MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table

Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exceptionally fast training by reducing the complexity of multilayer perceptron (MLP) networks. However, storing features in dense grids demands a substantial amount of memory space, resulting in a notable memory bottleneck within computer system. Consequently, it leads to a significant increase in training times without prior hyper-parameter tuning. To address this issue, in this work, we are the first to propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality. Specifically, we first design a mixed-feature hash encoding to adaptively mix part of multi-level feature grids and map it to a single hash table. Following that, in order to obtain the correct index of a grid point, we further develop an index transformation method that transforms indices of an arbitrary level grid to those of a canonical grid. Extensive experiments benchmarking with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimizing the modification of the learnt model for old tasks. This inevitably limits the backward knowledge transfer from the new task to the old tasks, because judicious model updates could possibly improve the learning performance of the old tasks as well. To tackle this problem, we first theoretically analyze the conditions under which updating the learnt model of old tasks could be beneficial for CL and also lead to backward knowledge transfer, based on the gradient projection onto the input subspaces of old tasks. Building on the theoretical analysis, we next develop a ContinUal learning method with Backward knowlEdge tRansfer (CUBER), for a fixed capacity neural network without data replay. In particular, CUBER first characterizes the task correlation to identify the positively correlated old tasks in a layer-wise manner, and then selectively modifies the learnt model of the old tasks when learning the new task. Experimental studies show that CUBER can even achieve positive backward knowledge transfer on several existing CL benchmarks for the first time without data replay, where the related baselines still suffer from catastrophic forgetting (negative backward knowledge transfer). The superior performance of CUBER on the backward knowledge transfer also leads to higher accuracy accordingly.Comment: Published as a conference paper at NeurIPS 202

Touching the Future: The Effects of Gesture-Based Interaction on Virtual Product Experience

With the popularity of touchscreen tablets and gesture control devices, the role of touch in online consumer behavior has become increasingly important. This study aims to investigate how sense of touch evoked by various interaction modes (i.e., mouse-driven interaction, touchscreen gesture interaction and mid-air gesture interaction) influences virtual product experience. Drawing on Feelings-as-Information Theory and Cognitive-Affective Framework in virtual product experience, we propose that sense of touch could influence consumer purchase intention by reducing product uncertainty and improving product attachment; furthermore, these effects are contingent on product characteristics, i.e., importance of product haptics and product valence. Accordingly, two lab experiments are designed. Potential theoretical contributions, practical implications as well as future research directions are discussed

Embodied Persuasion: How Holding Your Smartphone Changes Your Product Perception

Online shopping through mobile devices has dramatically increased worldwide. This research investigates the role embodied interactions may play in stimulating virtual product experience in mobile commerce settings. Drawing on research on virtual product experience and embodied cognition, we hypothesize that holding a mobile device in hands (vs. putting the mobile device on the table) is more likely to create an illusion that the products being viewed are actually present in the real world and to stimulate imagery consumption experience, leading to higher purchase intention and choice satisfaction. This effect is more salient for desirable products than for undesirable products. We describe an experiment design for testing the hypotheses, report preliminary data analysis results, and discuss the potential theoretical and practical implications of this study

Diacenaphthylene-fused benzo[1,2-b:4,5-b‘]dithiophenes: polycyclic heteroacenes containing full-carbon five-membered aromatic rings

We herein report on an efficient synthesis of diacenaphthylenefused benzo[1,2-b:4,5-b’]dithiphenes and demonstrate that their packing structure in the solid state depends on the substituent groups. These compounds form dimers with their radical cations in high solution concentration and display good field-effect mobility