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Shadow Removal via Global Residual Free Unet and Shadow Generation

Shadow Removal via Global Residual Free Unet and Shadow Gene...

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

作者： Dong Li Xin Lu Yurui Zhu Xi Wang Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing shadow removal methods face challenges when confronted with real-world scenes, particularly when dealing with complex background information and high-resolution images. To address these issues, we present a shadow removal framework based on shadow generation and Global Residual Free Unet (GRFUnet), which improves shadow removal from both data and network perspectives. For data enhancement, we train a generative adversarial network for producing shadow masks, which are then multiplied with clean images to obtain new shadow images. The additional shadow data produced by this structured generation approach allows for ample constraining of the network in color aspects, thereby enhancing color consistency in the images. In terms of network architecture, we design the Global Residual Free Unet that employs a convolutional framework to sequentially conduct Spatial Interaction and Channel Evolution for effective feature extraction. Moreover, we eliminate the commonly used Global residual connections in image restoration, as we discern their in-effectiveness for the non-additive task of shadow removal. Through this methodology, we achieve excellent shadow removal results, both qualitatively and quantitatively. Our method achieves the highest PSNR in the NTIRE24-Image Shadow Removal Challenge and achieves commendable and balanced outcomes across two tracks—placing third in the fidelity track and fourth in the perceptual track.

关键词： computer vision Image color analysis conferences computer architecture Network architecture Feature extraction Generative adversarial networks

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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

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

作者： Niklas Wagner Felix Mätzler Samed R. Vossberg Helen Schneider Svetlana Pavlitska J. Marius Zöllner Karlsruhe Institute of Technology (KIT) Germany FZI Research Center for Information Technology Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https://***/wagner-niklas/CAGE_expression_inference.

关键词： Training computer vision Analytical models Computational modeling conferences computer architecture Predictive models

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SAM-CLIP: Merging vision Foundation Models towards Semantic and Spatial Understanding

SAM-CLIP: Merging Vision Foundation Models towards Semantic ...

引用

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

作者： Haoxiang Wang Pavan Kumar Anasosalu Vasu Fartash Faghri Raviteja Vemulapalli Mehrdad Farajtabar Sachin Mehta Mohammad Rastegari Oncel Tuzel Hadi Pouransari University of Illinois Urbana-Champaign Apple

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their pre-training objectives. For instance, CLIP excels in semantic understanding, while SAM specializes in spatial understanding for segmentation. In this work, we introduce a simple recipe to efficiently merge VFMs into a unified model that absorbs their expertise. Our method integrates techniques of multitask learning, continual learning, and distillation. Further, it demands significantly less computational cost compared to traditional multi-task training from scratch, and it only needs a small fraction of the pre-training datasets that were initially used to train individual models. By applying our method to SAM and CLIP, we obtain SAM-CLIP : a unified model that combines the capabilities of SAM and CLIP into a single vision transformer. Compared with deploying SAM and CLIP independently, our merged model, SAM-CLIP, reduces storage and compute costs for inference, making it well-suited for edge device applications. We show that SAM-CLIP not only retains the foundational strengths of SAM and CLIP, but also introduces synergistic functionalities, notably in zero-shot semantic segmentation, where SAM-CLIP establishes new state-of-the-art results on 5 benchmarks. It outperforms previous models that are specifically designed for this task by a large margin, including +6.8% and +5.9% mean IoU improvement on Pascal-VOC and COCO-Stuff datasets, respectively.

关键词： Training computer vision Visualization Computational modeling Semantic segmentation Semantics Merging

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PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation

PP-SAM: Perturbed Prompts for Robust Adaption of Segment Any...

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

作者： Md Mostafijur Rahman Mustafa Munir Debesh Jha Ulas Bagci Radu Marculescu The University of Texas at Austin Northwestern University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM), originally designed for general-purpose segmentation tasks, has been used recently for polyp segmentation. Nonetheless, fine-tuning SAM with data from new imaging centers or clinics poses significant challenges. This is because this necessitates the creation of an expensive and time-intensive annotated dataset, along with the potential for variability in user prompts during inference. To address these issues, we propose a robust fine-tuning technique, PP-SAM, that allows SAM to adapt to the polyp segmentation task with limited images. To this end, we utilize variable perturbed bounding box prompts (BBP) to enrich the learning context and enhance the model’s robustness to BBP perturbations during inference. Rigorous experiments on polyp segmentation benchmarks reveal that our variable BBP perturbation significantly improves model resilience. Notably, on Kvasir, 1-shot fine-tuning boosts the DICE score by 20% and 37% with 50 and 100-pixel BBP perturbations during inference, respectively. Moreover, our experiments show that 1-shot, 5-shot, and 10-shot PP-SAM with 50-pixel perturbations during inference outperform a recent state-of-the-art (SOTA) polyp segmentation method by 26%, 7%, and 5% DICE scores, respectively. Our results motivate the broader applicability of our PP-SAM for other medical imaging tasks with limited samples. Our implementation is available at https://***/SLDGroup/PP-SAM.

关键词： Training Image segmentation Adaptation models Perturbation methods conferences Robustness pattern recognition

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DiCo-NeRF: Difference of Cosine Similarity for Neural Rendering of Fisheye Driving Scenes

DiCo-NeRF: Difference of Cosine Similarity for Neural Render...

引用

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

作者： Jiho Choi Gyutae Hwang Sang Jun Lee Jeonbuk National University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural radiance fields have emerged in the field of autonomous driving, which contributes to improve perception of the complex 3D environment through the reconstruction of geometry and appearance. Moving objects and sky for outdoor environment is challenging to optimize the NeRF model. Previous work addresses these challenges through preprocessing such as masking; however, the masking process requires additional ground-truth data and a segmentation network. We propose DiCo-NeRF, an approach for driving scenes by leveraging cosine similarity map differences of vision-language aligned model. DiCo-NeRF investigates the correlation between rendered patches and predefined text and adjusts the loss of challenging patches, such as moving objects and the sky. Our neural radiance field utilizes embedding vectors from a pre-trained CLIP to obtain the cosine similarity maps. We introduce SimLoss, a loss function aimed at regulating the color field of NeRF based on the quantified distribution differences between ground-truth and rendered similarity maps. Unlike previous NeRF models that used driving datasets, our approach does not require additional input, such as sensor data, to the model. Experimental results demonstrate that the incorporation of language semantic cues improves the performance of the novel view synthesis task, particularly in complex driving environments. We conducted experiments that included fisheye driving scenes from the KITTI-360 and real-world datasets. Our code is available at https://***/ziiho08/DiCoNeRF.

关键词： Three-dimensional displays Image color analysis Computational modeling Semantics Neural radiance field Rendering (computer graphics) Vectors

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Robustness Analysis on Foundational Segmentation Models

Robustness Analysis on Foundational Segmentation Models

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

作者： Madeline Chantry Schiappa Shehreen Azad Sachidanand Vs Yunhao Ge Ondrej Miksik Yogesh S Rawat Vibhav Vineet Center for Research in Computer Vision University of Central Florida Indian Institute of Technology Madras University of Southern California Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of multi-modal data using self-supervised or semi-supervised learning have emerged. These "foundation" models are often adapted to a variety of downstream tasks like classification, object detection, and segmentation with little-to-no training on the target dataset. In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks and focus on robustness against real-world distribution shift inspired perturbations. We benchmark seven state-of-the-art segmentation architectures using 2 different perturbed datasets, MS COCO-P and ADE20K-P, with 17 different perturbations with 5 severity levels each. Our findings reveal several key insights: (1) VFMs exhibit vulnerabilities to compression-induced corruptions, (2) despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and (3) VFMs demonstrate enhanced robustness for certain object categories. These observations suggest that our robustness evaluation framework sets new requirements for foundational models, encouraging further advancements to bolster their adaptability and performance. The code and dataset is available at: https://***/fm-robust.

关键词： Training Adaptation models Visualization Analytical models Perturbation methods Computational modeling Benchmark testing

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

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

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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The New Agronomists: Language Models are Experts in Crop Management

The New Agronomists: Language Models are Experts in Crop Man...

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

作者： Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan University of Illinois at Urbana-Champaign Cornell University University of South Florida University of Alabama

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing simple neural-network-based reinforcement learning (RL) agents. Building on this foundation, this paper introduces a more advanced intelligent crop management system. This system uniquely combines RL, a language model (LM), and crop simulations facilitated by the Decision Support System for Agrotechnology Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model’s capacity to understand states and explore optimal management practices. The empirical results reveal that the LM exhibits superior learning capabilities. Through simulation experiments with maize crops in Florida (US) and Zaragoza (Spain), the LM not only achieves state-of-the-art performance under various evaluation metrics but also demonstrates a remarkable improvement of over 49% in economic profit, coupled with reduced environmental impact when compared to baseline methods. Our code is available at https://***/jingwu6/LM_AG.

关键词： Decision support systems Profitability Noise Green products Crops Deep reinforcement learning pattern recognition

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UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

UAV-Net: A Fast Aerial Vehicle Detector for Mobile Platforms

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

作者： Tobias Ringwald Lars Sommer Arne Schumann Jürgen Beyerer Rainer Stiefelhagen CV:HCI Karlsruhe Institute of Technology Karlsruhe Germany Vision and Fusion Lab Karlsruhe Institute of Technology Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Vehicle detection in aerial imagery is a challenging task due to small object sizes, high object density and partial occlusions. While past research mostly focused on improving detection accuracy, inference speed is another important factor when using CNN object detectors in a real life scenario - especially when targeting mobile platforms like unmanned aerial vehicles (UAVs). In this work, we compare several established detection frameworks in terms of their accuracy-speed trade-off and show that the Single Shot MultiBox Detector (SSD) offers the best compromise. We subsequently undertake a thorough evaluation of several design choices to further increase detection speed while sacrificing little to no accuracy. This includes the choice of base network architecture, improved prediction layers and an automatic model pruning approach. Given our evaluation results, we finally construct UAV-Net - a novel aerial vehicle detector that has a model size of less than 0.4 MiB and is more than 16 times faster than current top performing approaches. UAV-Net is well suited for on-board processing and operates in real time on a Jetson TX2 platform. Nevertheless, its accuracy is on par with state-of-the-art approaches on the DLR 3K, VEDAI and UAVDT datasets. Code and models are available on the project website.

关键词： Convolution Detectors Feature extraction Vehicle detection Task analysis Machine learning Kernel

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Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images

Sat2Cap: Mapping Fine-Grained Textual Descriptions from Sate...

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

作者： Aayush Dhakal Adeel Ahmad Subash Khanal Srikumar Sastry Hannah Kerner Nathan Jacobs Washington University in St. Louis Taylor Geospatial Institute Arizona State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping. Prior works have approached mapping tasks by developing models that predict a fixed set of attributes using overhead imagery. However, these models are very restrictive as they can only solve highly specific tasks for which they were trained. Mapping text, on the other hand, allows us to solve a large variety of mapping problems with minimal restrictions. To achieve this, we train a contrastive learning framework called Sat2Cap on a new large-scale dataset with 6.1M pairs of overhead and ground-level images. For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery. The predicted CLIP embeddings are then used to learn about the textual space associated with that location. Sat2Cap is also conditioned on date-time information, allowing it to model temporally varying concepts over a location. Our experimental results demonstrate that our models successfully capture ground-level concepts and allow large-scale mapping of fine-grained textual queries. Our approach does not require any text-labeled data, making the training easily scalable. The code, dataset, and models will be made publicly available.

关键词： Training computer vision Codes Geology conferences Contrastive learning Predictive models

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