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Doodle2Clothing: A clothing design recognition and searching model from a doodle drawing 4

Doodle2Clothing: A clothing design recognition and searching...

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4th ieee Interdisciplinary conference on Electrics and computer (INTCEC)

作者： Ani, Md Fahim Afridi Palak, Asif Zubayer Dip, Shaikh Atisha Rahbath Pramanik, Amab Medha, Tayaba Amin Sadeque, Farig Yousuf Brac Univ Dhaka Bangladesh

ISBN: (纸本)9798350349467;9798350349450

In recent years, pattern recognition has advanced, allowing computers to detect and classify things from various sources. Recent advances in image and pattern recognition research can enhance user experience for various applications. Our research aims to address a common issue for shoppers: locating apparel with a specific style or pattern. Our system uses computer vision and pattern recognition to recognise designs on clothing based on user sketches and finds the best images or products online. To train our model, we will collect doodle sketches from end users by presenting them with a clothing product image. The model will extract features based on shape, colour, and design. The model will then generate a search query which will be inputted on a search engine to find the product that most closely resembles the doodle. Our methodology enables shoppers to find desired clothing without excessive browsing, improving human-computer connection and simplifying purchasing experiences.

关键词： computer vision pattern recognition Doodle sketches Human-computer interaction

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Seeing the Unseen: Visual Common Sense for Semantic Placement

Seeing the Unseen: Visual Common Sense for Semantic Placemen...

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

作者： Ramrakhya, Ram Kembhavi, Aniruddha Batra, Dhruv Kira, Zsolt Zeng, Kuo-Hao Weihs, Luca Georgia Inst Technol Atlanta GA 30332 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9798350353006

computer vision tasks typically involve describing what is present in an image (e.g. classification, detection, segmentation, and captioning). We study a visual common sense task that requires understanding 'what is not present'. Specifically, given an image (e.g. of a living room) and a name of an object ("cushion"), a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house), AR devices (automatically rendering an object in the user's space), and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image;neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context, which is easy to find online, and then remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a dataset comprising pairs of images with/without the object. With this proposed data generation pipeline, we collect a novel dataset, containing similar to 1.3M images across 9 object categories. We then train a SP prediction model, called CLIP-UNet, on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors, generalizes well to real-world and simulated images and exhibits semantics-aware reasoning for object placement. In our user studies, we find that the SP masks predicted by CLIP-UNet are favored 43.7% and 31.3% times when comparing against the 4 SP baselines on real and simulated

关键词： Common Sense Reasoning computer vision Embodied AI

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

引用

31st Meeting of the ieee/CVF conference on computer vision and pattern recognition, CVPR 2018

ISBN: (纸本)9781538664209

The proceedings contain 979 papers. The topics discussed include: embodied question answering;learning by asking questions;finding tiny faces in the wild with generative adversarial network;paired CycleGAN: asymmetric style transfer for applying and removing makeup;learning pose specific representations by predicting different views;weakly and semi supervised human body part parsing via pose-guided knowledge transfer;person transfer GAN to bridge domain gap for person re-identification;cross-modal deep variational hand pose estimation;disentangled person image generation;super-fan: integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs;multistage adversarial losses for pose-based human image synthesis;and a certifiably globally optimal solution to the non-minimal relative pose problem.

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Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention

Modality-agnostic Domain Generalizable Medical Image Segment...

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

作者： Nam, Ju-Hyeon Lee, Sang-Chul Syazwanyl, Nur Suriza Kim, Su Jung Inha Univ Dept Elect & Comp Engn Incheon South Korea DeepCardio Seoul South Korea

ISBN: (纸本)9798350353006

Generalizability in deep neural networks plays a pivotal role in medical image segmentation. However, deep learning-based medical image analyses tend to overlook the importance of frequency variance, which is critical element for achieving a model that is both modality-agnostic and domain-generalizable. Additionally, various models fail to account for the potential information loss that can arise from multi-task learning under deep supervision, a factor that can impair the model's representation ability. To address these challenges, we propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation, which comprises two key components: a Multi-Frequency in Multi-Scale Attention (MFMSA) block and Ensemble Sub-Decoding Module (E-SDM). The MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features, by incorporating multi-frequency and multi-scale features, thereby offering informative cues for tissue outline and anatomical structures. Moreover, we propose E-SDM to mitigate information loss in multi-task learning with deep supervision, especially during substantial upsampling from low resolution. We evaluate the segmentation performance of MADGNet across six modalities and fifteen datasets. Through extensive experiments, we demonstrate that MADGNet consistently outperforms state-of-the-art models across various modalities, showcasing superior segmentation performance. This affirms MADGNet as a robust solution for medical image segmentation that excels in diverse imaging scenarios. Our MADGNet code is available in GitHub Link.

关键词： computer vision Image Segmentation Medical Image Analysis

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Language Models as Black-Box Optimizers for vision-Language Models

Language Models as Black-Box Optimizers for Vision-Language ...

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

作者： Liu, Shihong Yu, Samuel Lin, Zhiqiu Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.

关键词： generative models large language models prompting text-to-image generation vision-language models

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EgoThink: Evaluating First-Person Perspective Thinking Capability of vision-Language Models

EgoThink: Evaluating First-Person Perspective Thinking Capab...

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

作者： Cheng, Sijie Guo, Zhicheng Wu, Jingwen Fang, Kechen Li, Peng Liu, Huaping Liu, Yang Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing Peoples R China Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Tsinghua Univ Zhili Coll Beijing Peoples R China 01 Ai Beijing Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority focusing on the third-person perspective, and only a few addressing specific tasks from the first-person perspective. However, the capability of VLMs to "think" from a first-person perspective, a crucial attribute for advancing autonomous agents and robotics, remains largely unexplored. To bridge this research gap, we introduce EgoThink, a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from ego-centric videos, with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs, we evaluate twenty-one popular VLMs on EgoThink. Moreover, given the open-ended format of the answers, we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions, all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile, enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion, EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs, providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics.

关键词： Benchmark Egocentric vision-Language Models

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InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models

InteractDiffusion: Interaction Control in Text-to-Image Diff...

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

作者： Hoe, Jiun Tian Jiang, Xudong Chan, Chee Seng Tan, Yap-Peng Hu, Weipeng Nanyang Technol Univ Sch EEE Singapore Singapore Univ Malaya CISiP Kuala Lumpur Malaysia

ISBN: (纸本)9798350353013;9798350353006

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications, such as creating realistic scenes with interacting characters. In this work, we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information, consisting of a triplet label (person, action, object) and corresponding bounding boxes. We propose a pluggable interaction control model, called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically, we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens, thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models, which outperforms existing baselines by a large margin in HOI detection score, as well as fidelity in FID and KID. Project page: https://***/interactdiffusion.

关键词： computer vision conditional image generation diffusion model generative ai human-object interaction image generation

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Pre-training vision Models with Mandelbulb Variations

Pre-training Vision Models with Mandelbulb Variations

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

作者： Chiche, Benjamin Naoto Horikawa, Yuto Fujita, Ryo Rist Inc 830 Hongakujimae-ChoGojo-DoriShimogyo-Ku Kyoto 6008102 Japan

ISBN: (纸本)9798350353006

The use of models that have been pre-trained on natural image datasets like ImageNet may face some limitations. First, this use may be restricted due to copyright and license on the training images, and privacy laws. Second, these datasets and models may incorporate societal and ethical biases. Formula-driven supervised learning (FDSL) enables model pre-training to circumvent these issues. This consists of generating a synthetic image dataset based on mathematical formulae and pre-training the model on it. In this work, we propose novel FDSL datasets based on Mandelbulb Variations. These datasets contain RGB images that are projections of colored objects deriving from the 3D Mandelbulb fractal. Pre-training ResNet-50 on one of our proposed datasets MandelbulbVAR-1k enables an average top-1 accuracy over target classification datasets that is at least 1% higher than pre-training on existing FDSL datasets. With regard to anomaly detection on MVTec AD, pre-training the WideResNet-50 backbone on MandelbulbVAR-1k enables PatchCore to achieve 97.2% average image-level AUROC. This is only 1.9% lower than pre-training on ImageNet-1k (99.1%) and 4.5% higher than pre-training on the second-best performing FDSL dataset i.e. VisualAtom-1k (92.7%). Regarding vision Transformer (ViT) pre-training, another dataset that we propose and coin MandelbulbVAR-Hybrid-21k enables ViT-Base to achieve 82.2% top-1 accuracy on ImageNet-1k, which is 0.4% higher than pre-training on ImageNet-21k (81.8%) and only 0.1% lower than pre-training on VisualAtom-1k (82.3%).

关键词： anomaly detection classification convolutional neural network Formula-driven supervised learning fractal mandelbulb pre-training vision transformer

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From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

From a Bird's Eye View to See: Joint Camera and Subject Regi...

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

作者： Qian, Zekun Han, Ruize Feng, Wei Wang, Song Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Univ South Carolina Columbia SC 29208 USA

ISBN: (纸本)9798350353013;9798350353006

We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration, which promotes the multi-view subject registration problem to a new calibration-free stage. This greatly alleviates the limitation in many practical applications. However, this is a very challenging problem since its only input is several RGB images from different first-person views (FPVs), without the BEV image and the calibration of the FPVs, while the output is a unified plane aggregated from all views with the positions and orientations of both the subjects and cameras in a BEV. For this purpose, we propose an end-to-end framework solving camera and subject registration together by taking advantage of their mutual dependence, whose main idea is as below: i) creating a subject view-transform module (VTM) to project each pedestrian from FPV to a virtual BEV, ii) deriving a multi-view geometry-based spatial alignment module (SAM) to estimate the relative camera pose in a unified BEV, iii) selecting and refining the subject and camera registration results within the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for training and evaluation. Additionally, we also collect a real dataset for cross-domain evaluation. The experimental results show the remarkable effectiveness of our method. The code and proposed datasets are available at BEVSee.

关键词： Camera Registration computer vision Multiview Detection Subject Registration

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Real-World Mobile Image Denoising Dataset with Efficient Baselines

Real-World Mobile Image Denoising Dataset with Efficient Bas...

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

作者： Flepp, Roman Ignatov, Andrey Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Wurzburg Comp Vis Lab Wurzburg Germany AI Witchlabs Ltd Zollikerberg Switzerland

ISBN: (纸本)9798350353006

The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware, the mobile camera sensor area cannot be increased significantly due to physical constraints, leading to a pixel size of 0.6-2.0 mu m, which results in strong image noise even in moderate lighting conditions. In the era of deep learning, one can train a CNN model to perform robust image denoising. However, there is still a lack of a substantially diverse dataset for this task. To address this problem, we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400,000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally, we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore, we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results, while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs, demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors, showing its high generalizability. The datasets, code and models can be found on the official project website (1,2).

关键词： Computational Photography computer vision Dataset Deep Learning Denoising Denoising Dataset Efficient Denoising Model Image Denoising Image Enhancement Image Restoration Mobile Image Denoising

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