OpenRaiser/Intern-Atlas huggingface dataset:
Robust shadow and illumination estimation using a mixture model
Authors: Alexandros Panagopoulos, Dimitris Samaras, Nikos Paragios
Venue: Conference CVPR 2009
Year: 2009

Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of exact 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination environment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of samples generated from this illumination environment. We integrate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D reasoning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the augmented reality results.
Tracking the articulated motion of two strongly interacting hands
Authors: Iasonas Oikonomidis, Nikolaos Kyriazis, Antonis A. Argyros
Venue: Conference CVPR 2012
Year: 2012

We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains observations provided by an RGB-D sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible.
Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams
Authors: Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, Jiawei Han
Venue: Conference KDD 2017
Year: 2017

Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in leveraging geo-tagged tweet streams for online local event detection. Nevertheless, the accuracies of existing methods still remain unsatisfactory for building reliable local event detection systems. We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection. The effectiveness of TrioVecEvent is underpinned by its two-step detection scheme. First, it ensures a high coverage of the underlying local events by dividing the tweets in the query window into coherent geo-topic clusters. To generate quality geo-topic clusters, we capture short-text semantics by learning multimodal embeddings of the location, time, and text, and then perform online clustering with a novel Bayesian mixture model. Second, TrioVecEvent considers the geo-topic clusters as candidate events and extracts a set of features for classifying the candidates. Leveraging the multimodal embeddings as background knowledge, we introduce discriminative features that can well characterize local events, which enables pinpointing true local events from the candidate pool with a small amount of training data. We have used crowdsourcing to evaluate TrioVecEvent, and found that it improves the performance of the state-of-the-art method by a large margin.
Correlational spectral clustering
Authors: Matthew B. Blaschko, Christoph H. Lampert
Venue: Conference CVPR 2008
Year: 2008

We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity measure, or by co-occurrence matrices that are then used in biclustering. In contrast, the proposed method uses separate similarity measures for each data representation, and allows for projection of previously unseen data that are only observed in one representation (e.g. images but not text). We show that this algorithm generalizes traditional spectral clustering algorithms and show consistent empirical improvement over spectral clustering on a variety of datasets of images with associated text.
3d generic object categorization, localization and pose estimation
Authors: Silvio Savarese, Li Fei-Fei 0001
Venue: Conference ICCV 2007
Year: 2007

We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotations and scale changes. Our approach is to capture a compact model of an object category by linking together diagnostic parts of the objects from different viewing points. We emphasize on the fact that our "parts" are large and discriminative regions of the objects that are composed of many local invariant features. Instead of recovering a full 3D geometry, we connect these parts through their mutual homographic transformation. The resulting model is a compact summarization of both the appearance and geometry information of the object class. We propose a framework in which learning is done via minimal supervision compared to previous works. Our results on categorization show superior performances to state-of-the-art algorithms such as [23]. Furthermore, we have compiled a new 3D object dataset that consists of 10 different object categories. We have tested our algorithm on this dataset and have obtained highly promising results.
Automatic recovery of relative camera rotations for urban scenes
Authors: Matthew E. Antone, Seth J. Teller
Venue: Conference CVPR 2000
Year: 2000

In this paper we describe a formulation of extrinsic camera calibration that decouples rotation from translation by exploiting properties inherent in urban scenes. We then present an algorithm which uses edge features to robustly and accurately estimate relative rotations among multiple cameras given intrinsic calibration and approximate initial pose. The algorithm is linear both in the number of images and the number of features. We estimate the number and directions of vanishing points (VPs) with respect to each camera using a hybrid approach that combines the robustness of the Hough transform with the accuracy of expectation maximization. Matching and labeling methods identify unique VPs and correspond them across all cameras. Finally, a technique akin to bundle adjustment produces globally optimal estimates of relative camera rotations by bringing all VPs into optimal alignment. Uncertainty is modeled and used at every stage to improve accuracy. We assess the algorithm's performance on both synthetic and real data, and compare our results to those of semi-automated photogrammetric methods for a large set of real hemispherical images, using several consistency and error metrics.
Manhattan scene understanding using monocular, stereo, and 3D features
Authors: Alex Flint, David William Murray 0001, Ian D. Reid 0001
Venue: Conference ICCV 2011
Year: 2011

This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through structure-from-motion. We combine geometric and photometric cues in a Bayesian framework, building on recent successes leveraging the indoor Manhattan assumption in monocular vision. We focus on indoor environments and show how to extract key boundaries while ignoring clutter and decorations. To achieve this we present a graphical model that relates photometric cues learned from labeled data, stereo photo-consistency across multiple views, and depth cues derived from structure-from-motion point clouds. We show how to solve MAP inference using dynamic programming, allowing exact, global inference in ~100 ms (in addition to feature computation of under one second) without using specialized hardware. Experiments show our system out-performing the state-of-the-art.
Non-iterative approach for fast and accurate vanishing point detection
Authors: Jean-Philippe Tardif
Venue: Conference ICCV 2009
Year: 2009

We present an algorithm that quickly and accurately estimates vanishing points in images of man-made environments. Contrary to previously proposed solutions, ours is neither iterative nor relies on voting in the space of vanishing points. Our formulation is based on a recently proposed algorithm for the simultaneous estimation of multiple models called J-Linkage. Our method avoids representing edges on the Gaussian sphere and the computations and error measures are done in the image. We show that a consistency measure between a vanishing point and an edge of the image can be computed in closed-form while being geometrically meaningful. Finally, given a set of estimated vanishing points, we show how this consistency measure can be used to identify the three vanishing points corresponding to the Manhattan directions. We compare our algorithm with other approaches on the York Urban Database and show significant performance improvements.
Building a test collection for complex document information processing
Authors: David D. Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David A. Grossman, Jefferson Heard
Venue: Conference SIGIR 2006
Year: 2006

Research and development of information access technology for scanned paper documents has been hampered by the lack of public test collections of realistic scope and complexity. As part of a project to create a prototype system for search and mining of masses of document images, we are assembling a 1.5 terabyte dataset to support evaluation of both end-to-end complex document information processing (CDIP) tasks (e.g., text retrieval and data mining) as well as component technologies such as optical character recognition (OCR), document structure analysis, signature matching, and authorship attribution.
Domino: Discovering systematic errors with cross-modal embeddings
Authors: Sabri Eyuboglu, Maya Varma, Khaled Kamal Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou 0001, Christopher Ré
Venue: Conference ICLR 2022
Year: 2022

Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data). Then, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our framework - a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35% of settings.
Missingness bias in model debugging
Authors: Saachi Jain, Hadi Salman, Eric Wong 0001, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry
Venue: Conference ICLR 2022
Year: 2022

Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice. Our code is available at https://github.com/madrylab/missingness
Video moment retrieval from text queries via single frame annotation
Authors: Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, Yu-Gang Jiang
Venue: Conference SIGIR 2022
Year: 2022

Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor. In this paper, we look closer into the annotation process and propose a new paradigm called "glance annotation". This paradigm requires the timestamp of only one single random frame, which we refer to as a "glance", within the temporal boundary of the fully supervised counterpart. We argue this is beneficial because comparing to weak supervision, trivial cost is added yet more potential in performance is provided. Under the glance annotation setting, we propose a method named as Video moment retrieval via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input video into clips and contrasts between clips and queries, in which glance guided Gaussian distributed weights are assigned to all clips. Our extensive experiments indicate that ViGA achieves better results than the state-of-the-art weakly supervised methods by a large margin, even comparable to fully supervised methods in some cases.
Frank: a ranking method with fidelity loss
Authors: Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, Wei-Ying Ma
Venue: Conference SIGIR 2007
Year: 2007

Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss notonly inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.
Cala: Complementary association learning for augmenting comoposed image retrieval
Authors: Xintong Jiang, Yaxiong Wang, Mengjian Li, Yujiao Wu, Bingwen Hu, Xueming Qian
Venue: Conference SIGIR 2024
Year: 2024

Composed image retrieval (CIR) is the task of searching target images using an image-text pair as a query. Given the straightforward relation of query pair-target image, the dominant methods follow the learning paradigm of common image-text retrieval and simply model this problem as the query-target matching problem. Particularly, the common practice first encodes the multi-modal query into one feature and then aligns it with the target image. However, such a learning paradigm only explores the naive relation in the triplets. We argue that CIR triplets encompass additional associations besides the primary query-target relation, which is overlooked in existing works. In this paper, we disclose two new relations residing in the triplets by viewing the triplet as a graph node. In analogy with the graph node, we mine two associations of text-bridged image alignment and complementary text reasoning. The text-bridged image alignment considers composed image retrieval as a specialized form of image retrieval, where the query text acts as a bridge between the query image and the target one, and a hinge-based cross attention is proposed to incorporate this relation into the network learning. On the other hand, the association of complementary text reasoning regards composed image retrieval as a specific type of cross-modal retrieval, where the composite two images are used to reason the complementary text. To integrate these views effectively, a twin attention-based compositor is designed. By combining these two types of complementary associations with the explicit query pair-target image relation, we establish a comprehensive set of constraints for composed image retrieval. With the above designs, we finally developed our CaLa, a Complementary Association Learning framework for Augmenting Composed Image Retrieval. Experimental evaluations are conducted on the widely-used CIRR and FashIionIQ benchmarks with multiple backbones to validate the effectiveness of our CaLa. The results demonstrate the superiority of our method in the composed image retrieval task. Our code and models are available at https://github.com/Chiangsonw/CaLa
Depth discontinuities by pixel-to-pixel stereo
Authors: Stan Birchfield, Carlo Tomasi
Venue: Conference ICCV 1998
Year: 1998

An algorithm to detect depth discontinuities from a stereo pair of images is presented. The algorithm matches individual pixels in corresponding scanline pairs while allowing occluded pixels to remain unmatched, then propagates the information between scanlines by means of a fast postprocessor. The algorithm handles large untextured regions, uses a measure of pixel dissimilarity that is insensitive to image sampling, and prunes bad search nodes to increase the speed of dynamic programming. The computation is relatively fast, taking about 1.5 microseconds per pixel per disparity on a workstation. Approximate disparity maps and precise depth discontinuities (along both horizontal and vertical boundaries) are shown for five stereo images containing textured, untextured, fronto-parallel, and slanted objects.
Xai for transformers: Better explanations through conservative propagation
Authors: Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf
Venue: Conference ICML 2022
Year: 2022

Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transparency. Multiple interpretability methods, often based on gradient information, have been proposed. We show that the gradient in a Transformer reflects the function only locally, and thus fails to reliably identify the contribution of input features to the prediction. We identify Attention Heads and LayerNorm as main reasons for such unreliable explanations and propose a more stable way for propagation through these layers. Our proposal, which can be seen as a proper extension of the well-established LRP method to Transformers, is shown both theoretically and empirically to overcome the deficiency of a simple gradient-based approach, and achieves state-of-the-art explanation performance on a broad range of Transformer models and datasets.
Higher-order clique reduction in binary graph cut
Authors: Hiroshi Ishikawa 0002
Venue: Conference CVPR 2009
Year: 2009

We introduce a new technique that can reduce any higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we combine the reduction with the fusion-move and QPBO algorithms to optimize higher-order multi-label problems. While many vision problems today are formulated as energy minimization problems, they have mostly been limited to using first-order energies, which consist of unary and pairwise clique potentials, with a few exceptions that consider triples. This is because of the lack of efficient algorithms to optimize energies with higher-order interactions. Our algorithm challenges this restriction that limits the representational power of the models, so that higher-order energies can be used to capture the rich statistics of natural scenes. To demonstrate the algorithm, we minimize a third-order energy, which allows clique potentials with up to four pixels, in an image restoration problem. The problem uses the fields of experts model, a learned spatial prior of natural images that has been used to test two belief propagation algorithms capable of optimizing higher-order energies. The results show that the algorithm exceeds the BP algorithms in both optimization performance and speed.
Dynamic modality interaction modeling for image-text retrieval
Authors: Leigang Qu, Meng Liu 0006, Jianlong Wu, Zan Gao, Liqiang Nie
Venue: Conference SIGIR 2021
Year: 2021

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although much progress has been made in bridging vision and language, it remains challenging because of the difficult intra-modal reasoning and cross-modal alignment. Existing modality interaction methods have achieved impressive results on public datasets. However, they heavily rely on expert experience and empirical feedback towards the design of interaction patterns, therefore, lacking flexibility. To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To endow the model with the capability of path decision, we integrate a dynamic router in each cell for pattern exploration. As the routers are conditioned on inputs, our model can dynamically learn different activated paths for different data. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, verify the superiority of our model compared with several state-of-the-art baselines.