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"Photographic memory."
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The reminders : a novel
\"What happens when a girl who can't forget befriends a man who's desperate to remember?\"--Jacket.
Book
Commonplace
A LITTLE WHILE back I took some photographs of shore-birds, many of which are migratory and fly to the Arctic in our autumn to breed. Some of the godwits I photographed had orange leg-tags and when I zoomed in I could read the letters and numbers, so I reported these on a website that tracks migratory birds and which tells me these birds were tagged one year ago, in exactly the same place.
Magazine Article
'Photographic Memory': The permanence and impermanence of what we choose to preserve
Since the 1980s, documentarian and Harvard film professor Ross McElwee has been composing wonderful personal memoirs that are both forward-looking and elegiac.
Newspaper Article
STYLE & CULTURE; Seeing their good side; Bill Claxton's 'Photographic Memory' is a book that gathers up his life in pictures
BEYOND THE RAINBOW: Singer-actress Judy Garland in Las Vegas, 1961.; PHOTOGRAPHER: Photographs by William Claxton; WHO'S THE CAT THAT WON'T COP OUT: Oscar-winning composer- musician [Isaac Hayes] in Hollywood, 1995.; PHOTOGRAPHER: Photographs by William Claxton; EASY RIDER: Actor-director-writer Peter Fonda in Montana, 1998.; PHOTOGRAPHER: Photographs by William Claxton; SONGBIRD: Singer-actress Barbra Streisand in New York City, 1964.; PHOTOGRAPHER: Photographs by William Claxton; E1 STILL SHOOTING: [Bill Claxton] remains busy at 75.; PHOTOGRAPHER: George Wilhelm Los Angeles Times Then there is the photo of Claxton's good friend, Tom Pittman, a twentysomething actor who'd been pegged by movie moguls for a meteoric career. The year was 1957 and Pittman loved a young fashion model named Peggy Moffitt, who didn't love him back. She'd fallen, instead, for the tall, handsome photographer, Claxton.
Newspaper Article
False memories in highly superior autobiographical memory individuals
The recent identification of highly superior autobiographical memory (HSAM) raised the possibility that there may be individuals who are immune to memory distortions. We measured HSAM participants’ and age- and sex-matched controls’ susceptibility to false memories using several research paradigms. HSAM participants and controls were both susceptible to false recognition of nonpresented critical lure words in an associative word-list task. In a misinformation task, HSAM participants showed higher overall false memory compared with that of controls for details in a photographic slideshow. HSAM participants were equally as likely as controls to mistakenly report they had seen nonexistent footage of a plane crash. Finding false memories in a superior-memory group suggests that malleable reconstructive mechanisms may be fundamental to episodic remembering. Paradoxically, HSAM individuals may retrieve abundant and accurate autobiographical memories using fallible reconstructive processes.
Journal Article
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.
Journal Article
Technology, Identity, and Inertia Through the Lens of \The Digital Photography Company\
Organizations often experience difficulty when pursuing new technology. Large bodies of research have examined the behavioral, social, and cognitive forces that underlie this phenomenon; however, the role of an organization's identity remains relatively unexplored. Identity comprises insider and outsider perceptions of what is core about an organization. An identity has associated with it a set of norms that represent shared beliefs about legitimate behavior for an organization with that identity. In this paper, technologies that deviate from the expectations associated with an organization's identity are labeled identity-challenging technologies. Based on a comprehensive field-based case study of the entire life history of a company, identity-challenging technologies are found to be difficult to capitalize on for two reasons. First, identity serves as a filter, such that organizational members notice and interpret external stimuli in a manner consistent with the identity. As a result, identity-challenging technological opportunities may be missed. Second, because identity becomes intertwined in the routines, procedures, and beliefs of both organizational and external constituents, explicit efforts to shift identity in order to accommodate identity-challenging technology are difficult. Given the disruptive nature of identity shifts, understanding whether technology is identity challenging is a critical consideration for managers pursuing new technology.
Journal Article
Virtual memory palaces: immersion aids recall
Virtual reality displays, such as head-mounted displays (HMD), afford us a superior spatial awareness by leveraging our vestibular and proprioceptive senses, as compared to traditional desktop displays. Since classical times, people have used memory palaces as a spatial mnemonic to help remember information by organizing it spatially and associating it with salient features in that environment. In this paper, we explore whether using virtual memory palaces in a head-mounted display with head-tracking (HMD condition) would allow a user to better recall information than when using a traditional desktop display with a mouse-based interaction (desktop condition). We found that virtual memory palaces in HMD condition provide a superior memory recall ability compared to the desktop condition. We believe this is a first step in using virtual environments for creating more memorable experiences that enhance productivity through better recall of large amounts of information organized using the idea of virtual memory palaces.
Journal Article
Building Extraction from Remote Sensing Images with Sparse Token Transformers
Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.
Journal Article