Abstract

Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset.

Abstract

Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression function directly on the input images fail to do so. To this end, we propose to use a higher level representation to regress the head pose while using deep learning architectures. More specifically, we use the uncertainty maps in the form of 2D soft localization heatmap images over five facial keypoints, namely left ear, right ear, left eye, right eye and nose, and pass them through an convolutional neural network to regress the head-pose. We show head pose estimation results on two challenging benchmarks BIWI and AFLW and our approach surpasses the state of the art on both the datasets. Index Terms— Image analysis, Pose estimation

Abstract

Abstract— We propose a novel pipeline that blends encodings from natural language and 3D semantic maps obtained from visual imagery to generate local trajectories that are executed by a low-level controller. The pipeline precludes the need for a prior registered map through a local waypoint generator neural network. The waypoint generator network (WGN) maps semantics and natural language encodings (NLE) to local waypoints. A local planner then generates a trajectory from the ego location of the vehicle (an outdoor car in this case) to these locally generated waypoints while a low-level controller executes these plans faithfully. The efficacy of the pipeline is verified in the CARLA simulator environment as well as on local semantic maps built from real-world KITTI dataset. In both these environments (simulated and real-world) we show the ability of the WGN to generate waypoints accurately by mapping NLE of varying sequence lengths and levels of complexity. We compare with baseline approaches and show significant performance gain over them. And finally, we show real implementations on our electric car verifying that the pipeline lends itself to practical and tangible realizations in uncontrolled outdoor settings. In loop execution of the proposed pipeline that involves repetitive invocations of the network is critical for any such language-based navigation framework. This effort successfully accomplishes this thereby bypassing the need for prior metric maps or strategies for metric level localization during traversal.

Abstract

Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset

Abstract

In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014.

Abstract

Abstract— We present here, a novel network architecture called MergeNet for discovering small obstacles for on-road scenes in the context of autonomous driving. The basis of the architecture rests on the central consideration of training with less amount of data since the physical setup and the annotation process for small obstacles is hard to scale. For making effective use of the limited data, we propose a multi-stage training procedure involving weight-sharing, separate learning of low and high level features from the RGBD input and a refining stage which learns to fuse the obtained complementary features. The model is trained and evaluated on the Lost and Found dataset and is able to achieve state-of-art results with just 135 images in comparison to the 1000 images used by the previous benchmark. Additionally, we also compare our results with recent methods trained on 6000 images and show that our method achieves comparable performance with only 1000 training samples.

Abstract

The current Document Image Quality Assessment (DIQA) algorithms directly relate the Optical Character Recognition (OCR) accuracies with the quality of the document to build supervised learning frameworks. This direct correlation has two major limitations: (a) OCR may be affected by factors independent of the quality of the capture and (b) it cannot account for blur variations within an image. An alternate possibility is to quantify the quality of capture using human judgement, however, it is subjective and prone to error. In this work, we build upon the idea of Spatial Frequency Response (SFR) to reliably quantify the quality of a document image. We present through quantitative and qualitative experiments that the proposed metric leads to significant improvement in document quality prediction in contrast to using OCR as ground truth.

Abstract

Uneven illumination and shadows in document images cause a challenge for digitization applications and automated workflows. In this work, we propose a new method to recover unshadowed document images from images with shadows/uneven illumination. We pose this problem as one of estimating the shading and reflectance components of the given original image. Our method first estimates the shading and uses it to compute the reflectance. The output reflectance map is then used to improve the shading and the process is repeated in an iterative manner. The iterative procedure allows for a gradual compensation and allows our algorithm to even handle difficult hard shadows without introducing any artifacts. Experiments over two different datasets demonstrate the efficacy of our algorithm and the low computation complexity makes it suitable for most practical applications.

Abstract

We present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re-edit a wide-angle video recording or a close-up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re-edit an entire movie in one shot. Our algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information, and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state-of-the-art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to gaze driven re-editing [JSSH15] and letterboxing methods, especially for wide-angle static camera recordings.

Abstract

Abstract. We propose a new long video dataset 1 (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.