Passenger satisfaction of interpretive programs: evaluation of the National Park Service and Amtrak partnership

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on August 22, 2007)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2006.Dissertations, Academic -- University of Missouri--Columbia -- Parks, recreation and tourism.The Trails & Rails program is an innovative partnership between the National Park Service and Amtrak. It allows train passengers the opportunity to attend educational programs focused on the natural and cultural heritage along selected routes. In 2005, over 400,000 passengers attended in the Trails & Rails programs. Other than anecdotal evidence, little information existed on passenger satisfaction. This pilot study measured satisfaction of the Trails & Rails program by surveying rail passengers on two trains in the mid-west region, using the Expectancy- Disconfirmation theory. Independent samples t-test were employed in the study. Results revealed that passengers were satisfied with the Trails & Rails program, including measures of interpreter characteristics, message quality, and program benefits. The findings provided useful implications for the program development and marketing strategy

Competitive learning and joint learning in international joint venture

As is the case in most normal independent organizations, international joint ventures (IJVs) are expected to be influenced by both stabilizing and destabilizing forces. This research emphasizes that inter-partner learning does not just play a role in IJV instability, but also helps sustain a stable IJV. More importantly, this study empirically tested the interpartner learning mechanism, through which instability/stability occurs. Specifically, it focused on how resource structure, knowledge characteristics, relational mechanism, and partner fit influence different types of learning, which provided a model explicating a mix of driving forces and restraining forces of IJV instability. A survey study was conducted in China, which is currently the largest emerging economy in the world. A great number of foreign firms have been forming IJVs with local Chinese firms. During this process, both foreign firms and local firms may need some guidance as they develop collaboration, particularly in how to learn from and how to learn together with each other. There were 124 usable questionnaires collected from one party of the IJVs for the study. The results showed that competitive learning destabilized IJVs, while joint learning stabilized IJVs. Furthermore, numerous antecedents to interpartner learning were elucidated and supported by statistical evaluations, which provided important prescriptive information for interpartner learning. Also, the study provided managerial implications from different perspectives. The IJV partner firm that would like to exert either or both learning behaviors can work on fostering the antecedents. This dissertation ended with a discussion of limitations and the future research.Includes bibliographical references (pages 147-161)

Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos

Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. However, existing approaches rely on multi-stage paradigms, where the person detection and tracking stages are performed in a multi-person setting, while temporal dynamics are only modeled for one person at a time. Consequently, their performance is severely limited by the lack of inter-person interactions in the spatial-temporal mesh recovery, as well as by detection and tracking defects. To address these challenges, we propose the Coordinate transFormer (CoordFormer) that directly models multi-person spatial-temporal relations and simultaneously performs multi-mesh recovery in an end-to-end manner. Instead of partitioning the feature map into coarse-scale patch-wise tokens, CoordFormer leverages a novel Coordinate-Aware Attention to preserve pixel-level spatial-temporal coordinate information. Additionally, we propose a simple, yet effective Body Center Attention mechanism to fuse position information. Extensive experiments on the 3DPW dataset demonstrate that CoordFormer significantly improves the state-of-the-art, outperforming the previously best results by 4.2%, 8.8% and 4.7% according to the MPJPE, PAMPJPE, and PVE metrics, respectively, while being 40% faster than recent video-based approaches. The released code can be found at https://github.com/Li-Hao-yuan/CoordFormer.Comment: ICCV 202

GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

Image-based Virtual Try-ON aims to transfer an in-shop garment onto a specific person. Existing methods employ a global warping module to model the anisotropic deformation for different garment parts, which fails to preserve the semantic information of different parts when receiving challenging inputs (e.g, intricate human poses, difficult garments). Moreover, most of them directly warp the input garment to align with the boundary of the preserved region, which usually requires texture squeezing to meet the boundary shape constraint and thus leads to texture distortion. The above inferior performance hinders existing methods from real-world applications. To address these problems and take a step towards real-world virtual try-on, we propose a General-Purpose Virtual Try-ON framework, named GP-VTON, by developing an innovative Local-Flow Global-Parsing (LFGP) warping module and a Dynamic Gradient Truncation (DGT) training strategy. Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs.On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem. Furthermore, our GP-VTON can be easily extended to multi-category scenario and jointly trained by using data from different garment categories. Extensive experiments on two high-resolution benchmarks demonstrate our superiority over the existing state-of-the-art methods.Comment: 8 pages, 8 figures, The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR