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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

LLM-Seg: Bridging Image Segmentation and Large Language Mode...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Junchi Wang Lei Ke ETH Zurich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://***/wangjunchi/LLMSeg.

关键词： Training Image segmentation Large language models Design methodology Pipelines Cognition pattern recognition

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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Analyzing the Internals of Neural Radiance Fields

Analyzing the Internals of Neural Radiance Fields

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Lukas Radl Andreas Kurz Michael Steiner Markus Steinberger Graz University of Technology Austria

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density leveraging proposal network samplers. In contrast to the coarse-to-fine sampling approach with two NeRFs, this offers significant potential for acceleration using lower network capacity. Given that NeRFs utilize most of their network capacity to estimate radiance, they could store valuable density information in their parameters or their deep features. To investigate this proposition, we take one step back and analyze large, trained ReLU-MLPs used in coarse-to-fine sampling. Building on our novel activation visualization method, we find that trained NeRFs, Mip-NeRFs and proposal network samplers map samples with high density to local minima along a ray in activation feature space. We show how these large MLPs can be accelerated by transforming intermediate activations to a weight estimate, without any modifications to the training protocol or the network architecture. With our approach, we can reduce the computational requirements of trained NeRFs by up to 50% with only a slight hit in rendering quality. Extensive experimental evaluation on a variety of datasets and architectures demonstrates the effectiveness of our approach. Consequently, our methodology provides valuable insight into the inner workings of NeRFs.

关键词： Training Visualization computer vision Protocols conferences Network architecture Neural radiance field

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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Fully Test-time Adaptation for Object Detection

Fully Test-time Adaptation for Object Detection

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiaoqian Ruan Wei Tang University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://***/XiaoqianRuan1/IoU-filter.

关键词： Training computer vision Codes conferences Training data Detectors Object detection

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Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

Dynamic Addition of Noise in a Diffusion Model for Anomaly D...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Justin Tebbe Jawad Tayyub Otto von Guericke University Magdeburg Magdeburg Germany Endress + Hauser Maulburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies such as entire missing components. Addressing this, we present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach [24] in three significant ways. First, we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second, we demonstrate that denoising an only scaled input, without any added noise, outperforms conventional denoising process. Third, we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally, we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on prominent anomaly detection datasets VisA, BTAD and MVTec yielding strong performance. Importantly, our framework effectively localizes anomalies regardless of their scale, marking a pivotal advancement in diffusion-based anomaly detection.

关键词： Location awareness Technological innovation computer vision conferences Noise reduction Noise Diffusion models

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： computer vision Visualization conferences Crops Cameras Trajectory pattern recognition

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Gait Analysis with Trinocular computer vision Using Deep Learning

Gait Analysis with Trinocular Computer Vision Using Deep Lea...

引用

2023 ieee International conference on Image Processing Challenges and workshops, ICIPCW 2023

作者： Stavrakakis, Odysseas Mastrogeorgiou, Athanasios Smyrli, Aikaterini Papadopoulos, Evangelos School of Mechanical Engineering National Technical University of Athens Control Systems Lab Greece

ISBN: (纸本)9798350302585

The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait analysis (GA) is attracting the attention of the field of deep learning due to its seamless integration applicability. Our approach focuses on CNN-based GA application on healthcare and orthopaedics. Using CNNs and visual fiducial systems for recognition and 3D mesh re-construction of the human form and the floor respectively, we can virtually recreate the human-floor interaction, which can be particularly useful in the study of gait dynamics, through the per-frame gait phase classification. However, the current state-of-the-art (SOTA) is that most CNN mesh reconstruction software produces a mesh from a monocular input. The use of photogrammetry could alternatively be implemented, but would require multiple cameras and expensive equipment. Our approach aims to create a refined mesh obtained from trinocular footage, along with an interactive and easy-to-use interface. © 2023 ieee.

关键词： Floors

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BGDNet: Background-guided Indoor Panorama Depth Estimation

BGDNet: Background-guided Indoor Panorama Depth Estimation

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiajing Chen Zhiqiang Wan Manjunath Narayana Yuguang Li Will Hutchcroft Senem Velipasalar Sing Bing Kang Syracuse University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Depth estimation from single perspective image has received significant attention in the past decade, whereas the same task applied to single panoramic image remains comparatively under-explored. Most existing depth estimation models for panoramic images imitate models proposed for perspective images, which take RGB images as input and output depth directly. However, as demonstrated by our experiments, model performance drops significantly when the training and testing datasets greatly differ, since they over-fit the training data. To address this issue, we propose a novel method, referred to as the Background-guided Network (BGDNet), for more robust and accurate depth estimation from indoor panoramic images. Different from existing models, our proposed BGDNet first infers the background depth, namely from walls, floor and ceiling, via background masks, room layout and camera model. The background depth is then used to guide and improve the output foreground depth. We perform within dataset as well as cross-domain experiments on two benchmark datasets. The results show that BGDNet outperforms the state-of-the-art baselines, and is more robust to overfitting issues, with superior generalization across datasets.

关键词： Training computer vision conferences Layout Estimation Training data Cameras

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DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer

DehazeDCT: Towards Effective Non-Homogeneous Dehazing via De...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Dong Han Zhou Ruiyi Wang Xiaohong Liu Guangtao Zhai Jun Chen McMaster University Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images. Many deep learning methods with powerful representation learning capability demonstrate advanced performance on non-homogeneous dehazing, however, these methods usually struggle with processing high-resolution images (e.g., 4000 × 6000) due to their heavy computational demands. To address these challenges, we introduce an innovative non-homogeneous Dehazing method via Deformable Convolutional Transformer-like architecture (DehazeDCT). Specifically, we first design a transformer-like network based on deformable convolution v4, which offers long-range dependency and adaptive spatial aggregation capabilities and demonstrates faster convergence and forward speed. Furthermore, we leverage a lightweight Retinex-inspired transformer to achieve color correction and structure refinement. Extensive experiment results and highly competitive performance of our method in NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge, ranking second among all 16 submissions, demonstrate the superior capability of our proposed method. The code is available: https://***/movingforward100/Dehazing_R.

关键词： Convolutional codes Representation learning Image color analysis Convolution computer architecture Transformer cores Transformers

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