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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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Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Label-free Anomaly Detection in Aerial Agricultural Images w...

引用

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

作者： Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India

ISBN: (纸本)9798350365474

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-vision Challenge Dataset [5].

关键词： Anomaly Detection computer vision Masked Image Modelling Precision Agriculture UAV Images

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LASO: Language-guided Affordance Segmentation on 3D Object

LASO: Language-guided Affordance Segmentation on 3D Object

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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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Emotion recognition Using Transformers with Random Masking

Emotion Recognition Using Transformers with Random Masking

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

作者： Min, Seongjae Yang, Junseok Lim, Sejoon Kookmin Univ Seoul South Korea

ISBN: (纸本)9798350365474

In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors. Initiatives such as the Affective Behavior Analysis in-the-wild (ABAW) competition have been particularly instrumental in driving research in this area by providing diverse and challenging datasets that enable precise evaluation of complex emotional states. This study leverages the vision Transformer (ViT) and Transformer models to focus on the estimation of Valence-Arousal (VA), which signifies the positivity and intensity of emotions, recognition of various facial expressions, and detection of Action Units (AU) representing fundamental muscle movements. This approach transcends traditional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) based methods, proposing a new Transformer-based framework that maximizes the understanding of temporal and spatial features. The core contributions of this research include the introduction of a learning technique through random frame masking and the application of Focal loss adapted for imbalanced data, enhancing the accuracy and applicability of emotion and behavior analysis in real-world settings. This approach is expected to contribute to the advancement of emotional computing and deep learning methodologies.

关键词： Convolutional neural networks

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Coarse or Fine? Recognising Action End States without Labels

Coarse or Fine? Recognising Action End States without Labels

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

作者： Moltisanti, Davide Bilen, Hakan Sevilla-Lara, Laura Keller, Frank Univ Bath Bath Avon England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350365474

We focus on the problem of recognising the end state of an action in an image, which is critical for understanding what action is performed and in which manner. We study this focusing on the task of predicting the coarseness of a cut, i.e., deciding whether an object was cut "coarsely" or "finely". No dataset with these annotated end states is available, so we propose an augmentation method to synthesise training data. We apply this method to cutting actions extracted from an existing action recognition dataset. Our method is object agnostic, i.e., it presupposes the location of the object but not its identity. Starting from less than a hundred images of a whole object, we can generate several thousands images simulating visually diverse cuts of different coarseness. We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects. Results demonstrate that the model successfully recognises the end state of the cutting action despite the domain gap between training and testing, and that the model generalises well to unseen objects.

关键词： adverb recognition fine-grained recognition object end-state recognition

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Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

Phase-Shifting Coder: Predicting Accurate Orientation in Ori...

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

作者： Yu, Yi Da, Feipeng Southeast Univ Sch Automat Nanjing Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China

ISBN: (纸本)9798350301298

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://***/open-mmlab/mmrotate.

关键词： detection recognition: Categorization retrieval

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Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological Images

Tumor Micro-environment Interactions Guided Graph Learning f...

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

作者： Shao, Wei Shi, YangYang Zhang, Daoqiang Zhou, JunJie Wan, Peng Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Key Lab Brain Machine Intelligence Technol Minist Educ Nanjing Peoples R China MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However, most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs, which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact, tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME), and the de-tailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper, we considered the spatial interactions among tumor and its two major TME components (i.e., lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically, we firstly selected different types of patches as nodes to build graph for each WSI. Then, a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally, a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models, but also has good explainable ability for survival prediction.

关键词： Survival Analysis

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Exploring the Effect of Primitives for Compositional Generalization in vision-and-Language

Exploring the Effect of Primitives for Compositional General...

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

作者： Li, Chuanhao Li, Zhen Jing, Chenchen Jia, Yunde Wu, Yuwei Beijing Inst Technol Sch Comp Sci & Technol Beijing Key Lab Intelligent Informat Technol Beijing Peoples R China Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Shenzhen Peoples R China Zhejiang Univ Sch Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Compositionality is one of the fundamental properties of human cognition (Fodor & Pylyshyn, 1988). Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community. It is essential to understand the effect of the primitives, including words, image regions, and video frames, to improve the compositional generalization capability. In this paper, we explore the effect of primitives for compositional generalization in V&L. Specifically, we present a self-supervised learning based framework that equips existing V&L methods with two characteristics: semantic equivariance and semantic invariance. With the two characteristics, the methods understand primitives by perceiving the effect of primitive changes on sample semantics and ground-truth. Experimental results on two tasks: temporal video grounding and visual question answering, demonstrate the effectiveness of our framework.

关键词： language reasoning vision

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Data-efficient Large Scale Place recognition with Graded Similarity Supervision

Data-efficient Large Scale Place Recognition with Graded Sim...

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

作者： Leyva-Vallina, Maria Strisciuglio, Nicola Petkov, Nicolai Univ Groningen Groningen Netherlands Univ Twente Enschede Netherlands

ISBN: (纸本)9798350301298

Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques.

关键词： detection recognition: Categorization retrieval

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