214 research outputs found
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such methods can produce misleading predictions when used to combine
specialized classifiers. This work explores a novel approach. Instead of
combining predictions from individual classifiers directly, it first decomposes
the predictions into sets of pairwise preferences, treating them as transition
channels between classes, and thereon constructs a continuous-time Markov
chain, and use the equilibrium distribution of this chain as the final
prediction. This way allows us to form a coherent picture over all specialized
predictions. On large public datasets, the proposed method obtains considerable
improvement compared to mainstream ensemble methods, especially when the
classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe
Sparse4D v3: Advancing End-to-End 3D Detection and Tracking
In autonomous driving perception systems, 3D detection and tracking are the
two fundamental tasks. This paper delves deeper into this field, building upon
the Sparse4D framework. We introduce two auxiliary training tasks (Temporal
Instance Denoising and Quality Estimation) and propose decoupled attention to
make structural improvements, leading to significant enhancements in detection
performance. Additionally, we extend the detector into a tracker using a
straightforward approach that assigns instance ID during inference, further
highlighting the advantages of query-based algorithms. Extensive experiments
conducted on the nuScenes benchmark validate the effectiveness of the proposed
improvements. With ResNet50 as the backbone, we witnessed enhancements of
3.0\%, 2.2\%, and 7.6\% in mAP, NDS, and AMOTA, achieving 46.9\%, 56.1\%, and
49.0\%, respectively. Our best model achieved 71.9\% NDS and 67.7\% AMOTA on
the nuScenes test set. Code will be released at
\url{https://github.com/linxuewu/Sparse4D}
Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion
Bird-eye-view (BEV) based methods have made great progress recently in
multi-view 3D detection task. Comparing with BEV based methods, sparse based
methods lag behind in performance, but still have lots of non-negligible
merits. To push sparse 3D detection further, in this work, we introduce a novel
method, named Sparse4D, which does the iterative refinement of anchor boxes via
sparsely sampling and fusing spatial-temporal features. (1) Sparse 4D Sampling:
for each 3D anchor, we assign multiple 4D keypoints, which are then projected
to multi-view/scale/timestamp image features to sample corresponding features;
(2) Hierarchy Feature Fusion: we hierarchically fuse sampled features of
different view/scale, different timestamp and different keypoints to generate
high-quality instance feature. In this way, Sparse4D can efficiently and
effectively achieve 3D detection without relying on dense view transformation
nor global attention, and is more friendly to edge devices deployment.
Furthermore, we introduce an instance-level depth reweight module to alleviate
the ill-posed issue in 3D-to-2D projection. In experiment, our method
outperforms all sparse based methods and most BEV based methods on detection
task in the nuScenes dataset
Memory Performance Characterization of SPEC CPU2006 Benchmarks Using TSIM
AbstractThis paper uses TSIM, a cycle accurate architecture simulator, to characterize the memory performance of SPEC CPU2006 Benchmarks under CMP platform. The experiment covers 54 workloads with different input sets, and collects statistical information of instruction mixture and cache behaviors. By detecting the cyclical changes of MPKI, this paper clearly shows the memory performance phases of some SPEC CPU2006 programs. These performance data and analysis results can not only help program developers and architects understand the memory performance caused by system architecture better, but also guide them in software and system optimization
A Study on the Damage and Economic Threshold of the Soybean Aphid at the Seedling Stage
Both plot inoculation experiments and field pest scouting at the seedling stage indicated that soybean yield losses were closely related to the number of soybean aphids and the proportion of plants colonized by soybean aphids. The main factors affecting the soybean yield were decrease in plant height and number of pods and seeds, owing to injury by soybean aphids at the seedling stage. Under existing production conditions, the economic injury level was 3.36%. The control threshold was 500 soybean aphids per 100 plants, with 35% of plants colonized by soybean aphids.Originating text in Chinese.Citation: Wang, Xibei, Fang, Yihao, Lin, Zhizhong, Zhang, Lirong, Wang, Huadi. (1994). A Study on the Damage and Economic Threshold of the Soybean Aphid at the Seedling Stage. Plant Protection (Institute of Plant Protection, CAAS, China), 20, 12-13
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
Deep learning (DL)-based rib fracture detection has shown promise of playing
an important role in preventing mortality and improving patient outcome.
Normally, developing DL-based object detection models requires huge amount of
bounding box annotation. However, annotating medical data is time-consuming and
expertise-demanding, making obtaining a large amount of fine-grained
annotations extremely infeasible. This poses pressing need of developing
label-efficient detection models to alleviate radiologists' labeling burden. To
tackle this challenge, the literature of object detection has witnessed an
increase of weakly-supervised and semi-supervised approaches, yet still lacks a
unified framework that leverages various forms of fully-labeled,
weakly-labeled, and unlabeled data. In this paper, we present a novel
omni-supervised object detection network, ORF-Netv2, to leverage as much
available supervision as possible. Specifically, a multi-branch omni-supervised
detection head is introduced with each branch trained with a specific type of
supervision. A co-training-based dynamic label assignment strategy is then
proposed to enable flexibly and robustly learning from the weakly-labeled and
unlabeled data. Extensively evaluation was conducted for the proposed framework
with three rib fracture datasets on both chest CT and X-ray. By leveraging all
forms of supervision, ORF-Netv2 achieves mAPs of 34.7, 44.7, and 19.4 on the
three datasets, respectively, surpassing the baseline detector which uses only
box annotations by mAP gains of 3.8, 4.8, and 5.0, respectively. Furthermore,
ORF-Netv2 consistently outperforms other competitive label-efficient methods
over various scenarios, showing a promising framework for label-efficient
fracture detection.Comment: 11 pages, 4 figures, and 7 table
- …