Image Reconstruction from Bag-of-Visual-Words

The objective of this work is to reconstruct an original image from Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means of identifying the characteristics of features. Additionally, it enables us to generate novel images via features. Although BoVW is the de facto standard feature for image recognition and retrieval, successful image reconstruction from BoVW has not been reported yet. What complicates this task is that BoVW lacks the spatial information for including visual words. As described in this paper, to estimate an original arrangement, we propose an evaluation function that incorporates the naturalness of local adjacency and the global position, with a method to obtain related parameters using an external image database. To evaluate the performance of our method, we reconstruct images of objects of 101 kinds. Additionally, we apply our method to analyze object classifiers and to generate novel images via BoVW

ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.Comment: To appear at ECCV 201

Can adversarial networks hallucinate occluded people with a plausible aspect?

When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world

Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

The egocentric and exocentric viewpoints of a human activity look dramatically different, yet invariant representations to link them are essential for many potential applications in robotics and augmented reality. Prior work is limited to learning view-invariant features from paired synchronized viewpoints. We relax that strong data assumption and propose to learn fine-grained action features that are invariant to the viewpoints by aligning egocentric and exocentric videos in time, even when not captured simultaneously or in the same environment. To this end, we propose AE2, a self-supervised embedding approach with two key designs: (1) an object-centric encoder that explicitly focuses on regions corresponding to hands and active objects; (2) a contrastive-based alignment objective that leverages temporally reversed frames as negative samples. For evaluation, we establish a benchmark for fine-grained video understanding in the ego-exo context, comprising four datasets -- including an ego tennis forehand dataset we collected, along with dense per-frame labels we annotated for each dataset. On the four datasets, our AE2 method strongly outperforms prior work in a variety of fine-grained downstream tasks, both in regular and cross-view settings.Comment: Project website: https://vision.cs.utexas.edu/projects/AlignEgoExo

Pose guided person image generation

This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details.Comment: Xu Jia and Qianru Sun contribute equally. Accepted in Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017

Learning human activities and poses with interconnected data sources

Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such as action recognition, pose estimation, human-object interaction, and activity detection. Knowledge of actions and poses could benefit many applications, including video search, surveillance, auto-tagging, event detection, and human-computer interfaces. To understand humans' actions and poses, we need to address several challenges. First, humans are able to perform an enormous amount of poses. For example, simply to move forward, we can do crawling, walking, running, and sprinting. These poses all look different and require examples to cover these variations. Second, the appearance of a person's pose changes when looking from different viewing angles. The learned action model needs to cover the variations from different views. Third, many actions involve interactions between people and other objects, so we need to consider the appearance change corresponding to that object as well. Fourth, collecting such data for learning is difficult and expensive. Last, even if we can learn a good model for an action, to localize when and where the action happens in a long video remains a difficult problem due to the large search space. My key idea to alleviate these obstacles in learning humans' actions and poses is to discover the underlying patterns that connect the information from different data sources. Why will there be underlying patterns? The intuition is that all people share the same articulated physical structure. Though we can change our pose, there are common regulations that limit how our pose can be and how it can move over time. Therefore, all types of human data will follow these rules and they can serve as prior knowledge or regularization in our learning framework. If we can exploit these tendencies, we are able to extract additional information from data and use them to improve learning of humans' actions and poses. In particular, we are able to find patterns for how our pose could vary over time, how our appearance looks in a specific view, how our pose is when we are interacting with objects with certain properties, and how part of our body configuration is shared across different poses. If we could learn these patterns, they can be used to interconnect and extrapolate the knowledge between different data sources. To this end, I propose several new ways to connect human activity data. First, I show how to connect snapshot images and videos by exploring the patterns of how our pose could change over time. Building on this idea, I explore how to connect humans' poses across multiple views by discovering the correlations between different poses and the latent factors that affect the viewpoint variations. In addition, I consider if there are also patterns connecting our poses and nearby objects when we are interacting with them. Furthermore, I explore how we can utilize the predicted interaction as a cue to better address existing recognition problems including image re-targeting and image description generation. Finally, after learning models effectively incorporating these patterns, I propose a robust approach to efficiently localize when and where a complex action happens in a video sequence. The variants of my proposed approaches offer a good trade-off between computational cost and detection accuracy. My thesis exploits various types of underlying patterns in human data. The discovered structure is used to enhance the understanding of humans' actions and poses. By my proposed methods, we are able to 1) learn an action with very few snapshots by connecting them to a pool of label-free videos, 2) infer the pose for some views even without any examples by connecting the latent factors between different views, 3) predict the location of an object that a person is interacting with independent of the type and appearance of that object, then use the inferred interaction as a cue to improve recognition, and 4) localize an action in a complex long video. These approaches improve existing frameworks for understanding humans' actions and poses without extra data collection cost and broaden the problems that we can tackle.Computer Science

Towards Learning Representations in Visual Computing Tasks

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Embodied learning for visual recognition

The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the wake of this, my focus has been on understanding and exploiting alternate "free" sources of supervision available to visual learning agents that are situated within real environments. For example, even simply moving from orderless image collections to continuous visual observations offers opportunities to understand the dynamics and other physical properties of the visual world. Further, embodied agents may have the abilities to move around their environment and/or effect changes within it, in which case these abilities offer new means to acquire useful supervision. In this dissertation, I present my work along this and related directions.Electrical and Computer Engineerin