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Learning Tracking Representations from Single Point Annotations

Learning Tracking Representations from Single Point Annotati...

引用

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

作者： Qiangqiang Wu Antoni B. Chan Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing deep trackers are typically trained with large-scale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5 × faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.

关键词： Training Visualization Target tracking Costs Correlation Annotations Noise

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Sparse Multimodal vision Transformer for Weakly Supervised Semantic Segmentation

Sparse Multimodal Vision Transformer for Weakly Supervised S...

引用

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

作者： Joëlle Hanna Michael Mommert Damian Borth AIML Lab School of Computer Science University of St.Gallen

vision Transformers have proven their versatility and utility for complex computer vision tasks, such as land cover segmentation in remote sensing applications. While performing on par or even outperforming other methods like Convolutional Neural Networks (CNNs), Transformers tend to require even larger datasets with fine-grained annotations (e.g., pixel-level labels for land cover segmentation). To overcome this limitation, we propose a weakly-supervised vision Transformer that leverages image-level labels to learn a semantic segmentation task to reduce the human annotation load. We achieve this by slightly modifying the architecture of the vision Transformer through the use of gating units in each attention head to enforce sparsity during training and thereby retaining only the most meaningful heads. This allows us to directly infer pixel-level labels from image-level labels by post-processing the un-pruned attention heads of the model and refining our predictions by iteratively training a segmentation model with high fidelity. Training and evaluation on the DFC2020 dataset show that our method 1 not only generates high-quality segmentation masks using image-level labels, but also performs on par with fully-supervised training relying on pixel-level labels. Finally, our results show that our method is able to perform weakly-supervised semantic segmentation even on small-scale datasets.

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Monitoring Social Insect Activity with Minimal Human Supervision

Monitoring Social Insect Activity with Minimal Human Supervi...

引用

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

作者： Tarun Sharma Julian M. Wagner Sara Beery William B. Dickson Michael H. Dickinson Joseph Parker California Institute of Technology USA MIT USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tracking the behavior of animals and their group dynamics in nature offers a crucial look into the delicate ecological networks that compose wildlife diversity. The velvety tree ant (Liometopum occidentale) is an ecologically dominant ant species found in South Western North America; their extensive foraging activity shapes forest communities, and their nests are a biodiversity hot-spot for a multitude of symbiotic invertebrates (myrmecophiles). Despite their vital role in the ecosystem, their activity is largely unstudied. In this work, we develop a multi-sensor camera trap, named ’Ethocam,’ to capture ant behavioral patterns in the field, and combine this technology with a computer vision approach to track colony activity in an undisturbed fashion. We demonstrate an accurate system for counting ants built with minimal human labeling. We show that L. occidentale activity drops rapidly through the morning and study the effect of environmental conditions on ant count. We also report the occurrences of the ants’ interactions with other invertebrates in our camera trap data. Together, these findings demonstrate the potential of our system to capture the behavior of Liometopum occidentale as well as its complex associations with various local species including symbionts, potentially at landscape scale. Our study provides proof of concept for the promise of low-cost remote monitoring of social insect populations.

关键词： Symbiosis computer vision Shape Insects Wildlife Vegetation Cameras

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StegaNeRV: Video Steganography using Implicit Neural Representation

StegaNeRV: Video Steganography using Implicit Neural Represe...

引用

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

作者： Monsij Biswal Tong Shao Kenneth Rose Peng Yin Sean McCarthy Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Dolby Laboratories Inc Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Numerous studies have recently advanced the state-of-the art for representing videos through an implicit neural network (INR). As these models become increasingly ubiquitous, there is a growing demand for concealing data within INR reconstructed videos such as for storing content metadata and sensitive licensing information. In this paper, we explore a new space in video steganography, hiding a distinct image within each RGB frame output by an INR. We propose a joint training strategy of a U-Net based steganographic decoder with an INR model for video. Experimental results show that hidden images can be embedded and subsequently reconstructed with high fidelity while preserving the quality of the cover frames. Furthermore we demonstrate that by introducing an attention module which emphasizes hiding within the edges and rich texture patches in the cover frame, secret images can be reconstructed with superior quality and can also be concealed at greater resolutions.

关键词： Training Steganography Image resolution Neural networks Metadata Space exploration Decoding

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VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces

VolRAFT: Volumetric Optical Flow Network for Digital Volume ...

引用

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

作者： Tak Ming Wong Julian Moosmann Berit Zeller-Plumhoff Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Germany Institute of Materials Physics Helmholtz-Zentrum Hereon Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Optical flow-based DVC is particularly popular because of its robustness to estimate the correlation as a dense deformation vector. Recently, computer vision researchers showed that network-based optical flow approaches can outperform classical iterative optical flow approaches. In this paper, we propose a supervised machine learning approach for digital volume correlation, VolRAFT, that estimates the 3D displacement vector between the reference volume and the deformed volume. The proposed approach extends the state-of-the-art network-based optical flow method, RAFT, from 2D images to 3D volumes such that it predicts the volumetric displacement vector from the input volume pairs. Experiments show that the proposed network performs well in estimating different displacement fields when compared to cutting-edge iterative DVC methods for bone-implant materials based on high resolution synchrotron-radiation micro-computed tomography imaging data.

关键词： Training computer vision Materials science and technology Correlation Three-dimensional displays Deformation Tomography

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Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object recognition

Scattering Prompt Tuning: A Fine-tuned Foundation Model for ...

引用

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

作者： Weilong Guo Shengyang Liv Jian Yang Key Laboratory of Space Utilization Chinese Academy of Sciences Technology and the Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic Aperture Radar (SAR) serves as a vital tool in various earth observation applications, providing robust imaging under challenging weather conditions. While the fine-tuned foundation models excel in many downstream tasks, they struggle with SAR object recognition because of SAR's unique imaging and scattering characteristics. In this study, we propose a novel approach named Scattering Prompt Tuning (SPT) based vision foundation model. It uses SAR image scattering information as a prompt and integrates learnable parameters into the pre-trained model's input space to help learn SAR's unique information. We also employ a lightweight Residual AdapterMLP for fine-tuning, design a Sequential Feature Aggregation (SFA) to selectively fuse features from different transformer blocks effectively, and develop a Dynamic Distributional Contrast loss (DCLoss) to maintain the proper distance between different objects in feature space. Additionally, a four-stage training strategy, incorporating semi-supervised learning, is deployed to enhance SAR object recognition performance further Our approach reaches a Top-1 accuracy of 37.9% and an AUROC of 0.83 on the final dataset, winning the first place in the SAR Classification track of PBVS 2024 Multi-modal Aerial View Object Classification Challenge, which is better than the latest advanced fine-tuned foundation models.

关键词： Training Scattering Imaging Semisupervised learning Radar imaging Radar polarimetry pattern recognition

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Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

引用

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

作者： Denis Dresvyanskiy Maxim Markitantov Jiawei Yu Heysem Kaya Alexey Karpov Ulm University Germany ITMO University Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely ‘in-the-wild’ data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the Af-fWild2 dataset following the Affective Behavior Analysis in-the-Wild 2024 (ABAW’24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： Deep learning Emotion recognition Protocols conferences Estimation computer architecture Transformers

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Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distrib...

引用

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

作者： Syed Sha Qutub Michael Paulitsch Kay-Ulrich Scholl Neslihan Kose Cihangir Korbinian Hagn Fabian Oboril Gereon Hinz Alois Knoll Intel Labs Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models to enhance reliability in safety-critical machine learning applications such as autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases the OOD performance by integrating a diversity loss into the training process on top of the budding ensemble architecture, detecting Far-OOD samples and minimizing false positives on Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model’s OOD performance and improves the calibration of confidence scores, particularly concerning the intersection over union of the detected objects. The DBEA model achieves these advancements with a 14% reduction in trainable parameters compared to the vanilla model. This signifies a substantial improvement in efficiency without compromising the model’s ability to detect OOD instances and calibrate the confidence scores accurately.

关键词： Training Adaptation models Zero-shot learning Computational modeling Scalability computer architecture Object detection

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Low Latency Point Cloud Rendering with Learned Splatting

Low Latency Point Cloud Rendering with Learned Splatting

引用

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

作者： Yueyu Hu Ran Gong Qi Sun Yao Wang Tandon School of Engineering New York University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts.

关键词： Point cloud compression Visualization Three-dimensional displays Visual communication Neural networks Rendering (computer graphics) Robustness

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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Conv-Adapter: Exploring Parameter Efficient Transfer Learnin...

引用

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

作者： Hao Chen Ran Tao Han Zhang Yidong Wang Xiang Li Wei Ye Jindong Wang Guosheng Hu Marios Savvides Carneige Mellon University Peking University Microsoft Research Asia Oosto

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-scale ConvNets is still under-studied on computer vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for ConvNets. Conv-Adapter is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks. When transferring on downstream tasks, Conv- Adapter learns tasks-specific feature modulation to the intermediate representations of backbones while keeping the pre-trained parameters frozen. By introducing only a tiny amount of learnable parameters, e.g., only 3.5% full finetuning parameters of ResNet50. It can also be applied for transformer-based backbones. Conv-Adapter outperforms previous PET baseline methods and achieves comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains. It also presents superior performance on the few-shot classification with an average margin of 3.39%. Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning 1

关键词： computer vision conferences Transfer learning Modulation Transformers Natural language processing Robustness

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