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共 20950 条记录，以下是561-570 订阅

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Graph Representation for Order-aware Visual Transformation

Graph Representation for Order-aware Visual Transformation

引用

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

作者： Qiu, Yue Sun, Yanjun Matsuzawa, Fumiya Iwata, Kenji Kataoka, Hirokatsu Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9798350301298

This paper proposes a new visual reasoning formulation that aims at discovering changes between image pairs and their temporal orders. Recognizing scene dynamics and their chronological orders is a fundamental aspect of human cognition. The aforementioned abilities make it possible to follow step-by-step instructions, reason about and analyze events, recognize abnormal dynamics, and restore scenes to their previous states. However, it remains unclear how well current AI systems perform in these capabilities. Although a series of studies have focused on identifying and describing changes from image pairs, they mainly consider those changes that occur synchronously, thus neglecting potential orders within those changes. To address the above issue, we first propose a visual transformation graph structure for conveying order-aware changes. Then, we bench-marked previous methods on our newly generated dataset and identified the issues of existing methods for change order recognition. Finally, we show a significant improvement in order-aware change recognition by introducing a new model that explicitly associates different changes and then identifies changes and their orders in a graph representation.

关键词： and reasoning language vision

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Semi-supervised Parametric Real-world Image Harmonization

Semi-supervised Parametric Real-world Image Harmonization

引用

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

作者： Wang, Ke Gharbi, Michael Zhang, He Xia, Zhihao Shechtman, Eli Adobe Inc San Jose CA 95110 USA Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9798350301298

Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo. This simulated data does not model many of the important appearance mismatches (illumination, object boundaries, etc.) between foreground and background in real composites, leading to models that do not generalize well and cannot model complex local changes. We propose a new semi-supervised training strategy that addresses this problem and lets us learn complex local appearance harmonization from unpaired real composites, where foreground and background come from different images. Our model is fully parametric. It uses RGB curves to correct the global colors and tone and a shading map to model local variations. Our method outperforms previous work on established benchmarks and real composites, as shown in a user study, and processes high-resolution images interactively. Code, and project page available at: https://***/sprih/.

关键词： Low-level vision

来源：评论

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Object Detection with Self-Supervised Scene Adaptation

Object Detection with Self-Supervised Scene Adaptation

引用

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

作者： Zhang, Zekun Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9798350301298

This paper proposes a novel method to improve the performance of a trained object detector on scenes with fixed camera perspectives based on self-supervised adaptation. Given a specific scene, the trained detector is adapted using pseudo-ground truth labels generated by the detector itself and an object tracker in a cross-teaching manner. When the camera perspective is fixed, our method can utilize the background equivariance by proposing artifact-free object mixup as a means of data augmentation, and utilize accurate background extraction as an additional input modality. We also introduce a large-scale and diverse dataset for the development and evaluation of scene-adaptive object detection. Experiments on this dataset show that our method can improve the average precision of the original detector, outperforming the previous state-of-the-art selfsupervised domain adaptive object detection methods by a large margin. Our dataset and code are published at https://***/cvlab-stonybrook/scenes100.

关键词： detection recognition: Categorization retrieval

来源：评论

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Model-Agnostic Gender Debiased Image Captioning

Model-Agnostic Gender Debiased Image Captioning

引用

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

作者： Hirota, Yusuke Nakashima, Yuta Garcia, Noa Osaka Univ Suita Osaka 565 Japan

ISBN: (纸本)9798350301298

Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models. While prior work has addressed this problem by forcing models to focus on people to reduce gender misclassification, it conversely generates gender-stereotypical words at the expense of predicting the correct gender. From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender. To mitigate both types of gender biases, we propose a framework, called LIBRA, that learns from synthetically biased samples to decrease both types of biases, correcting gender misclassification and changing gender-stereotypical words to more neutral ones.

关键词： accountability ethics in vision fairness privacy Transparency

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Robust Unsupervised StyleGAN Image Restoration

Robust Unsupervised StyleGAN Image Restoration

引用

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

作者： Poirier-Ginter, Yohan Lalonde, Jean-Franois Univ Cote Dazur INRIA Nice France Univ Laval Quebec City PQ Canada

ISBN: (纸本)9798350301298

GAN-based image restoration inverts the generative process to repair images corrupted by known degradations. Existing unsupervised methods must be carefully tuned for each task and degradation level. In this work, we make StyleGAN image restoration robust: a single set of hyperparameters works across a wide range of degradation levels. This makes it possible to handle combinations of several degradations, without the need to retune. Our proposed approach relies on a 3-phase progressive latent space extension and a conservative optimizer, which avoids the need for any additional regularization terms. Extensive experiments demonstrate robustness on inpainting, upsampling, denoising, and deartifacting at varying degradations levels, outperforming other StyleGAN-based inversion techniques. Our approach also favorably compares to diffusion-based restoration by yielding much more realistic inversion results. Code is available at the above URL.

关键词： Low-level vision

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Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

Towards Scalable 3D Anomaly Detection and Localization: A Be...

引用

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

作者： Li, Wenqiao Xu, Xiaohao Gu, Yao Zheng, Bozhong Gao, Shenghua Wu, Yingna ShanghaiTech Univ Shanghai Peoples R China Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9798350353006

Recently, 3D anomaly detection, a crucial problem involving fine-grained geometry discrimination, is getting more attention. However, the lack of abundant real 3D anomaly data limits the scalability of current models. To enable scalable anomaly data collection, we propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3D models for 3D anomaly detection. Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, based on ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories, which provides a rich and varied collection of data, enabling efficient training and enhancing adaptability to industrial scenarios. Meanwhile, to enable scalable representation learning for 3D anomaly localization, we propose a self-supervised method, i.e., Iterative Mask Reconstruction Network (IMRNet). During training, we propose a geometry-aware sample module to preserve potentially anomalous local regions during point cloud down-sampling. Then, we randomly mask out point patches and sent the visible patches to a transformer for reconstruction-based self-supervision. During testing, the point cloud repeatedly goes through the Mask Reconstruction Network, with each iteration's output becoming the next input. By merging and contrasting the final reconstructed point cloud with the initial input, our method successfully locates anomalies. Experiments show that IMRNet outperforms previous state-of-the-art methods, achieving 66.1% in I-AUC on our Anomaly-ShapeNet dataset and 72.5% in I-AUC on Real3D-AD dataset. Our benchmark will be released at https://***/Chopper-233/Anomaly-ShapeNet.

关键词： Anomaly Detection 3D vision Self-Supervised

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Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding

Visual Programming for Zero-shot Open-Vocabulary 3D Visual G...

引用

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

作者： Yuan, Zhihao Ren, Jinke Feng, Chun-Mei Zhao, Hengshuang Cui, Shuguang Li, Zhen CUHKSZ FNii Shenzhen Peoples R China CUHKSZ SSE Shenzhen Peoples R China ASTAR IHPC Singapore Singapore HKU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary, which can be restrictive. To address this issue, we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG, leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method, engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this, we design a visual program that consists of three types of modules, i.e., view-independent, view-dependent, and functional modules. These modules, specifically tailored for 3D scenarios, work collaboratively to perform complex reasoning and inference. Furthermore, we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines, marking a significant stride towards effective 3DVG. Code is available at https://***/ZSVG3D.

关键词： Point Cloud vision and Language Visual Grounding

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

引用

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

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Shenzhen Univ Comp Vision Inst Sch Comp Sci Software Engn Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9798350353006

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support port prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

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Co-training 2^L Submodels for Visual recognition

Co-training 2<SUP>L</SUP> Submodels for Visual Recognition

引用

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

作者： Touvron, Hugo Cord, Matthieu Oquab, Maxime Bojanowski, Piotr Verbeek, Jakob Jegou, Herve Meta AI FAIR Paris Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798350301298

We introduce submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we implicitly instantiate two altered networks, "sub-models", with stochastic depth: we activate only a subset of the layers. Each network serves as a soft teacher to the other, by providing a loss that complements the regular loss provided by the one-hot label. Our approach, dubbed "co-sub", uses a single set of weights, and does not involve a pre-trained external model or temporal averaging. Experimentally, we show that submodel co-training is effective to train backbones for recognition tasks such as image classification and semantic segmentation. Our approach is compatible with multiple architectures, including RegNet, ViT, PiT, XCiT, Swin and ConvNext. Our training strategy improves their results in comparable settings. For instance, a ViT-B pretrained with cosub on ImageNet-21k obtains 87.4% top-1 acc. @448 on ImageNet-val.

关键词： Deep learning architectures and techniques

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LASO: Language-guided Affordance Segmentation on 3D Object

LASO: Language-guided Affordance Segmentation on 3D Object

引用

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

作者： Li, Yicong Zhao, Na Xiao, Junbin Feng, Chun Wang, Xiang Chua, Tat-seng Natl Univ Singapore Singapore Singapore Singapore Univ Technol & Design Singapore Singapore Univ Sci & Technol China Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Inst Dataspace Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

Segmenting affordance in 3D data is key for bridging perception and action in robots. Existing efforts mostly focus on the visual side and overlook the affordance knowledge from a semantic aspect. This oversight not only limits their generalization to unseen objects, but more importantly, hinders their synergy with large language models (LLMs) which are excellent task planners that can decompose an overarching command into agent-actionable instructions. With this regard, we propose a novel task, Language-guided Affordance Segmentation on 3D Object (LASO), which challenges a model to segment a 3D object's part relevant to a given affordance question. To facilitate the task, we contribute a dataset comprising 19,751 point-question pairs, covering 8434 object shapes and 870 expert-crafted questions. As a pioneer solution, we further propose PointRefer, which highlights an adaptive fusion module to identify target affordance regions at different scales. To ensure a text-aware segmentation, we adopt a set of affordance queries conditioned on linguistic cues to generate dynamic kernels. These kernels are further used to convolute with point features and generate a segmentation mask. Comprehensive experiments and analyses validate PointRefer's effectiveness. With these efforts, We hope that LASO can steer the direction of 3D affordance, guiding it towards enhanced integration with the evolving capabilities of LLMs. Code and data are available at https://***/yl3800/LASO.

关键词： 3D-Language Multimodal vision-Language

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