140 research outputs found
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple stages and components in the pipelines. In this work, we propose a
simple yet powerful pipeline that yields fast and accurate text detection in
natural scenes. The pipeline directly predicts words or text lines of arbitrary
orientations and quadrilateral shapes in full images, eliminating unnecessary
intermediate steps (e.g., candidate aggregation and word partitioning), with a
single neural network. The simplicity of our pipeline allows concentrating
efforts on designing loss functions and neural network architecture.
Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500
demonstrate that the proposed algorithm significantly outperforms
state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR
2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps
at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Underwater object detection for robot picking has attracted a lot of
interest. However, it is still an unsolved problem due to several challenges.
We take steps towards making it more realistic by addressing the following
challenges. Firstly, the currently available datasets basically lack the test
set annotations, causing researchers must compare their method with other SOTAs
on a self-divided test set (from the training set). Training other methods lead
to an increase in workload and different researchers divide different datasets,
resulting there is no unified benchmark to compare the performance of different
algorithms. Secondly, these datasets also have other shortcomings, e.g., too
many similar images or incomplete labels. Towards these challenges we introduce
a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark,
based on the collection and re-annotation of all relevant datasets. DUO
contains a collection of diverse underwater images with more rational
annotations. The corresponding benchmark provides indicators of both efficiency
and accuracy of SOTAs (under the MMDtection framework) for academic research
and industrial applications, where JETSON AGX XAVIER is used to assess detector
speed to simulate the robot-embedded environment
PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures
Neural Radiance Fields (NeRF) have been widely adopted as practical and
versatile representations for 3D scenes, facilitating various downstream tasks.
However, different architectures, including plain Multi-Layer Perceptron (MLP),
Tensors, low-rank Tensors, Hashtables, and their compositions, have their
trade-offs. For instance, Hashtables-based representations allow for faster
rendering but lack clear geometric meaning, making spatial-relation-aware
editing challenging. To address this limitation and maximize the potential of
each architecture, we propose Progressive Volume Distillation with Active
Learning (PVD-AL), a systematic distillation method that enables any-to-any
conversions between different architectures. PVD-AL decomposes each structure
into two parts and progressively performs distillation from shallower to deeper
volume representation, leveraging effective information retrieved from the
rendering process. Additionally, a Three-Levels of active learning technique
provides continuous feedback during the distillation process, resulting in
high-performance results. Empirical evidence is presented to validate our
method on multiple benchmark datasets. For example, PVD-AL can distill an
MLP-based model from a Hashtables-based model at a 10~20X faster speed and
0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover,
PVD-AL permits the fusion of diverse features among distinct structures,
enabling models with multiple editing properties and providing a more efficient
model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.Comment: Project website: http://sk-fun.fun/PVD-AL. arXiv admin note:
substantial text overlap with arXiv:2211.1597
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
Open-vocabulary learning has emerged as a cutting-edge research area,
particularly in light of the widespread adoption of vision-based foundational
models. Its primary objective is to comprehend novel concepts that are not
encompassed within a predefined vocabulary. One key facet of this endeavor is
Visual Grounding, which entails locating a specific region within an image
based on a corresponding language description. While current foundational
models excel at various visual language tasks, there's a noticeable absence of
models specifically tailored for open-vocabulary visual grounding. This
research endeavor introduces novel and challenging OV tasks, namely
Open-Vocabulary Visual Grounding and Open-Vocabulary Phrase Localization. The
overarching aim is to establish connections between language descriptions and
the localization of novel objects. To facilitate this, we have curated a
comprehensive annotated benchmark, encompassing 7,272 OV-VG images and 1,000
OV-PL images. In our pursuit of addressing these challenges, we delved into
various baseline methodologies rooted in existing open-vocabulary object
detection, VG, and phrase localization frameworks. Surprisingly, we discovered
that state-of-the-art methods often falter in diverse scenarios. Consequently,
we developed a novel framework that integrates two critical components:
Text-Image Query Selection and Language-Guided Feature Attention. These modules
are designed to bolster the recognition of novel categories and enhance the
alignment between visual and linguistic information. Extensive experiments
demonstrate the efficacy of our proposed framework, which consistently attains
SOTA performance across the OV-VG task. Additionally, ablation studies provide
further evidence of the effectiveness of our innovative models. Codes and
datasets will be made publicly available at https://github.com/cv516Buaa/OV-VG
Text-driven Editing of 3D Scenes without Retraining
Numerous diffusion models have recently been applied to image synthesis and
editing. However, editing 3D scenes is still in its early stages. It poses
various challenges, such as the requirement to design specific methods for
different editing types, retraining new models for various 3D scenes, and the
absence of convenient human interaction during editing. To tackle these issues,
we introduce a text-driven editing method, termed DN2N, which allows for the
direct acquisition of a NeRF model with universal editing capabilities,
eliminating the requirement for retraining. Our method employs off-the-shelf
text-based editing models of 2D images to modify the 3D scene images, followed
by a filtering process to discard poorly edited images that disrupt 3D
consistency. We then consider the remaining inconsistency as a problem of
removing noise perturbation, which can be solved by generating training data
with similar perturbation characteristics for training. We further propose
cross-view regularization terms to help the generalized NeRF model mitigate
these perturbations. Our text-driven method allows users to edit a 3D scene
with their desired description, which is more friendly, intuitive, and
practical than prior works. Empirical results show that our method achieves
multiple editing types, including but not limited to appearance editing,
weather transition, material changing, and style transfer. Most importantly,
our method generalizes well with editing abilities shared among a set of model
parameters without requiring a customized editing model for some specific
scenes, thus inferring novel views with editing effects directly from user
input. The project website is available at http://sk-fun.fun/DN2NComment: Project Website: http://sk-fun.fun/DN2
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
Visual Grounding (VG) aims at localizing target objects from an image based
on given expressions and has made significant progress with the development of
detection and vision transformer. However, existing VG methods tend to generate
false-alarm objects when presented with inaccurate or irrelevant descriptions,
which commonly occur in practical applications. Moreover, existing methods fail
to capture fine-grained features, accurate localization, and sufficient context
comprehension from the whole image and textual descriptions. To address both
issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with
Masked Reference based Centerpoint Supervision (MRCS). The framework introduces
iterative multi-level vision-language fusion (IMVF) for better alignment. We
use MRCS to ahieve more accurate localization with point-wised feature
supervision. Then, to improve the robustness of VG, we also present a
multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of
false-alarm objects when presented with inaccurate expressions. The proposed
framework is evaluated on five regular VG datasets and two newly constructed
robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new
state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to
existing SOTA approaches on the two newly proposed robust VG datasets.
Moreover, the proposed framework is also verified effective on five regular VG
datasets. Codes and models will be publicly at
https://github.com/cv516Buaa/IR-VG
One-Step Preparation of High Performance TiO 2 /CNT/CQD Nanocomposites Bactericidal Coating with Ultrasonic Radiation
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).As an environmental semiconductor material, TiO2 has important applications in the fields of environmental protection and water treatment. The preparation of P25 particles into nano-functional material films with a high specific surface area has always been a bottleneck limiting its large-scale application. In this paper, a one-step method of preparing TiO2 nanocomposites by doping carbon nanotube (CNT) and carbon quantum dots (CQD) with tetrabutyltitanate and P25 TiO2 under ultrasonic radiation is proposed to synthesize a novel antifouling material, which both eliminates the bacterium of Escherichia coli and shows good photoelectric properties, indicating a great value for the industrial promotion of TiO2/CNT. This mesoporous composite exhibits a high specific surface area of 78.07 M2/g (BET) and a tested pore width range within 10–120 nm. The surface morphology of this composite is characterized by TEM and the microstructure is characterized through XRD. This preparation method can fabricate P25 particles into a nano-functional material film with a high specific surface area at a very low cost.Peer reviewe
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