1,579 research outputs found
Recurrent Session Approach to Generative Association Rule based Recommendation
This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot. In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolutional neural network (CNN) architecture is proposed
which could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,
AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very
different from natural images, the fine-tune strategy still works well. At
last, we prove that our method could also work well on 2D skeleton video data.
We achieve the state-of-the-art results on the popular benchmard datasets e.g.
NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge
NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods
by a large margion, which proves the efficacy of the proposed method
Neural Scene Decoration from a Single Photograph
Furnishing and rendering indoor scenes has been a long-standing task for
interior design, where artists create a conceptual design for the space, build
a 3D model of the space, decorate, and then perform rendering. Although the
task is important, it is tedious and requires tremendous effort. In this paper,
we introduce a new problem of domain-specific indoor scene image synthesis,
namely neural scene decoration. Given a photograph of an empty indoor space and
a list of decorations with layout determined by user, we aim to synthesize a
new image of the same space with desired furnishing and decorations. Neural
scene decoration can be applied to create conceptual interior designs in a
simple yet effective manner. Our attempt to this research problem is a novel
scene generation architecture that transforms an empty scene and an object
layout into a realistic furnished scene photograph. We demonstrate the
performance of our proposed method by comparing it with conditional image
synthesis baselines built upon prevailing image translation approaches both
qualitatively and quantitatively. We conduct extensive experiments to further
validate the plausibility and aesthetics of our generated scenes. Our
implementation is available at
\url{https://github.com/hkust-vgd/neural_scene_decoration}.Comment: ECCV 2022 paper. 14 pages of main content, 4 pages of references, and
11 pages of appendi
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis
Self-supervised learning (SSL) has recently achieved promising performance
for 3D medical image analysis tasks. Most current methods follow existing SSL
paradigm originally designed for photographic or natural images, which cannot
explicitly and thoroughly exploit the intrinsic similar anatomical structures
across varying medical images. This may in fact degrade the quality of learned
deep representations by maximizing the similarity among features containing
spatial misalignment information and different anatomical semantics. In this
work, we propose a new self-supervised learning framework, namely Alice, that
explicitly fulfills Anatomical invariance modeling and semantic alignment via
elaborately combining discriminative and generative objectives. Alice
introduces a new contrastive learning strategy which encourages the similarity
between views that are diversely mined but with consistent high-level
semantics, in order to learn invariant anatomical features. Moreover, we design
a conditional anatomical feature alignment module to complement corrupted
embeddings with globally matched semantics and inter-patch topology
information, conditioned by the distribution of local image content, which
permits to create better contrastive pairs. Our extensive quantitative
experiments on three 3D medical image analysis tasks demonstrate and validate
the performance superiority of Alice, surpassing the previous best SSL
counterpart methods and showing promising ability for united representation
learning. Codes are available at https://github.com/alibaba-damo-academy/alice.Comment: This paper has been accepted by ICCV 2023 (oral
DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
We present DreamCraft3D, a hierarchical 3D content generation method that
produces high-fidelity and coherent 3D objects. We tackle the problem by
leveraging a 2D reference image to guide the stages of geometry sculpting and
texture boosting. A central focus of this work is to address the consistency
issue that existing works encounter. To sculpt geometries that render
coherently, we perform score distillation sampling via a view-dependent
diffusion model. This 3D prior, alongside several training strategies,
prioritizes the geometry consistency but compromises the texture fidelity. We
further propose Bootstrapped Score Distillation to specifically boost the
texture. We train a personalized diffusion model, Dreambooth, on the augmented
renderings of the scene, imbuing it with 3D knowledge of the scene being
optimized. The score distillation from this 3D-aware diffusion prior provides
view-consistent guidance for the scene. Notably, through an alternating
optimization of the diffusion prior and 3D scene representation, we achieve
mutually reinforcing improvements: the optimized 3D scene aids in training the
scene-specific diffusion model, which offers increasingly view-consistent
guidance for 3D optimization. The optimization is thus bootstrapped and leads
to substantial texture boosting. With tailored 3D priors throughout the
hierarchical generation, DreamCraft3D generates coherent 3D objects with
photorealistic renderings, advancing the state-of-the-art in 3D content
generation. Code available at https://github.com/deepseek-ai/DreamCraft3D.Comment: Project Page: https://mrtornado24.github.io/DreamCraft3D
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