4,067 research outputs found
Learning Fully Dense Neural Networks for Image Semantic Segmentation
Semantic segmentation is pixel-wise classification which retains critical
spatial information. The "feature map reuse" has been commonly adopted in CNN
based approaches to take advantage of feature maps in the early layers for the
later spatial reconstruction. Along this direction, we go a step further by
proposing a fully dense neural network with an encoder-decoder structure that
we abbreviate as FDNet. For each stage in the decoder module, feature maps of
all the previous blocks are adaptively aggregated to feed-forward as input. On
the one hand, it reconstructs the spatial boundaries accurately. On the other
hand, it learns more efficiently with the more efficient gradient
backpropagation. In addition, we propose the boundary-aware loss function to
focus more attention on the pixels near the boundary, which boosts the "hard
examples" labeling. We have demonstrated the best performance of the FDNet on
the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when
not considering training on other datasets
Special Purpose Pulsar Telescope for the Detection of Cosmic Gravitational Waves
Pulsars can be used to search for stochastic backgrounds of gravitational
waves of cosmological origin within the very low frequency band (VLF),
to Hz. We propose to construct a special 50 m radio
telescope. Regular timing measurements of about 10 strong millisecond pulsars
will perhaps allow the detection of gravitational waves within VLF or at least
will give a more stringent upper limits.Comment: 5 pages, no figure, Latex fil
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
Vision transformers have achieved significant improvements on various vision
tasks but their quadratic interactions between tokens significantly reduce
computational efficiency. Many pruning methods have been proposed to remove
redundant tokens for efficient vision transformers recently. However, existing
studies mainly focus on the token importance to preserve local attentive tokens
but completely ignore the global token diversity. In this paper, we emphasize
the cruciality of diverse global semantics and propose an efficient token
decoupling and merging method that can jointly consider the token importance
and diversity for token pruning. According to the class token attention, we
decouple the attentive and inattentive tokens. In addition to preserving the
most discriminative local tokens, we merge similar inattentive tokens and match
homogeneous attentive tokens to maximize the token diversity. Despite its
simplicity, our method obtains a promising trade-off between model complexity
and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35%
with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token
diversity, our method can even improve the accuracy of DeiT-T by 0.1% after
reducing its FLOPs by 40%
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation
Recently, semi-supervised semantic segmentation has achieved promising
performance with a small fraction of labeled data. However, most existing
studies treat all unlabeled data equally and barely consider the differences
and training difficulties among unlabeled instances. Differentiating unlabeled
instances can promote instance-specific supervision to adapt to the model's
evolution dynamically. In this paper, we emphasize the cruciality of instance
differences and propose an instance-specific and model-adaptive supervision for
semi-supervised semantic segmentation, named iMAS. Relying on the model's
performance, iMAS employs a class-weighted symmetric intersection-over-union to
evaluate quantitative hardness of each unlabeled instance and supervises the
training on unlabeled data in a model-adaptive manner. Specifically, iMAS
learns from unlabeled instances progressively by weighing their corresponding
consistency losses based on the evaluated hardness. Besides, iMAS dynamically
adjusts the augmentation for each instance such that the distortion degree of
augmented instances is adapted to the model's generalization capability across
the training course. Not integrating additional losses and training procedures,
iMAS can obtain remarkable performance gains against current state-of-the-art
approaches on segmentation benchmarks under different semi-supervised partition
protocols
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