Context-aware CNNs for person head detection

Person detection is a key problem for many computer vision tasks. While face detection has reached maturity, detecting people under a full variation of camera view-points, human poses, lighting conditions and occlusions is still a difficult challenge. In this work we focus on detecting human heads in natural scenes. Starting from the recent local R-CNN object detector, we extend it with two types of contextual cues. First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image. Second, we explicitly model pairwise relations among objects and train a Pairwise CNN model using a structured-output surrogate loss. The Local, Global and Pairwise models are combined into a joint CNN framework. To train and test our full model, we introduce a large dataset composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate our method and demonstrate improvements of person head detection against several recent baselines in three datasets. We also show improvements of the detection speed provided by our model.Comment: To appear in International Conference on Computer Vision (ICCV), 201

Essays on the Economic Consequences of International Pension Accounting Standard IAS19

This thesis examines the economic consequences of the adoption of international pension accounting standard IAS19 Revised (IAS19R) on pension asset allocation decisions by applying a difference-in-differences with propensity score matching method. The publication of IAS19R in 2011 marked a fundamental change to pension reporting in financial statements. In particular, it had a significant impact on (1) how sponsor firms recognise net pension assets/liabilities on the balance sheet, (2) the calculation and recognition of pension expenses, (3) the presentation of re-measurement (actuarial gains and losses), treatment of which had been heavily debated by academics and practitioners, and (4) disclosure requirements for pension schemes, which had been criticised as “excessive” under IAS19. This research examines the “real effect” of IAS19R adoption on management investment decisions. Using a difference-in-differences with propensity score matching method, the results suggest that, on average, UK sponsor firms affected by IAS19R have reduced their risk taking in pension investments post-IAS19R, both over time and compared with a control sample of unaffected US firms (matched by propensity score matching). The results of sensitivity analysis also suggest that UK sponsor firms tried to avoid the expensive liquidity costs of asset re-allocation by switching their pension plan asset allocations gradually during the period around the publication and adoption of IAS19R. Furthermore, the outcomes of sensitivity tests suggest a positive relationship between equity investment levels, and firms’ leverage and cash flow risk, consistent with the “risk-shifting” hypothesis documented in the previous literature. The thesis also applies a manual textual analysis on the comment letters sent by industrial firms to the IASB to provide their opinions on the IAS19R Exposure Draft. The analysis describes and tabulates the arguments raised by these firms on three main amendment areas of IAS19: recognition, presentation and disclosure. Based on this description, this part aims to motivate the empirical research mentioned previously and shed light on the other potential consequences of IAS19R adoption. These consequences include: the management of funding might be driven by accounting rules rather than management rules; the increasing volatility of balance sheet; de-risking in the pension plan portfolio following the adoption of IAS19R; the diminishing of financial statement “true and fair view” and its usefulness due to the abolition of expected rate of return and excessive requirements on pension disclosure. Furthermore, the study also suggests that the lobbying behaviour of these firms on the standard setting process is consistent with the predictions of Positive Accounting Theory

Tube-CNN: Modeling temporal evolution of appearance for object detection in video

Object detection in video is crucial for many applications. Compared to images, video provides additional cues which can help to disambiguate the detection problem. Our goal in this paper is to learn discriminative models for the temporal evolution of object appearance and to use such models for object detection. To model temporal evolution, we introduce space-time tubes corresponding to temporal sequences of bounding boxes. We propose two CNN architectures for generating and classifying tubes, respectively. Our tube proposal network (TPN) first generates a large number of spatio-temporal tube proposals maximizing object recall. The Tube-CNN then implements a tube-level object detector in the video. Our method improves state of the art on two large-scale datasets for object detection in video: HollywoodHeads and ImageNet VID. Tube models show particular advantages in difficult dynamic scenes.Comment: 13 pages, 8 figures, technical repor

Some Overviews on Organic Agriculture Apply Circular Economy

The paper provides an overview on agriculture apply circular economy (CE). It provides the concepts of circular economy and organic agriculture. The paper used secondary researches from topics, documents related to organic agriculture apply CE. With results which the previous studies mentioned, the paper bases on different CE theoretical approaches to clarify the concept of organic agriculture in the direction of circular economy. Keywords: organic agriculture, circular economy DOI: 10.7176/JESD/13-20-01 Publication date:October 31st 202

Zero-Shot Semantic Segmentation

International audienceSemantic segmentation models are limited in their ability to scale to large numbers of object classes. In this paper, we introduce the new task of zero-shot semantic segmentation: learning pixel-wise classifiers for never-seen object categories with zero training examples. To this end, we present a novel architecture, ZS3Net, combining a deep visual segmentation model with an approach to generate visual representations from semantic word embeddings. By this way, ZS3Net addresses pixel classification tasks where both seen and unseen categories are faced at test time (so called "generalized" zero-shot classification). Performance is further improved by a self-training step that relies on automatic pseudo-labeling of pixels from unseen classes. On the two standard segmentation datasets, Pascal-VOC and Pascal-Context, we propose zero-shot benchmarks and set competitive baselines. For complex scenes as ones in the Pascal-Context dataset, we extend our approach by using a graph-context encoding to fully leverage spatial context priors coming from class-wise segmentation maps.Code and models are available at: https://github.com/valeoai/zero_shot_semantic_segmentatio