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AiDe: Improving 3D Open-Vocabulary Semantic Segmentation by Aligned vision-Language Learning

AiDe: Improving 3D Open-Vocabulary Semantic Segmentation by ...

引用

ieee workshop on applications of computer vision (WACV)

作者： Yimu Wang Krzysztof Czarnecki University of Waterloo

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

3D open-vocabulary semantic segmentation aims at recognizing countless categories beyond the limited set of annotations used in traditional settings. Due to the lack of large-scale 3D-vision-language segmentation data, instead of training models from scratch, the current solutions distill knowledge from pre-trained 2D vision-language models (VLMs) into 3D models. However, this distillation is supervised by misaligned 3D-scene-image-to-text data pairs, consequently leading to suboptimal performance. Moreover, as 2D VLMs are trained on 2D datasets, text encoders of VLMs, which serve as the bridge between 3D models and an unbounded set of categories, lack 3D semantics. In this paper, to address these issues and improve generalization performance, we propose an Aligned 3D Open-Vocabulary SEmantic Segmentation framework, called AiDe, with two novel modules. To collect high-quality and well-aligned 3D-scene-image-to-text pairs, our CLIP-rewarded alignment module (i) generates diverse captions of multi-view images of 3D scenes to capture details by varying the temperatures and then (ii) samples captions based on their similarity to corresponding images for rich and accurate associations. Next, to adapt 2D VLMs to 3D contexts, our adaptive segmentation module introduces (iii) trainable tokens within the input space and each layer of the text encoder, while freezing the text encoder to avoid catastrophic forgetting. Extensive experiments show that AiDe outperforms previous methods by a large margin on three representative benchmarks, demonstrating its effectiveness.

关键词： Training Bridges Solid modeling computer vision Three-dimensional displays Semantic segmentation Semantics Data collection Benchmark testing Data models

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SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera

SoundLoc3D: Invisible 3D Sound Source Localization and Class...

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ieee workshop on applications of computer vision (WACV)

作者： Yuhang He Sangyun Shin Anoop Cherian Niki Trigoni Andrew Markham Department of Computer Science University of Oxford UK Mitsubishi Electric Research Labs Cambridge MA US

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Accurately localizing 3D sound sources and estimating their semantic labels - where the sources may not be visible, but are assumed to lie on the physical surface of objects in the scene - have many real applications, including detecting gas leak and machinery malfunction. The audio-visual weak-correlation in such setting poses new challenges in deriving innovative methods to answer if or how we can use cross-modal information to solve the task. Towards this end, we propose to use an acoustic-camera rig consisting of a pinhole RGB-D camera and a coplanar four-channel microphone array (Mic-Array). By using this rig to record audio-visual signals from multiviews, we can use the cross-modal cues to estimate the sound sources 3D locations. Specifically, our framework SoundLoc3D treats the task as a set prediction problem, each element in the set corresponds to a potential sound source. Given the audio-visual weak-correlation, the set representation is initially learned from a single view microphone array signal, and then refined by actively incorporating physical surface cues revealed from multiview RGB-D images. We demonstrate the efficiency and superiority of SoundLoc3D on large-scale simulated dataset, and further show its robustness to RGB-D measurement inaccuracy and ambient noise interference.

关键词： Location awareness Three-dimensional displays Semantics Noise Cameras Microphone arrays Robustness Noise measurement Machinery Surface treatment

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Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information

Make VLM Recognize Visual Hallucination on Cartoon Character...

引用

ieee workshop on applications of computer vision (WACV)

作者： Bumsoo Kim Wonseop Shin Kyuchul Lee Yonghoon Jung Sanghyun Seo Chung-Ang University Coupang

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Leveraging large-scale Text-to-Image (TTI) models have become a common technique for generating exemplar or training dataset in the fields of image synthesis, video editing, 3D reconstruction. However, semantic structural visual hallucinations involving perceptually severe defects remain a concern, especially in the domain of non-photorealistic rendering (NPR) such as cartoons and pixelization-style character. To detect these hallucinations in NPR, We propose a novel semantic structural hallucination detection system using vision-Language Model (VLM). Our approach is to leverage the emerging capability of large language model, in-context learning which denotes that VLM has seen some examples by user for specific downstream task, here hallucination detection. Based on in-context learning, we introduce pose-aware in-context visual learning (PA-ICVL) which improve the overall performance of VLM by further inputting visual data beyond prompts, RGB images and pose information. By incorporating pose guidance, we enable VLMs to make more accurate decisions. Experimental results demonstrate significant improvements in identifying visual hallucinations compared to baseline methods relying solely on RGB images. Within selected two VLMs, GPT-4v, Gemini pro vision, our proposed PA-ICVL improves the hallucination detection with 50% to 78%, 57% to 80%, respectively. This research advances a capability of TTI models toward real-world applications by mitigating visual hallucinations via in-context visual learning, expanding their potential in non-photorealistic domains. In addition, it showcase how users can boost the downstream-specialized capability of open VLM by harnessing additional conditions. We collect synthetic cartoon-hallucination dataset with TTI models, this dataset and final tuned VLM will be publicly available. The dataset and demo VLMs are provided in the corresponding page: https://***/Cartoon-Hallucinations-Detection/.

关键词： Training Visualization Solid modeling Accuracy Three-dimensional displays Large language models Semantics Text to image Rendering (computer graphics) Numerical models

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SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution

SwinFuSR: an image fusion-inspired model for RGB-guided ther...

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Cyprien Arnold Philippe Jouvet Lama Seoud Polytechnique Montréal Montréal Canada CHU Sainte Justine Montréal Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://***/visionICLab/SwinFuSR.

关键词： Training Measurement PSNR Conferences Superresolution computer architecture Transformers

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Forensic Iris Image-Based Post-Mortem Interval Estimation

Forensic Iris Image-Based Post-Mortem Interval Estimation

引用

ieee workshop on applications of computer vision (WACV)

作者： Rasel Ahmed Bhuiyan Adam Czajka University of Notre Dame Notre Dame Indiana USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Post-mortem iris recognition is an emerging application of iris-based human identification in a forensic setup. One factor that may be useful in conditioning iris recognition methods is the tissue decomposition level, which is correlated with the post-mortem interval (PMI), i.e. the number of hours that have elapsed since death. PMI, however, is not always available, and its precise estimation remains one of the core challenges in forensic examination. This paper presents the first known to us method of the PMI estimation directly from iris images captured after death. To assess the feasibility of the iris-based PMI estimation, we designed models predicting the PMI from (a) near-infrared (NIR), (b) visible (RGB), and (c) multispectral (RGB+NIR) forensic iris images. Models were evaluated following a 10-fold cross-validation, in (S1) sample-disjoint, (S2) subject-disjoint, and (S3) cross-dataset scenarios. We explore two data balancing techniques for S3: resampling-based balancing (S3-real), and synthetic data-supplemented balancing (S3 -synthetic), We found that using the multispectral data offers a spectacularly low mean absolute error (MAE) of≈ 3.5 hours in the scenario (S1), a bit worse MAE ≈ 17.5 hours in the scenario (S2), and MAE ≈ 45.77 hours in the scenario (S3). Additionally, supplementing the training set with synthetically-generated forensic iris images (S3-synthetic) significantly enhances the models' ability to generalize to new NIR, RGB and multispectral data collected in a different lab. This suggests that if the environmental conditions are favorable (e.g., bodies are kept in low temperatures), forensic iris images provide features that are in-dicative of the PMI and can be automatically estimated.

关键词： Training computer vision Forensics Estimation Predictive models Data models Iris recognition Synthetic data

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Multi-sensor Ensemble-guided Attention Network for Aerial Vehicle Perception beyond visible spectrum

Multi-sensor Ensemble-guided Attention Network for Aerial Ve...

引用

ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Alicja Kwasniewska Anastacia MacAllister Rey Nicolas Javier Garza SiMa Technologies San Jose CA General Atomics Poway CA Lockheed Martin Fort Worth TX

Researchers from different market domains have made significant developments in Artificial Intelligence (AI) enabling more advanced automated sensing systems and, thus, eliminating the need for the time-consuming manual analysis of data, which is prone to human errors. However, successful deployment of such systems in real world applications requires careful design and analysis of the proposed models. This work focuses on perception done on Unmanned Aerial Vehicles (UAV) using multi-task learning. There are multiple challenges when considering such platforms. First of all, they often operate in difficult and dynamic conditions affected by various factors, such as background noises, ego-noise of the motors and occluded views. At the same time, they require high performance local compute, co-designed with optimized software solutions that meet small size, weight, and power (SWaP) requirements. Therefore, the AI models designed for such systems should not introduce computational and memory overheads to allow for real time processing at the embedded edge. Taking this into account, this work proposes a novel neural network-based system that utilizes ensemble-guided modulations of audio path fused with the infrared (IR) visual embedding using the attention mechanism. The ensemble mechanism doesn’t require spawning new ensemble members, but instead operates on FiLM (Feature-wise Linear Modulation) activation, making it suitable for resource-constraints embedded edge platforms. The performed experiments show that the proposed network outperforms a single FiLM network by 15% and is more robust to noise.

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception beyond the visible spectrum (PBVS) 2022 workshop at the computer vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision Conferences Training data Object detection Semisupervised learning Transformers

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The Mirror Reflection Principle and Probabilistic Invariants in the Theory of Multiple Photon Scattering 21st

The Mirror Reflection Principle and Probabilistic Invariants...

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21st International Conference on Computational Science and Its applications (ICCSA)

作者： Smokty, Oleg, I Russian Acad Sci Fed Res Ctr St Petersburg Inst Informat & Automat 39 14th Line St Petersburg 198199 Russia

ISBN: (纸本)9783030866532;9783030866525

In the framework of the statistical interpretation of multiple photon scattering as a stationary Markov process, which was given in well-known works by V.V. Sobolev and S. Ueno, the author has formulated the principle of mirror spatial-angular symmetry for total probabilities of photons exiting a homogeneous slab of finite optical thickness to < oo. Based on this principle, linear second kind Fredholm integral equations have been obtained, as well as linear singular integral equations, for new objects of the classical radiative transfer theory, namely probabilistic invariants in the case of arbitrary distribution and power of primary energy sources in the medium. Besides, a unified probabilistic function for photons exiting a homogeneous slab was constructed. For finding unique regular solutions of the obtained linear singular integral equations, by analogy with the classical theory, additional integral relations are given, depending on the presence or absence of roots or pseudo-roots in respective characteristic equations. Given the analytic connection between probabilistic and photometric invariants, it has been shown that their calculations by standard computational methods, such as discretization technique or method of successive approximations, lead to substantial savings of computer resources and provide a more convenient form for representing theoretical data and results of numerical modelling of radiation fields of an atmosphere in the visible spectrum range of 0.6-0.8 mu m.

关键词： Mirror reflection principle Total probability of photon exiting Probabilistic invariants Basic boundary-value problem Phase function Single scattering albedo Optical thickness Mirror symmetrical optical levels Mirror symmetry vision lines Unified probabilistic function

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4DComplete: Non-Rigid Motion Estimation beyond the Observable Surface

4DComplete: Non-Rigid Motion Estimation Beyond the Observabl...

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International Conference on computer vision (ICCV)

作者： Yang Li Hikari Takehara Takafumi Taketomi Bo Zheng Matthias Nießner The University of Tokyo Huawei Tokyo Research Center Technical University Munich

ISBN: (纸本)9781665428132

Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics. However, due to occlusions and physical limitations of range sensors, existing methods only handle the visible surface, thus causing discontinuities and in-completeness in the motion field. To this end, we introduce 4DComplete, a novel data-driven approach that estimates the non-rigid motion for the unobserved geometry. 4DComplete takes as input a partial shape and motion observation, extracts 4D time-space embedding, and jointly infers the missing geometry and motion field using a sparse fully-convolutional network. For network training, we constructed a large-scale synthetic dataset called DeformingThings4D, which consists of 1,972 animation sequences spanning 31 different animals or humanoid categories with dense 4D annotation. Experiments show that 4DComplete 1) reconstructs high-resolution volumetric shape and motion field from a partial observation, 2) learns an entangled 4D feature representation that benefits both shape and motion estimation, 3) yields more accurate and natural deformation than classic non-rigid priors such as As-Rigid-As-Possible (ARAP) deformation, and 4) generalizes well to unseen objects in real-world sequences.

关键词： Geometry Training computer vision Shape Tracking Motion estimation Humanoid robots

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Semantic Segmentation of Spectral Images: A Comparative Study using FCN8s and U-NET on RIT-18 Dataset 11

Semantic Segmentation of Spectral Images: A Comparative Stud...

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11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Darshik, S.A. Dev, Aditya Bharath, M. Nair, Bharath Ashok Gopakumar, G. Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9781728168517

Fully convolutional networks (FCN) have been used in various semantic segmentation applications. The spectral images consist of several bands that carry information that is beyond the visible spectrum of a human eye. Semantic segmentation has great potential in analyzing spectral images. Applying deep learning models on spectral images is limited due to the unavailability of the benchmark dataset. Recent trends in deep learning suggest methods to make complex problems like semantic segmentation more accurate or faster. In our study, we have adopted two FCN architectures (FCN8s and U-Net) and conducted a comparative study on a benchmark multispectral image dataset (RIT-18 Dataset). Since FCN architectures are so diverse in terms of use and remodeling, it can be easily extended for training multi-channeled image data. From our study, FCN8s perform better than U-Net on the segmentation of the RIT-18 dataset. © 2020 ieee.

关键词： Convolution

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