184 research outputs found
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
Towards Accurate One-Stage Object Detection with AP-Loss
One-stage object detectors are trained by optimizing classification-loss and
localization-loss simultaneously, with the former suffering much from extreme
foreground-background class imbalance issue due to the large number of anchors.
This paper alleviates this issue by proposing a novel framework to replace the
classification task in one-stage detectors with a ranking task, and adopting
the Average-Precision loss (AP-loss) for the ranking problem. Due to its
non-differentiability and non-convexity, the AP-loss cannot be optimized
directly. For this purpose, we develop a novel optimization algorithm, which
seamlessly combines the error-driven update scheme in perceptron learning and
backpropagation algorithm in deep networks. We verify good convergence property
of the proposed algorithm theoretically and empirically. Experimental results
demonstrate notable performance improvement in state-of-the-art one-stage
detectors based on AP-loss over different kinds of classification-losses on
various benchmarks, without changing the network architectures. Code is
available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material,
accepted to CVPR 201
Is Vanilla MLP in Neural Radiance Field Enough for Few-shot View Synthesis?
Neural Radiance Field (NeRF) has achieved superior performance for novel view
synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a
volume rendering procedure, however, when fewer known views are given (i.e.,
few-shot view synthesis), the model is prone to overfit the given views. To
handle this issue, previous efforts have been made towards leveraging learned
priors or introducing additional regularizations. In contrast, in this paper,
we for the first time provide an orthogonal method from the perspective of
network structure. Given the observation that trivially reducing the number of
model parameters alleviates the overfitting issue, but at the cost of missing
details, we propose the multi-input MLP (mi-MLP) that incorporates the inputs
(i.e., location and viewing direction) of the vanilla MLP into each layer to
prevent the overfitting issue without harming detailed synthesis. To further
reduce the artifacts, we propose to model colors and volume density separately
and present two regularization terms. Extensive experiments on multiple
datasets demonstrate that: 1) although the proposed mi-MLP is easy to
implement, it is surprisingly effective as it boosts the PSNR of the baseline
from to . 2) the overall framework achieves state-of-the-art
results on a wide range of benchmarks. We will release the code upon
publication.Comment: Accepted by CVPR 202
- …