mtybilly/Instruct2DS huggingface dataset:
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
Authors: Kui Jia, Dit-Yan Yeung, Bertram E. Shi Lin Sun
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects. Inspired by the success of convolutional neural networks (CNN) for image classification, recent attempts have been made to learn 3D CNNs for recognizing human actions in videos. However, partly due to the high complexity of training 3D convolution kernels and the need for large quantities of training videos, only limited success has been reported. This has triggered us to investigate in this paper a new deep architecture which can handle 3D signals more effectively. Specifically, we propose factorized spatio-temporal convolutional networks (FstCN) that factorize the original 3D convolution kernel learning as a sequential process of learning 2D spatial kernels in the lower layers (called spatial convolutional layers), followed by learning 1D temporal kernels in the upper layers (called temporal convolutional layers). We introduce a novel transformation and permutation operator to make factorization in FstCN possible. Moreover, to address the issue of sequence alignment, we propose an effective training and inference strategy based on sampling multiple video clips from a given action video sequence. We have tested FstCN on two commonly used benchmark datasets (UCF-101 and HMDB-51). Without using auxiliary training videos to boost the performance, FstCN outperforms existing CNN based methods and achieves comparable performance with a recent method that benefits from using auxiliary training videos.
Bayesian Non-Parametric Inference for Manifold Based MoCap Representation
Authors: Valsamis Ntouskos, Marta Sanzari, Fiora Pirri Fabrizio Natola
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We propose a novel approach to human action recognition, with motion capture data (MoCap), based on  grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach,  generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment.
Semantic Video Entity Linking Based on Visual Content and Metadata
Authors: Xitong Yang, Jiebo Luo Yuncheng Li
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Video entity linking, which connects online videos to the related entities in a semantic knowledge base, can enable a wide variety of video based applications including video retrieval and video recommendation. Most existing systems for video entity linking rely on video metadata. In this paper, we propose to exploit video visual content to improve video entity linking. In the proposed framework, videos are first linked to entity candidates using a text-based method. Next, the entity candidates are verified and reranked according to visual content. In order to properly handle large variations in visual content matching, we propose to use Multiple Instance Metric Learning to learn a "set to sequence'' metric for this specific matching problem. To evaluate the proposed framework, we collect and annotate 1912 videos crawled from the YouTube open API. Experiment results have shown consistent gains by the proposed framework over several strong baselines.
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Authors: Lamberto Ballan, Li Fei-Fei Justin Johnson
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically; in contrast, our nonparametric treatment allows our model to perform well even when the vocabulary of metadata changes between training and testing. We perform comprehensive experiments on the NUS-WIDE dataset, where we show that our model outperforms state-of-the-art methods for multilabel image annotation even when our model is forced to generalize to new types of metadata.
Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-Encoders
Authors: Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, Baining Guo Huan Yang
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: With the growing popularity of short-form video sharing platforms such as Instagram and Vine, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM) cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of downloaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised setting.
Learning Visual Clothing Style With Heterogeneous Dyadic Co-Occurrences
Authors: Balazs Kovacs, Sean Bell, Julian McAuley, Kavita Bala, Serge Belongie Andreas Veit
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like `What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data; in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.
Text Flow: A Unified Text Detection System in Natural Scene Images
Authors: Yifeng Pan, Chang Huang, Shijian Lu, Kai Yu, Chew Lim Tan Shangxuan Tian
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: The prevalent scene text detection approach follows four sequential steps comprising character candidate detection, false character candidate removal, text line extraction, and  text line verification. However, errors occur and accumulate throughout each of these sequential steps which often lead to low detection performance. To address these issues, we propose a unified scene text detection system, namely Text Flow, by utilizing the minimum cost (min-cost) flow network model. With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively. The proposed technique has been tested on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a multilingual dataset and it outperforms the state-of-the-art methods on all three datasets with much higher recall and F-score. The good performance on the multilingual dataset shows that the proposed technique can be used for the detection of texts in different languages.
Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos
Authors: Jagannadan Varadarajan, Ramanathan Subramanian, Samuel Rota Bulo, Narendra Ahuja, Oswald Lanz Elisa Ricci
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e.g., a cocktail party captured by surveillance cameras). Differing from related works that have (i) coupled head and body pose learning by exploiting the limited range of orientations that the two can jointly take, or (ii) determined F-formations based on the mutual head (but not body) orientations of in- teractors, we present a unified framework to jointly infer both (i) and (ii). Apart from exploiting spatial and orien- tation relationships, we also integrate cues pertaining to temporal consistency and occlusions, which are beneficial while handling low-resolution data under surveillance set- tings. Efficacy of the joint inference framework reflects via increased head, body pose and F-formation estimation ac- curacy over the state-of-the-art, as confirmed by extensive experiments on two social datasets.
Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!
Authors: Ali Farhadi, Linda Shapiro Bilge Soran
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2% when only 10 frames of an action are seen (2/3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7% when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3% when the prediction is made at every time step.
Partial Person Re-Identification
Authors: Xiang Li, Tao Xiang, Shengcai Liao, Jianhuang Lai, Shaogang Gong Wei-Shi Zheng
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views. This differs significantly from the conventional person re-id setting where it is assumed that the full body of a person is detected and aligned. To solve this more challenging and realistic re-id problem without the implicit assumption of manual body-parts alignment, we propose a matching framework consisting of 1) a local patch-level matching model based on a novel sparse representation classification formulation with explicit patch ambiguity modelling, and 2) a global part-based matching model providing complementary spatial layout information. Our framework is evaluated on a new partial person re-id dataset as well as two existing datasets modified to include partial person images. The results show that the proposed method outperforms significantly existing re-id methods as well as other partial visual matching methods.
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering With Corrupted and Incomplete Data
Authors: Mathieu Salzmann, Hongdong Li Pan Ji
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces). In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods. Our analysis lets us derive a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise. We justify our method by intuitive examples and the matrix perturbation theory. We then show how this approach can be extended to handle missing data, thus yielding an efficient and general subspace clustering algorithm. We demonstrate the benefits of our approach over state-of-the-art subspace clustering methods on several challenging motion segmentation and face clustering problems, where the data includes corruptions and missing measurements.
Multiple Hypothesis Tracking Revisited
Authors: Fuxin Li, Arridhana Ciptadi, James M. Rehg Chanho Kim
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge.
Learning to Track: Online Multi-Object Tracking by Decision Making
Authors: Alexandre Alahi, Silvio Savarese Yu Xiang
Published in: IEEE International Conference on Computer Vision (ICCV), 2015 (Main Conference Track), 2015

Abstract: Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving. In tracking-by-detection, a major challenge of online MOT is how to robustly associate noisy object detections on a new video frame with previously tracked objects. In this work, we formulate the online MOT problem as decision making in Markov Decision Processes (MDPs), where the lifetime of an object is modeled with a MDP. Learning a similarity function for data association is equivalent to learning a policy for the MDP, and the policy learning is approached in a reinforcement learning fashion which benefits from both advantages of offline-learning and online-learning for data association. Moreover, our framework can naturally handle the birth/death and appearance/disappearance of targets by treating them as state transitions in the MDP while leveraging existing online single object tracking methods. We conduct experiments on the MOT Benchmark to verify the effectiveness of our method.