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DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection

DaFF: Dual Attentive Feature Fusion for Multispectral Pedest...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Afnan Althoupety Li-Yun Wang Wu-Chi Feng Banafsheh Rekabdar Portland State University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inspired by how humans perceive and interpret the world using multiple senses, multi-modal learning involves integrating information from multiple modalities to improve understanding and performance in various tasks. Aligning with that notion, our key intuition is to utilize multi-model learning to solve the domain shift problem in night-time pedestrian *** this paper, we show that pairing RGB and infrared (IR) image features increases the robustness of pedestrian detection at night. Indeed, this solution is unbiased towards a specific time of the day as the IR domain reduces the reliance on lighting and serves as complementary information to the RGB domain. Our work aims at exploiting the power of attention mechanisms to guide a multi-modal framework in feature fusing from RGB and IR modalities. Our novel fusion approach, named dual attentive feature fusion (DaFF), leverages the duality of the transformer and channel-wise global attentions. To demonstrate the effectiveness of DaFF, we conducted experiments on two real-world multispectral pedestrian datasets. Extensive experimental results reveal the superiority of DaFF. We believe that combining the complementary properties of RGB and IR modalities is an effective remedy to mitigate the domain shift problem in pedestrian detection.

关键词： Degradation computer vision Pedestrians Attention mechanisms conferences Lighting Feature extraction

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SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes

SemiGPC: Distribution-Aware Label Refinement for Imbalanced ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Abdelhak Lemkhenter Manchen Wang Luca Zancato Gurumurthy Swaminathan Paolo Favaro Davide Modolo AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as Co-Match [17] and SimMatch [34], our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity. This explicit control allows SemiGPC to be more robust to confirmation bias especially under class imbalance. We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch [23], ReMixMatch [4], SimMatch [34] and FreeMatch [32] and different pre-training strategies including MSN [2] and Dino [5]. We also show that SemiGPC achieves state of the art results under different degrees of class imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime. Using SemiGPC also results in about 2% avg. accuracy increase compared to a new competitive baseline on the more challenging benchmarks SemiAves, SemiCUB, SemiFungi [27] and Semi-iNat [26].

关键词： computer vision Sensitivity Accuracy conferences Gaussian processes Semisupervised learning Predictive models

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tonmoay Deb Lichen Wang Zachary Bessinger Naji Khosravan Eric Penner Sing Bing Kang Northwestern University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper focuses on bridging the gap between natural language descriptions, 360° panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell 1 . We first introduce an effective technique for extracting geometric information from ZInD’s raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naïve methods in both tasks, hence, can be considered as a complement to the naïve to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： Geometry computer vision Shape Focusing Benchmark testing Information retrieval Human in the loop

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引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shihong Wang Ruixun Liu Kaiyu Li Jiawei Jiang Xiangyong Cao Xi’an Jiaotong University China Sun Yat-Sen University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In Generalized Few-shot Segmentation (GFSS), a model is trained with a large set of base class samples and then adapted on limited samples of novel classes. This paper focuses on the relevance between base and novel classes, and improves GFSS in two aspects: 1) mining the similarity between base and novel classes to promote the learning of novel classes, and 2) mitigating the class imbalance issue caused by the volume difference between the support set and the training set. Specifically, we first propose a similarity transition matrix to guide the learning of novel classes with base class knowledge. Then, we leverage the Label-Distribution-Aware Margin (LDAM) loss and Trans-ductive Inference to the GFSS task to address the problem of class imbalance as well as overfitting the support set. In addition, by extending the probability transition matrix, the proposed method can mitigate the catastrophic forgetting of base classes when learning novel classes. With a simple training phase, our proposed method can be applied to any segmentation network trained on base classes. We validated our methods on the adapted version of OpenEarthMap. Compared to existing GFSS baselines, our method excels them all from 3% to 7% and ranks second in the OpenEarthMap Land Cover Mapping Few-Shot Challenge at the completion of this paper.

关键词： Training computer vision Adaptation models conferences Land surface pattern recognition

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wenjin Zhang Keyi Li Sen Yang Sifan Yuan Ivan Marsic Genevieve J. Sippel Mary S. Kim Randall S. Burd Rutgers University Waymo Children’s National Hospital

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： computer vision conferences Focusing Self-supervised learning Manuals Activity recognition Time factors

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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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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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QuantNAS: Quantization-aware Neural Architecture Search For Efficient Deployment On Mobile Device

QuantNAS: Quantization-aware Neural Architecture Search For ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tianxiao Gao Li Guo Shanwei Zhao Peihan Xu Yukun Yang Xionghao Liu Shihao Wang Shiai Zhu Dajiang Zhou Ant Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep convolutional networks are increasingly applied in mobile AI scenarios. To achieve efficient deployment, researchers combine neural architecture search (NAS) and quantization to find the best quantized architecture. However, existing methods overlook the on-device implementation of quantization. The searching result is usually sub-optimal or has limited latency reduction. To this end, we propose QuantNAS, a novel quantization-aware NAS based on a two-stage one-shot method. Different from the previous method, our method considers the on-device implementation of the quantized network and searches for the architecture from a fully quantized supernet. During training, we propose a batch-statistics-based strategy to alleviate the non-convergence problem. Besides, a scale predictor is proposed and is jointly trained with the supernet. During search, the scale predictor can provide optimal scale for different subnets without retraining. At different latency levels on Kirin 9000 mobile CPU, the proposed method achieves 1.53%-1.68% Top-1 accuracy improvement on ImageNet 1K dataset and 1.7% mAP improvement.

关键词： Training computer vision Quantization (signal) conferences Computational modeling computer architecture Mobile handsets

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Analyzing the Internals of Neural Radiance Fields

Analyzing the Internals of Neural Radiance Fields

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

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

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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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