57 research outputs found
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition
Real-world data often have a long-tailed distribution, where the number of
samples per class is not equal over training classes. The imbalanced data form
a biased feature space, which deteriorates the performance of the recognition
model. In this paper, we propose a novel long-tailed recognition method to
balance the latent feature space. First, we introduce a MixUp-based data
augmentation technique to reduce the bias of the long-tailed data. Furthermore,
we propose a new supervised contrastive learning method, named Supervised
contrastive learning on Mixed Classes (SMC), for blended images. SMC creates a
set of positives based on the class labels of the original images. The
combination ratio of positives weights the positives in the training loss. SMC
with the class-mixture-based loss explores more diverse data space, enhancing
the generalization capability of the model. Extensive experiments on various
benchmarks show the effectiveness of our one-stage training method
Point-DynRF: Point-based Dynamic Radiance Fields from a Monocular Video
Dynamic radiance fields have emerged as a promising approach for generating
novel views from a monocular video. However, previous methods enforce the
geometric consistency to dynamic radiance fields only between adjacent input
frames, making it difficult to represent the global scene geometry and
degenerates at the viewpoint that is spatio-temporally distant from the input
camera trajectory. To solve this problem, we introduce point-based dynamic
radiance fields (\textbf{Point-DynRF}), a novel framework where the global
geometric information and the volume rendering process are trained by neural
point clouds and dynamic radiance fields, respectively. Specifically, we
reconstruct neural point clouds directly from geometric proxies and optimize
both radiance fields and the geometric proxies using our proposed losses,
allowing them to complement each other. We validate the effectiveness of our
method with experiments on the NVIDIA Dynamic Scenes Dataset and several
causally captured monocular video clips.Comment: WACV202
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