OpenRaiser/Intern-Atlas huggingface dataset:
Robust and discriminative distance for multi-instance learning
Authors: Hua Wang 0007, Feiping Nie 0001, Heng Huang 0001
Venue: Conference CVPR 2012
Year: 2012

Multi-Instance Learning (MIL) is an emerging topic in machine learning, which has broad applications in computer vision. For example, by considering video classification as a MIL problem where we only need labeled video clips (such as tagged online videos) but not labeled video frames, one can lower down the labeling cost, which is typically very expensive. We propose a novel class specific distance Metrics enhanced Class-to-Bag distance (M-C2B) method to learn a robust and discriminative distance for multi-instance data, which employs the not-squared ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm distance to address the most difficult challenge in MIL, i.e., the outlier instances that abound in multi-instance data by nature. As a result, the formulated objective ends up to be a simultaneous ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2, 1</sub> -norm minimization and maximization (minmax) problem, which is very hard to solve in general due to the non-smoothness of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2, 1</sub> -norm. We thus present an efficient iterative algorithm to solve the general ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2, 1</sub> -norm minmax problem with rigorously proved convergence. To the best of our knowledge, we are the first to solve a general ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2, 1</sub> -norm minmax problem in literature. We have conducted extensive experiments to evaluate various aspects of the proposed method, in which promising results validate our new method in cost-effective video classification.
Learning to attend, copy, and generate for session based query suggestion
Authors: Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, Pascal Fleury
Venue: Conference CIKM 2017
Year: 2017

Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.
A hierarchical recurrent encoder-decoder for generative context-aware query suggestion
Authors: Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, Jian-Yun Nie
Venue: Conference CIKM 2015
Year: 2015

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.
Symmetric piecewise planar object reconstruction from a single image
Authors: Tianfan Xue, Jianzhuang Liu, Xiaoou Tang
Venue: Conference CVPR 2011
Year: 2011

Recovering 3D geometry from a single view of an object is an important and challenging problem in computer vision. Previous methods mainly focus on one specific class of objects without large topological changes, such as cars, faces, or human bodies. In this paper, we propose a novel single view reconstruction algorithm for symmetric piece-wise planar objects that are not restricted to some object classes. Symmetry is ubiquitous in manmade and natural objects and provides rich information for 3D reconstruction. Given a single view of a symmetric piecewise planar object, we first find out all the symmetric line pairs. The geometric properties of symmetric objects are used to narrow down the searching space. Then, based on the symmetric lines, a depth map is recovered through a Markov random field. Experimental results show that our algorithm can efficiently recover the 3D shapes of different objects with significant topological variations.
Recovering the spatial layout of cluttered rooms
Authors: Varsha Hedau, Derek Hoiem, David A. Forsyth
Venue: Conference ICCV 2009
Year: 2009

In this paper, we consider the problem of recovering the spatial layout of indoor scenes from monocular images. The presence of clutter is a major problem for existing single-view 3D reconstruction algorithms, most of which rely on finding the ground-wall boundary. In most rooms, this boundary is partially or entirely occluded. We gain robustness to clutter by modeling the global room space with a parameteric 3D “box” and by iteratively localizing clutter and refitting the box. To fit the box, we introduce a structured learning algorithm that chooses the set of parameters to minimize error, based on global perspective cues. On a dataset of 308 images, we demonstrate the ability of our algorithm to recover spatial layout in cluttered rooms and show several examples of estimated free space.
Feature correspondence and deformable object matching via agglomerative correspondence clustering
Authors: Minsu Cho, Jungmin Lee, Kyoung Mu Lee
Venue: Conference ICCV 2009
Year: 2009

We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local invariant features. We formulate object-based image matching as an unsupervised multi-class clustering problem on a set of candidate feature matches, and propose a novel pairwise dissimilarity measure and a robust linkage model in the framework of hierarchical agglomerative clustering. The algorithm handles significant amount of outliers and deformation as well as multiple clusters, thus enabling simultaneous feature matching and clustering from real-world image pairs with significant clutter and multiple deformable objects. The experimental evaluation on feature correspondence, object recognition, and object-based image matching demonstrates that our method is robust to both outliers and deformation, and applicable to a wide range of image matching problems.
Handling occlusions in dense multi-view stereo
Authors: Sing Bing Kang, Richard Szeliski, Jinxiang Chai
Venue: Conference CVPR 2001
Year: 2001

While stereo matching was originally formulated as the recovery of 3D shape from a pair of images, it is now generally recognized that using more than two images can dramatically improve the quality of the reconstruction. Unfortunately, as more images are added, the prevalence of semi-occluded regions (pixels visible in some but not all images) also increases. We propose some novel techniques to deal with this problem. Our first idea is to use a combination of shiftable windows and a dynamically selected subset of the neighboring images to do the matches. Our second idea is to explicitly label occluded pixels within a global energy minimization framework, and to reason about visibility within this framework so that only truly visible pixels are matched. Experimental results show a dramatic improvement using the first idea over conventional multibaseline stereo, especially when used in conjunction with a global energy minimization technique. These results also show that explicit occlusion labeling and visibility reasoning do help, but not significantly, if the spatial and temporal selection is applied first.
A theory of shape by space carving
Authors: Kiriakos N. Kutulakos, Steven M. Seitz
Venue: Conference ICCV 1999
Year: 1999

In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) build photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their effects on arbitrary views of a 3D scene.
Real-time visibility-based fusion of depth maps
Authors: Paul Merrell, Amir Akbarzadeh, Liang Wang 0002, Philippos Mordohai, Jan-Michael Frahm, Ruigang Yang, David Nistér, Marc Pollefeys
Venue: Conference ICCV 2007
Year: 2007

We present a viewpoint-based approach for the quick fusion of multiple stereo depth maps. Our method selects depth estimates for each pixel that minimize violations of visibility constraints and thus remove errors and inconsistencies from the depth maps to produce a consistent surface. We advocate a two-stage process in which the first stage generates potentially noisy, overlapping depth maps from a set of calibrated images and the second stage fuses these depth maps to obtain an integrated surface with higher accuracy, suppressed noise, and reduced redundancy. We show that by dividing the processing into two stages we are able to achieve a very high throughput because we are able to use a computationally cheap stereo algorithm and because this architecture is amenable to hardware-accelerated (GPU) implementations. A rigorous formulation based on the notion of stability of a depth estimate is presented first. It aims to determine the validity of a depth estimate by rendering multiple depth maps into the reference view as well as rendering the reference depth map into the other views in order to detect occlusions and free- space violations. We also present an approximate alternative formulation that selects and validates only one hypothesis based on confidence. Both formulations enable us to perform video-based reconstruction at up to 25 frames per second. We show results on the multi-view stereo evaluation benchmark datasets and several outdoors video sequences. Extensive quantitative analysis is performed using an accurately surveyed model of a real building as ground truth.
BA-net: Dense bundle adjustment networks
Authors: Chengzhou Tang, Ping Tan
Venue: Conference ICLR 2019
Year: 2019

This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.
A mobile vision system for robust multi-person tracking
Authors: Andreas Ess, Bastian Leibe, Konrad Schindler, Luc Van Gool
Venue: Conference CVPR 2008
Year: 2008

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.
Collaborative deep metric learning for video understanding
Authors: Joonseok Lee, Sami Abu-El-Haija, Balakrishnan Varadarajan, Apostol Natsev
Venue: Conference KDD 2018
Year: 2018

The goal of video understanding is to develop algorithms that enable machines understand videos at the level of human experts. Researchers have tackled various domains including video classification, search, personalized recommendation, and more. However, there is a research gap in combining these domains in one unified learning framework. Towards that, we propose a deep network that embeds videos using their audio-visual content, onto a metric space which preserves video-to-video relationships. Then, we use the trained embedding network to tackle various domains including video classification and recommendation, showing significant improvements over state-of-the-art baselines. The proposed approach is highly scalable to deploy on large-scale video sharing platforms like YouTube.
Markerless motion capture with unsynchronized moving cameras
Authors: Nils Hasler, Bodo Rosenhahn, Thorsten Thormählen, Michael Wand 0001, Juergen Gall, Hans-Peter Seidel
Venue: Conference CVPR 2009
Year: 2009

In this work we present an approach for markerless motion capture (MoCap) of articulated objects, which are recorded with multiple unsynchronized moving cameras. Instead of using fixed (and expensive) hardware synchronized cameras, this approach allows us to track people with off-the-shelf handheld video cameras. To prepare a sequence for motion capture, we first reconstruct the static background and the position of each camera using Structure-from-Motion (SfM). Then the cameras are registered to each other using the reconstructed static background geometry. Camera synchronization is achieved via the audio streams recorded by the cameras in parallel. Finally, a markerless MoCap approach is applied to recover positions and joint configurations of subjects. Feature tracks and dense background geometry are further used to stabilize the MoCap. The experiments show examples with highly challenging indoor and outdoor scenes.
Diffusion distance for histogram comparison
Authors: Haibin Ling, Kazunori Okada
Venue: Conference CVPR 2006
Year: 2006

In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.
Non-metric image-based rendering for video stabilization
Authors: Chris Buehler, Michael Bosse, Leonard McMillan
Venue: Conference CVPR 2001
Year: 2001

We consider the problem of video stabilization: removing unwanted image perturbations due to unstable camera motions. We approach this problem from an image-based rendering (IBR) standpoint. Given an unstabilized video sequence, the task is to synthesize a new sequence as seen from a stabilized camera trajectory. This task is relatively straightforward if one has a Euclidean reconstruction of the unstabilized camera trajectory and a suitable IBR algorithm. However, it is often not feasible to obtain a Euclidean reconstruction from an arbitrary video sequence. In light of this problem, we describe IBR techniques for non-metric reconstructions, which are often much easier to obtain since they do not require camera calibration. These rendering techniques are well suited to the video stabilization problem. The key idea behind our techniques is that all measurements are specified in the image space, rather than in the non-metric space.
Video stabilization with a depth camera
Authors: Shuaicheng Liu, Yinting Wang, Lu Yuan, Jiajun Bu, Ping Tan 0002, Jian Sun 0001
Venue: Conference CVPR 2012
Year: 2012

Previous video stabilization methods often employ homographies to model transitions between consecutive frames, or require robust long feature tracks. However, the homography model is invalid for scenes with significant depth variations, and feature point tracking is fragile in videos with textureless objects, severe occlusion or camera rotation. To address these challenging cases, we propose to solve video stabilization with an additional depth sensor such as the Kinect camera. Though the depth image is noisy, incomplete and low resolution, it facilitates both camera motion estimation and frame warping, which make the video stabilization a much well posed problem. The experiments demonstrate the effectiveness of our algorithm.
From projective to euclidean space under any practical situation, a criticism of self-calibration
Authors: Sylvain Bougnoux
Venue: Conference ICCV 1998
Year: 1998

For many practical applications it is important to relax the self-calibration conditions to allow for changing internal camera parameters (e.g. zooming/focusing...). Classical techniques failed for such conditions. We present the available constraints that allow us to right a projective calibration to a Euclidean one. Meanwhile, we found that the estimations of the internal parameters were rather inaccurate. We discuss theoretically this difficulty and above all the resulting effect on the 3D reconstruction. In fact, we show that the uncertainty on the focal length estimation leads to an Euclidean calibration up to a quasi anisotropic homothety whereas the error on the principal point can often be interpreted as a translation. Hopefully, the calibration we come up with, is quite acceptable for reconstruction of models.
Simultaneous linear estimation of multiple view geometry and lens distortion
Authors: Andrew W. Fitzgibbon
Venue: Conference CVPR 2001
Year: 2001

A problem in uncalibrated stereo reconstruction is that cameras which deviate from the pinhole model have to be pre-calibrated in order to correct for nonlinear lens distortion. If they are not, and point correspondence is attempted using the uncorrected images, the matching constraints provided by the fundamental matrix must be set so loose that point matching is significantly hampered. This paper shows how linear estimation of the fundamental matrix from two-view point correspondences may be augmented to include one term of radial lens distortion. This is achieved by (1) changing from the standard radial-lens model to another which (as we show) has equivalent power, but which takes a simpler form in homogeneous coordinates, and (2) expressing fundamental matrix estimation as a quadratic eigenvalue problem (QEP), for which efficient algorithms are well known. I derive the new estimator, and compare its performance against bundle-adjusted calibration-grid data. The new estimator is fast enough to be included in a RANSAC-based matching loop, and we show cases of matching being rendered possible by its use. I show how the same lens can be calibrated in a natural scene where the lack of straight lines precludes most previous techniques. The modification when the multi-view relation is a planar homography or trifocal tensor is described.