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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

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

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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ViTs for SITS: vision Transformers for Satellite Image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time ...

引用

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

作者： Tarasiou, Michail Chavez, Erik Zafeiriou, Stefanos Imperial Coll London London England

ISBN: (纸本)9798350301298

In this paper we introduce the Temporo-Spatial vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://***/michaeltrs/DeepSatModels.

关键词： Photogrammetry and remote sensing

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Learning Attribute and Class-Specific Representation Duet for Fine-grained Fashion Analysis

Learning Attribute and Class-Specific Representation Duet fo...

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

作者： Jiao, Yang Gao, Yan Meng, Jingjing Shang, Tin Sun, Yi Amazon Seattle WA 98109 USA

ISBN: (纸本)9798350301298

Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them. Existing works often learn fine-grained fashion representations at the attribute level without considering their relationships and inter-dependencies across different classes. In this work, we propose to learn an attribute and class-specific fashion representation duet to better model such attribute relationships and inter-dependencies by leveraging prior knowledge about the taxonomy of fashion attributes and classes. Through two sub-networks for the attributes and classes, respectively, our proposed an embedding network progressively learns and refines the visual representation of a fashion image to improve its robustness for fashion retrieval. A multi-granularity loss consisting of attribute-level and class-level losses is proposed to introduce appropriate inductive bias to learn across different granularities of the fashion representations. Experimental results on three benchmark datasets demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods by a large margin.

关键词： detection recognition: Categorization retrieval

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Multi Model Ensemble for Compound Expression recognition

Multi Model Ensemble for Compound Expression Recognition

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

作者： Yu, Jun Zhu, Jichao Zhu, Wangyuan Cai, Zhongpeng Zhao, Gongpeng Wei, Zhihong Xie, Guochen Zhang, Zerui Liu, Qingsong Liang, Jiaen Univ Sci & Technol China Hefei Anhui Peoples R China Unisound AI Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

Compound Expression recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions, which leads to the existence of compound expressions, it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper, to address this issue, we propose a solution for compound expression recognition based on ensemble learning methods. Specifically, our task is classification. We trained three expression classification models based on convolutional networks (ResNet50), vision Transformers, and multi-scale local attention networks, respectively. Then, by using late fusion, integrated the outputs of three models to predict the final result, leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge, achieves an F1 score of 0.224 on the test set of C-EXPR-DB.

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Rugby Scene Classification Enhanced by vision Language Model

Rugby Scene Classification Enhanced by Vision Language Model

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

作者： Nonaka, Naoki Fujihira, Ryo Koshiba, Toshiki Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Wako Saitama Japan Hakata Knee & Sports Clin Fukuoka Japan

ISBN: (纸本)9798350365474

This study investigates the integration of vision language models (VLM) to enhance the classification of situations within rugby match broadcasts. The importance of accurately identifying situations in sports videos is emphasized for understanding game dynamics and facilitating downstream tasks like performance evaluation and injury prevention. Utilizing a dataset comprising 18, 000 labeled images extracted at 0.2-second intervals from 100 minutes of rugby match broadcasts, scene classification tasks including contact plays (scrums, mauls, rucks, tackles, lineouts), rucks, tackles, lineouts, and multiclass classification were performed. The study aims to validate the utility of VLM outputs in improving classification performance compared to using solely image data. Experimental results demonstrate substantial performance improvements across all tasks with the incorporation of VLM outputs. Our analysis of prompts suggests that, when provided with appropriate contextual information through natural language, VLMs can effectively capture the context of a given image. The findings of our study indicate that leveraging VLMs in the domain of sports analysis holds promise for developing image processing models capable of incorpolating the tacit knowledge encoded within language models, as well as information conveyed through natural language descriptions.

关键词： Rugby Scene classification vision language model

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Content-aware Token Sharing for Efficient Semantic Segmentation with vision Transformers

Content-aware Token Sharing for Efficient Semantic Segmentat...

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

作者： Lu, Chenyang de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350301298

This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use vision Transformers (ViTs). Existing works have proposed token reduction approaches to improve the efficiency of ViT-based image classification networks, but these methods are not directly applicable to semantic segmentation, which we address in this work. We observe that, for semantic segmentation, multiple image patches can share a token if they contain the same semantic class, as they contain redundant information. Our approach leverages this by employing an efficient, class-agnostic policy network that predicts if image patches contain the same semantic class, and lets them share a token if they do. With experiments, we explore the critical design choices of CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes datasets, various ViT backbones, and different segmentation decoders. With Content-aware Token Sharing, we are able to reduce the number of processed tokens by up to 44%, without diminishing the segmentation quality.

关键词： Scene analysis and understanding

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Towards Efficient Audio-Visual Learners via Empowering Pre-trained vision Transformers with Cross-Modal Adaptation

Towards Efficient Audio-Visual Learners via Empowering Pre-t...

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

作者： Wang, Kai Tian, Yapeng Hatzinakos, Dimitrios Univ Toronto Toronto ON Canada Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350365474

In this paper, we explore the cross-modal adaptation of pre-trained vision Transformers (ViTs) for the audio-visual domain by incorporating a limited set of trainable parameters. To this end, we propose a Spatial-Temporal-Global Cross-Modal Adaptation (STG-CMA) to gradually equip the frozen ViTs with the capability for learning audio-visual representation, consisting of the modality-specific temporal adaptation for temporal reasoning of each modality, the cross-modal spatial adaptation for refining the spatial information with the cue from counterpart modality, and the cross-modal global adaptation for global interaction between audio and visual modalities. Our STG-CMA presents a meaningful finding that only leveraging the shared pre-trained image model with inserted lightweight adapters is enough for spatial-temporal modeling and feature interaction of audio-visual modality. Extensive experiments indicate that our STG-CMA achieves state-of-the-art performance on various audio-visual understanding tasks including AVE, AVS, and AVQA while containing significantly reduced tunable parameters. The code is available at https://***/kaiw7/STG-CMA.

关键词： Audio-visual Lenarning Cross-modal Adaptation Pre-trained vision Transformers Reduced Tunanle Parameters Spatial-temporal-global Modeling

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Rawgment: Noise-Accounted RAW Augmentation Enables recognition in a Wide Variety of Environments

Rawgment: Noise-Accounted RAW Augmentation Enables Recogniti...

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

作者： Yoshimura, Masakazu Otsuka, Junji Irie, Atsushi Ohashi, Takeshi Sony Grp Corp Tokyo Japan

ISBN: (纸本)9798350301298

Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.

关键词： Computational imaging

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Low-Rank Few-Shot Adaptation of vision-Language Models

Low-Rank Few-Shot Adaptation of Vision-Language Models

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

作者： Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350365474

Recent progress in the few-shot adaptation of vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, overlooking the recent advances in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot learning methods for VLMs often rely on heavy training procedures and/or carefully chosen, taskspecific hyper-parameters, which might impede their applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot learning for VLMs, and show its potential on 11 datasets, in comparison to current state-ofthe-art prompt- and adapter-based approaches. Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times and keeping the same hyper-parameters in all the target tasks, i.e., across all the datasets and numbers of shots. Certainly, our surprising results do not dismiss the potential of prompt-learning and adapter-based research. However, we believe that our strong baseline could be used to evaluate progress in these emergent subjects in few-shot VLMs.

关键词： adapter few-shot LoRA PEFT prompt vision-Language

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively;and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

关键词： Multi-modal learning

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