicator equation and through a systematic way of initialization. The proposed algorithm possesses two characteristics: 1) robust to outliers, and 2) being able to discover all common visual patterns, no matter the mappings among the common visual patterns are one to one, one to many, or many to many. Extensive experiments on both point sets and real images demonstrate the properties of our proposed algorithm in terms of robustness to outliers, tolerance to large spatial deformations, and simplicity in implementation.
Feature Intertwiner for Object Detection
Authors: Hongyang Li 0001, Bo Dai 0002, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang 0001
Venue: Conference ICLR 2019
Year: 2019

A well-trained model should classify objects with a unanimous score for every category. This requires the high-level semantic features should be as much alike as possible among samples. To achive this, previous works focus on re-designing the loss or proposing new regularization constraints. In this paper, we provide a new perspective. For each category, it is assumed that there are two feature sets: one with reliable information and the other with less reliable source. We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space. Such a scheme also benefits the reliable set since samples become closer within the same category - implying that it is easier for the classifier to identify. We refer to this mutual learning process as feature intertwiner and embed it into object detection. It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass (e.g., RoI operation). We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set. Specifically, an intertwiner is designed to minimize the distribution divergence between two sets. The choice of generating an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) theory into the framework. Samples in the less reliable set are better aligned with aid of OT metric. Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts.
Estimating the aspect layout of object categories
Authors: Yu Xiang, Silvio Savarese
Venue: Conference CVPR 2012
Year: 2012

In this work we seek to move away from the traditional paradigm for 2D object recognition whereby objects are identified in the image as 2D bounding boxes. We focus instead on: i) detecting objects; ii) identifying their 3D poses; iii) characterizing the geometrical and topological properties of the objects in terms of their aspect configurations in 3D. We call such characterization an object's aspect layout (see Fig. 1). We propose a new model for solving these problems in a joint fashion from a single image for object categories. Our model is constructed upon a novel framework based on conditional random fields with maximal margin parameter estimation. Extensive experiments are conducted to evaluate our model's performance in determining object pose and layout from images. We achieve superior viewpoint accuracy results on three public datasets and show extensive quantitative analysis to demonstrate the ability of accurately recovering the aspect layout of objects.
Model-based 3d tracking of an articulated hand
Authors: Bjoern Stenger, Paulo R. S. Mendonça, Roberto Cipolla
Venue: Conference CVPR 2001
Year: 2001

This paper presents a practical technique for model-based 3D hand tracking. An anatomically accurate hand model is built from truncated quadrics. This allows for the generation of 2D profiles of the model using elegant tools from projective geometry, and for an efficient method to handle self-occlusion. The pose of the hand model is estimated with an Unscented Kalman filter (UKF), which minimizes the geometric error between the profiles and edges extracted from the images. The use of the UKF permits higher frame rates than more sophisticated estimation methods such as particle filtering, whilst providing higher accuracy than the extended Kalman filter The system is easily scalable from single to multiple views, and from rigid to articulated models. First experiments on real data using one and two cameras demonstrate the quality of the proposed method for tracking a 7 DOF hand model.
Challenges in disentangling independent factors of variation
Authors: Attila Szabó, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro
Venue: Conference ICLR 2018
Year: 2018

We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set. Our weak labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. To make use of weak labels we introduce an autoencoder model and train it through constraints on image pairs and triplets. We formally prove that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature. We call this issue the reference ambiguity. Moreover, we show the role of the feature dimensionality and adversarial training. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.
View-independent recognition of hand postures
Authors: Ying Wu 0001, Thomas S. Huang
Venue: Conference CVPR 2000
Year: 2000

Since the human hand is highly articulated and deformable, hand posture recognition is a challenging example in the research on view-independent object recognition. Due to the difficulties of the model-based approach, the appearance-based learning approach is promising to handle large variation in visual inputs. However, the generalization of many proposed supervised learning methods to this problem often suffers from the insufficiency of labeled training data. This paper describes an approach to alleviate this difficulty by adding a large unlabeled training set. Combining supervised and unsupervised learning paradigms, a novel and powerful learning approach, the Discriminant-EM (D-EM) algorithm, is proposed in this paper to handle the case of a small labeled training set. Experiments show that D-EM outperforms many other learning methods. Based on this approach, we implement a gesture interface to recognize a set of predefined gesture commands, and it is also extended to hand detection. This algorithm can also apply to other object recognition tasks.
The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation
Authors: Jonathan Taylor 0001, Jamie Shotton, Toby Sharp, Andrew W. Fitzgibbon
Venue: Conference CVPR 2012
Year: 2012

Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time.
Learning binary codes for collaborative filtering
Authors: Ke Zhou, Hongyuan Zha
Venue: Conference KDD 2012
Year: 2012

This paper tackles the efficiency problem of making recommendations in the context of large user and item spaces. In particular, we address the problem of learning binary codes for collaborative filtering, which enables us to efficiently make recommendations with time complexity that is independent of the total number of items. We propose to construct binary codes for users and items such that the preference of users over items can be accurately preserved by the Hamming distance between their respective binary codes. By using two loss functions measuring the degree of divergence between the training and predicted ratings, we formulate the problem of learning binary codes as a discrete optimization problem. Although this optimization problem is intractable in general, we develop effective relaxations that can be efficiently solved by existing methods. Moreover, we investigate two methods to obtain the binary codes from the relaxed solutions. Evaluations are conducted on three public-domain data sets and the results suggest that our proposed method outperforms several baseline alternatives.
From Structure-from-Motion Point Clouds to Fast Location Recognition
Authors: Arnold Irschara, Christopher Zach, Jan-Michael Frahm, Horst Bischof
Venue: Conference CVPR 2009
Year: 2009

Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.