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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Parelli, Maria Delitzas, Alexandros Hars, Nikolas Vlassis, Georgios Anagnostidis, Sotirios Bachmann, Gregor Hofmann, Thomas Eth Zurich Switzerland

ISBN: (纸本)9798350302493

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model's 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features. © 2023 ieee.

关键词： computer vision

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Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding

Mushroom Segmentation and 3D Pose Estimation from Point Clou...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Retsinas, George Efthymiou, Niki Maragos, Petros National Technical University of Athens School of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350302493

Modern agricultural applications rely more and more on deep learning solutions. However, training well-performing deep networks requires a large amount of annotated data that may not be available and in the case of 3D annotation may not even be feasible for human annotators. In this work, we develop a deep learning approach to segment mushrooms and estimate their pose on 3D data, in the form of point clouds acquired by depth sensors. To circumvent the annotation problem, we create a synthetic dataset of mushroom scenes, where we are fully aware of 3D information, such as the pose of each mushroom. The proposed network has a fully convolutional backbone, that parses sparse 3D data, and predicts pose information that implicitly defines both instance segmentation and pose estimation task. We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors. © 2023 ieee.

关键词： Convolution

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Dark Corner on Skin Lesion Image Dataset: Does it matter?

Dark Corner on Skin Lesion Image Dataset: Does it matter?

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Pewton, Samuel William Yap, Moi Hoon Manchester Metropolitan Univ Dept Comp & Math John Dalton Bldg Manchester M1 5GD Lancs England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Skin lesion image datasets gained popularity in recent years with the successes of ISIC datasets and challenges. While the users of these datasets are growing, the Dark Corner Artifact (DCA) phenomenon is under explored. This paper provides a better understanding of how and why DCA occurs, the types of DCAs and investigates the DCA within a curated ISIC image dataset. We introduce new labels of image artifacts on a curated balanced dataset of 9,810 images and identified 2,631 images with different intensities of DCA. Then, we improve the quality of this dataset by introducing automated DCA detection and removal methods. We evaluate the performance of our methods with image quality metrics on an unseen dataset (Dermofit), and achieved better SSIM score in every DCA intensity level. Further, we study the effects of DCA removal on a binary classification task (melanoma vs non-melanoma). Although deep learning performances in this task show marginal differences, we demonstrate that with DCA removal, it can help to shift the network activations to the skin lesions. All the artifact labels and codes are available at: https://***/mmu-dermatologyresearch/dark_corner_artifact_removal.

关键词： Deep learning Measurement Image quality computer vision Image edge detection Melanoma Skin

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Vicinal Counting Networks

Vicinal Counting Networks

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ranjan, Viresh Minh Hoai SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle the task of Few-Shot Counting. Given an image containing multiple objects of a novel visual category and few exemplar bounding boxes depicting the visual category of interest, we want to count all of the instances of the desired visual category in the image. A key challenge in building an accurate few-shot visual counter is the scarcity of annotated training data due to the laborious effort needed for collecting and annotating the data. To address this challenge, we propose Vicinal Counting Networks, which learn to augment the existing training data along with learning to count. A Vicinal Counting Network consists of a generator and a counting network. The generator takes as input an image along with a random noise vector and generates an augmented version of the input image. The counting network learns to count the objects in the original and augmented images. The training signal for the generator comes from the counting loss of the counting network, and the generator aims to synthesize images which result in a small counting loss. Unlike GANs which are trained in an adversarial setting, Vicinal Counting Networks are trained in a cooperative setting where the generator aims to help the counting network in achieving accurate predictions on the synthesized images. We also show that our proposed data augmentation framework can be extended to other counting tasks like crowd counting.

关键词： Training Visualization computer vision conferences Buildings Training data Generators

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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR S...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kalinicheva, Ekaterina Landrieu, Loic Mallet, Clement Chehata, Nesrine Univ Gustave Eiffel LASTIG IGN ENSG F-77454 Marne La Vallee France Univ Bordeaux BIOGECO UMR 1202 INRAE Bordeaux France Univ Bordeaux Montaigne Bordeaux INP Bordeaux France

ISBN: (纸本)9781665487399

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m(2) with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D pointwise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://***/ekalinicheva/multi_layer_vegetation.

关键词： computer vision Solid modeling Three-dimensional displays Laser radar Annotations Atmospheric modeling Time series analysis

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Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Watch and Act: Dual Interacting Agents for Automatic Generat...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sarkar, Saikat Mukherjee, Dipti Prasad Chakrabarti, Amlan Univ Calcutta Kolkata India Indian Stat Inst Kolkata India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.

关键词： Location awareness computer vision conferences Reinforcement learning Games computer architecture Real-time systems

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A robust non-blind deblurring method using deep denoiser prior

A robust non-blind deblurring method using deep denoiser pri...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fang, Yingying Zhang, Hao Wong, Hok Shing Zeng, Tieyong Imperial Coll London London England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The existing non-blind deblurring methods are mostly susceptible to noise in the given blurring kernel, which is usually estimated from the observed image. This will produce undesirable ringing artifacts around the recovered edges when the given kernel is not accurate enough. Besides, the noise and outliers in the observed images may also severely degrade the performance of the deblurring methods. Considering these factors, we designed a robust non-blind deblurring method taking all these noises into account. In this paper, we propose a kernel error term to rectify the given kernel in the midst of the deconvolution process. A residual error term is also introduced to deal with the outliers caused by noise or saturation. A deep learning denoiser prior is adopted to reserve the fine textures in the recovered image. The experiments show clearly that the proposed method achieves remarkable progress in both the visual quality and the numerical results of the recovered images compared to the state-of-the-art deblurring methods.

关键词： Deep learning Measurement Visualization Deconvolution Image edge detection conferences pattern recognition

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Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

Generating Adversarial Samples in Mini-Batches May Be Detrim...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Redgrave, Timothy Crum, Colton University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

ISBN: (纸本)9798350302493

Neural networks have been proven to be both highly effective within computer vision, and highly vulnerable to adversarial attacks. Consequently, as the use of neural networks increases due to their unrivaled performance, so too does the threat posed by adversarial attacks. In this work, we build towards addressing the challenge of adversarial robustness by exploring the relationship between the mini-batch size used during adversarial sample generation and the strength of the adversarial samples produced. We demonstrate that an increase in mini-batch size results in a decrease in the efficacy of the samples produced, and we draw connections between these observations and the phenomenon of vanishing gradients. Next, we formulate loss functions such that adversarial sample strength is not degraded by mini-batch size. Our findings highlight a potential risk for underestimating the true (practical) strength of adversarial attacks, and a risk of overestimating a model's robustness. We share our codes to let others replicate our experiments and to facilitate further exploration of the connections between batch size and adversarial sample strength. © 2023 ieee.

关键词： computer vision

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Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions

Three Stream Graph Attention Network using Dynamic Patch Sel...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jain, Ankith Kumar, Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.

关键词： Emotion recognition computer vision Face recognition conferences Feature extraction Skin Facial muscles

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HR-STAN: High-Resolution Spatio-Temporal Attention Network for 3D Human Motion Prediction

HR-STAN: High-Resolution Spatio-Temporal Attention Network f...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Medjaouri, Omar Desai, Kevin Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

3D human motion prediction requires making sense of the complex spatio-temporal dynamics which underpin human motion to make highly accurate predictions. Part of this complexity is due to the trade-off between long-term (>400ms) and short-term predictions (<400ms) which require different levels of granularity to observe patterns. Several works have explored methods of improving long-term prediction performance by utilizing longer motion histories but this typically comes at the cost of very short-term (<200ms) performance. Inspired by high-resolution network architectures, we propose a novel high-resolution spatio-temporal attention network (HR-STAN) which leverages parallel feature branches and dilated convolutions to observe human motion at different scales. Furthermore, we augment this architecture with split spatial and temporal attention mechanisms to efficiently capture spatio-temporal dependencies within a given motion. We evaluate the ability of our HR-STAN architecture at incorporating long-term motion histories while producing short-term predictions and show that it improves over several state-of-the-art methods on both the AMASS and Human3.6M benchmarks.

关键词： computer vision Three-dimensional displays Costs conferences Dynamics computer architecture Predictive models

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