1,306 research outputs found
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Generating 3D point clouds is challenging yet highly desired. This work
presents a novel autoregressive model, PointGrow, which can generate diverse
and realistic point cloud samples from scratch or conditioned on semantic
contexts. This model operates recurrently, with each point sampled according to
a conditional distribution given its previously-generated points, allowing
inter-point correlations to be well-exploited and 3D shape generative processes
to be better interpreted. Since point cloud object shapes are typically encoded
by long-range dependencies, we augment our model with dedicated self-attention
modules to capture such relations. Extensive evaluations show that PointGrow
achieves satisfying performance on both unconditional and conditional point
cloud generation tasks, with respect to realism and diversity. Several
important applications, such as unsupervised feature learning and shape
arithmetic operations, are also demonstrated
Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection
Arbitrary-oriented object detection is a relatively emerging but challenging
task. Although remarkable progress has been made, there still remain many
unsolved issues due to the large diversity of patterns in orientation, scale,
aspect ratio, and visual appearance of objects in aerial images. Most of the
existing methods adopt a coarse-grained fixed label assignment strategy and
suffer from the inconsistency between the classification score and localization
accuracy. First, to align the metric inconsistency between sample selection and
regression loss calculation caused by fixed IoU strategy, we introduce affine
transformation to evaluate the quality of samples and propose a distance-based
label assignment strategy. The proposed metric-aligned selection (MAS) strategy
can dynamically select samples according to the shape and rotation
characteristic of objects. Second, to further address the inconsistency between
classification and localization, we propose a critical feature sampling (CFS)
module, which performs localization refinement on the sampling location for
classification task to extract critical features accurately. Third, we present
a scale-controlled smooth loss (SC-Loss) to adaptively select high
quality samples by changing the form of regression loss function based on the
statistics of proposals during training. Extensive experiments are conducted on
four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016,
and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed
detector
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy
user expressions, and the shortage of annotated data. To better solve the above
problems, we propose CGoDial, new challenging and comprehensive Chinese
benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763
dialog sessions and 574,949 dialog turns totally, covering three datasets with
different knowledge sources: 1) a slot-based dialog (SBD) dataset with
table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed
knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed
knowledge. To bridge the gap between academic benchmarks and spoken dialog
scenarios, we either collect data from real conversations or add spoken
features to existing datasets via crowd-sourcing. The proposed experimental
settings include the combinations of training with either the entire training
set or a few-shot training set, and testing with either the standard test set
or a hard test subset, which can assess model capabilities in terms of general
prediction, fast adaptability and reliable robustness.Comment: EMNLP 202
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