192 research outputs found
Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping
Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications
IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection
3D object detection is one of the most important tasks in autonomous driving
and robotics. Our research focuses on tackling low efficiency issue of
point-based methods on large-scale point clouds. Existing point-based methods
adopt farthest point sampling (FPS) strategy for downsampling, which is
computationally expensive in terms of inference time and memory consumption
when the number of point cloud increases. In order to improve efficiency, we
propose a novel Instance-Centroid Faster Point Sampling Module (IC-FPS) , which
effectively replaces the first Set Abstraction (SA) layer that is extremely
tedious. IC-FPS module is comprised of two methods, local feature diffusion
based background point filter (LFDBF) and Centroid-Instance Sampling Strategy
(CISS). LFDBF is constructed to exclude most invalid background points, while
CISS substitutes FPS strategy by fast sampling centroids and instance points.
IC-FPS module can be inserted to almost every point-based models. Extensive
experiments on multiple public benchmarks have demonstrated the superiority of
IC-FPS. On Waymo dataset, the proposed module significantly improves
performance of baseline model and accelerates inference speed by 3.8 times. For
the first time, real-time detection of point-based models in large-scale point
cloud scenario is realized
A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm
In the procedure of surface defects detection for large-aperture aspherical
optical elements, it is of vital significance to adjust the optical axis of the
element to be coaxial with the mechanical spin axis accurately. Therefore, a
machine vision method for eccentric error correction is proposed in this paper.
Focusing on the severe defocus blur of reference crosshair image caused by the
imaging characteristic of the aspherical optical element, which may lead to the
failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to
strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark
Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely
Connected Network (MDC-Net). The enhancement effect of GFA is excellent but
time-consuming, and the enhancement effect of MDC-Net is slightly inferior but
strongly real-time. As AEA will be executed dozens of times during each
correction procedure, its real-time performance is very important. Therefore,
by setting the empirical threshold of definition evaluation function SMD2, GFA
and MDC-Net are respectively applied to highly and slightly blurred crosshair
images so as to ensure the enhancement effect while saving as much time as
possible. AEA has certain robustness in time-consuming performance, which takes
an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on
ten 200pixels 200pixels Region of Interest (ROI) images with different degrees
of blur. And the eccentricity error can be reduced to within 10um by our
method
EA-BEV: Edge-aware Bird' s-Eye-View Projector for 3D Object Detection
In recent years, great progress has been made in the Lift-Splat-Shot-based
(LSS-based) 3D object detection method, which converts features of 2D camera
view and 3D lidar view to Bird's-Eye-View (BEV) for feature fusion. However,
inaccurate depth estimation (e.g. the 'depth jump' problem) is an obstacle to
develop LSS-based methods. To alleviate the 'depth jump' problem, we proposed
Edge-Aware Bird's-Eye-View (EA-BEV) projector. By coupling proposed edge-aware
depth fusion module and depth estimate module, the proposed EA-BEV projector
solves the problem and enforces refined supervision on depth. Besides, we
propose sparse depth supervision and gradient edge depth supervision, for
constraining learning on global depth and local marginal depth information. Our
EA-BEV projector is a plug-and-play module for any LSS-based 3D object
detection models, and effectively improves the baseline performance. We
demonstrate the effectiveness on the nuScenes benchmark. On the nuScenes 3D
object detection validation dataset, our proposed EA-BEV projector can boost
several state-of-the-art LLS-based baselines on nuScenes 3D object detection
benchmark and nuScenes BEV map segmentation benchmark with negligible increment
of inference time
Application of a physiologically based pharmacokinetic model for the evaluation of single-point plasma phenotyping method of CYP2D6
Data-Efficient Image Quality Assessment with Attention-Panel Decoder
Blind Image Quality Assessment (BIQA) is a fundamental task in computer
vision, which however remains unresolved due to the complex distortion
conditions and diversified image contents. To confront this challenge, we in
this paper propose a novel BIQA pipeline based on the Transformer architecture,
which achieves an efficient quality-aware feature representation with much
fewer data. More specifically, we consider the traditional fine-tuning in BIQA
as an interpretation of the pre-trained model. In this way, we further
introduce a Transformer decoder to refine the perceptual information of the CLS
token from different perspectives. This enables our model to establish the
quality-aware feature manifold efficiently while attaining a strong
generalization capability. Meanwhile, inspired by the subjective evaluation
behaviors of human, we introduce a novel attention panel mechanism, which
improves the model performance and reduces the prediction uncertainty
simultaneously. The proposed BIQA method maintains a lightweight design with
only one layer of the decoder, yet extensive experiments on eight standard BIQA
datasets (both synthetic and authentic) demonstrate its superior performance to
the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875
(vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE).Comment: Accepted by AAAI 202
Breaking the Curse of Quality Saturation with User-Centric Ranking
A key puzzle in search, ads, and recommendation is that the ranking model can
only utilize a small portion of the vastly available user interaction data. As
a result, increasing data volume, model size, or computation FLOPs will quickly
suffer from diminishing returns. We examined this problem and found that one of
the root causes may lie in the so-called ``item-centric'' formulation, which
has an unbounded vocabulary and thus uncontrolled model complexity. To mitigate
quality saturation, we introduce an alternative formulation named
``user-centric ranking'', which is based on a transposed view of the dyadic
user-item interaction data. We show that this formulation has a promising
scaling property, enabling us to train better-converged models on substantially
larger data sets.Comment: accepted to publish at KDD 202
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Most named entity recognition (NER) systems focus on improving model
performance, ignoring the need to quantify model uncertainty, which is critical
to the reliability of NER systems in open environments. Evidential deep
learning (EDL) has recently been proposed as a promising solution to explicitly
model predictive uncertainty for classification tasks. However, directly
applying EDL to NER applications faces two challenges, i.e., the problems of
sparse entities and OOV/OOD entities in NER tasks. To address these challenges,
we propose a trustworthy NER framework named E-NER by introducing two
uncertainty-guided loss terms to the conventional EDL, along with a series of
uncertainty-guided training strategies. Experiments show that E-NER can be
applied to multiple NER paradigms to obtain accurate uncertainty estimation.
Furthermore, compared to state-of-the-art baselines, the proposed method
achieves a better OOV/OOD detection performance and better generalization
ability on OOV entities.Comment: accepted by ACL Findings (2023
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