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No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

No Time to Train: Empowering Non-Parametric Networks for Few...

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

作者： Zhu, Xiangyang Zhang, Renrui He, Bowei Guo, Ziyu Liu, Jiaming Xiao, Han Fu, Chaoyou Dong, Hao Gao, Peng City Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Peking Univ Beijing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'unseen' classes. However, the prior pre-training stage not only introduces excessive time over-head but also incurs a significant domain gap on 'unseen' classes. To tackle these issues, we propose a Non-parametric Network for few-shot 3D Segmentation, SegNN, and its Parametric variant, Seg-PN. Without training, Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parametric models. Due to the elimination of pre-training, SegNN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN, Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module, which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively, while reducing training time by -90%, indicating its effectiveness and efficiency. Code is available here.

关键词： 3D Segmentation 3D vision Few-shot Learning

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ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

ChAda-ViT : Channel Adaptive Attention for Joint Representat...

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

作者： Bourriez, Nicolas Bendidi, Ihab Cohen, Ethan Watkinson, Gabriel Sanchez, Maxime Bollot, Guillaume Genovesio, Auguste Ecole Normale Super PSL Paris France Minos Biosci Paris France Synsight Eviy France

ISBN: (纸本)9798350353006

Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Importantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-Cell100k, a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per experiment. Our architecture, trained in a self-supervised manner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with different microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. [GRAPHICS] .

关键词： Channel Adaptive Channel Embeddings Computational Biology computer vision Deep Learning Inter-Channel Attention Long Context Microscopy Imaging Self Supervised Learning Transformers vision Transformers

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LEOD: Label-Efficient Object Detection for Event Cameras

LEOD: Label-Efficient Object Detection for Event Cameras

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

作者： Wu, Ziyi Gehrig, Mathias Lyu, Qing Liu, Xudong Gilitschenski, Igor Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada Univ Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost, we present LEOD, the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then, the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events, we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise, we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example, on Gen1, it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available, reaching new state-of-the-art results. Finally, we show that our method readily scales to improve larger detectors as well. Code: https://***/Wuziyi616/LEOD.

关键词： Event-based vision Object Detection Semi-supervised Learning Weakly-supervised Learning

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Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

Edge-enhanced Feature Distillation Network for Efficient Sup...

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

作者： Wang, Yan Nankai Univ Nankai Baidu Joint Lab Tianjin Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://***/icandle/EFDN.

关键词： Training computer vision Convolution Image edge detection conferences Superresolution Neural networks

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OpenEQA: Embodied Question Answering in the Era of Foundation Models

OpenEQA: Embodied Question Answering in the Era of Foundatio...

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

作者： Majumdar, Arjun Ajay, Anurag Zhang, Xi Aohan Punya, Pranav Yenamandra, Sriram Henaff, Mikael Silwal, Sneha Mcvay, Paul Maksymets, Oleksandr Arnaud, Sergio Yadav, Karmesh Li, Qiyang Newman, Ben Sharma, Mohit Berges, Vincent Zhang, Shiqi Agrawal, Pulkit Bisk, Yonatan Batra, Dhruv Kalakrishnan, Mrinal Meier, Franziska Paxton, Chris Sax, Alexander Rajeswaran, Aravind Georgia Tech Atlanta GA 30332 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA SUNY Binghamton Binghamton NY USA Meta AI Menlo Pk CA USA Univ Calif Berkeley Berkeley CA USA CMU Pittsburgh PA USA Meta Fundamental AI Res FAIR Menlo Pk CA USA

ISBN: (纸本)9798350353006

We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory, exemplified by agents on smart glasses, or by actively exploring the environment, as in the case of mobile robots. We accompany our formulation with OpenEQA - the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset, we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol, we evaluate several state-of-the-art foundation models including GPT-4V, and find that they significantly lag behind human-level performance. Consequently, OpenEQA stands out as a straightforward, measurable, and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI, conversational agents, and world models.

关键词： Embodied AI Embodied Question Answering vision-Language Models

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Making Visual Sense of Oracle Bones for You and Me

Making Visual Sense of Oracle Bones for You and Me

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

作者： Qiao, Runqi Yang, Lan Pang, Kaiyue Zhang, Honggang Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Surrey CVSSP SketchX Guildford Surrey England

ISBN: (纸本)9798350353006

Visual perception evolves over time. This is particularly the case of oracle bone scripts, where visual glyphs seem intuitive to people from distant past prove difficult to be understood in contemporary eyes. While semantic correspondence of an oracle can be found via a dictionary lookup, this proves to be not enough for public viewers to connect the dots, i.e., why does this oracle mean that? Common solution relies on a laborious curation process to collect visual guide for each oracle (Fig. 1), which hinges on the case-by-case effort and taste of curators. This paper delves into one natural follow-up question: can AI take over? Begin with a comprehensive human study, we show participants could indeed make better sense of an oracle glyph subjected to a proper visual guide and its efficacy can be approximated via a novel metric termed TransOV (Transferable Oracle Visuals). We then define a new conditional visual generation task based on an oracle glyph and its semantic meaning and importantly approach it by circumventing any form of model training in the presence of fatal lack of oracle data. At its heart is to leverage foundation model like GPT-4V to reason about the visual cues hidden inside an oracle and take advantage of an existing text-to-image model for final visual guide generation. Extensive empirical evidence shows our AI-enabled visual guides achieve significantly comparable TransOV performance compared with those collected under manual efforts. Finally, we demonstrate the versatility of our system under a more complex setting, where it is required to work alongside with an AI image denoiser to cope with raw oracle scan image inputs (cf. processed clean oracle glyphs). Code is available at https://***/RQ-Lab/OBS-Visual.

关键词： vision

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GLaMM: Pixel Grounding Large Multimodal Model

GLaMM: Pixel Grounding Large Multimodal Model

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

作者： Rasheed, Hanoona Maaz, Muhammad Shaji, Sahal Shaker, Abdelrahman Khan, Salman Cholakkal, Hisham Anwer, Rao M. Xing, Eric Yang, Ming-Hsuan Khan, Fahad S. Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Aalto Univ Espoo Finland Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Merced Merced CA USA Linkoping Univ Linkoping Sweden Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recently, region-level LMMs have been used to generate visually grounded responses. However, they are limited to only referring to a single object cate-gory at a time, require users to specify the regions, or can-not offer dense pixel-wise object grounding. In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks. GLaMM not only grounds objects appearing in the conversations but is flexible enough to accept both textual and optional visual prompts (region of interest) as input. This empowers users to interact with the model at various levels of granularity, both in textual and visual domains. Due to the lack of standard benchmarks for the novel setting of visually Grounded Conversation Generation (GCG), we introduce a comprehensive evaluation protocol with our cu-rated grounded conversations. Our proposed GCG task requires densely grounded concepts in natural scenes at a large-scale. To this end, we propose a densely annotated Grounding-anything Dataset (GranD) using our proposed automated annotation pipeline that encompasses 7.5M unique concepts grounded in a total of 810M regions available with segmentation masks. Besides GCG, GLaMM also performs effectively on several downstream tasks, e. g., referring expression segmentation, image and region-level captioning and vision-language conversations.

关键词： automated dataset annotation LMM MLMM multimodal foundation models Multimodal LMM vision and language vision-language VLM

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Three Pillars improving vision Foundation Model Distillation for Lidar

Three Pillars improving Vision Foundation Model Distillation...

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

作者： Puy, Gilles Gidaris, Spyros Boulch, Alexandre Simeoni, Oriane Sautier, Corentin Perez, Patrick Bursucl, Andrei Marlet, Renaud Valeo ai Paris France Kyutai Paris France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France

ISBN: (纸本)9798350353006

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbone, and the pretraining 2D+3D dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations. The code is available at https://***/valeoai/ScaLR.

关键词： Semantic Segmentation

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Multi-Camera Vehicle Tracking System for AI City Challenge 2022

Multi-Camera Vehicle Tracking System for AI City Challenge 2...

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

作者： Li, Fei Wang, Zhen Nie, Ding Zhang, Shiyi Jiang, Xingqun Zhao, Xingxing Hu, Peng BOE Technol Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-Target Multi-Camera tracking is a fundamental task for intelligent traffic systems. The track 1 of AI City Challenge 2022 aims at the city-scale multi-camera vehicle tracking task. In this paper we propose an accurate vehicle tracking system composed of 4 parts, including: (1) State-of-the-art detection and re-identification models for vehicle detection and feature extraction. (2) Single camera tracking, where we introduce augmented tracks prediction and multi-level association method on top of tracking-by-detection paradigm.(3) Zone-based singe-camera track-let merging strategy. (4) Multi-camera spatial-temporal matching and clustering strategy. The proposed system achieves promising results and ranks the second place in Track 1 of the AI City Challenge 2022 with a IDF1 score of 0.8437.

关键词： computer vision Vehicle detection conferences Urban areas Merging Predictive models Feature extraction

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Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval

Multi-view Multi-label Canonical Correlation Analysis for Cr...

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

作者： Sanghavi, Rushil Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a generalization of CCA for multi-view data that also makes use of high-level semantic information available in the form of multi-label annotations in each view. While CCA relies on explicit pairings/associations of samples between two views (or modalities), MVMLCCA uses the available multi-label annotations to establish correspondence across multiple (two or more) views without the need of explicit pairing of multi-view samples. Extensive experiments on two multi-modal datasets demonstrate that the proposed approach offers much more flexibility than the related approaches without compromising on scalability and cross-modal retrieval performance. Our code and precomputed features are available at https://***/Rushil231100/MVMLCCA.

关键词： computer vision Correlation Codes Annotations Scalability conferences Semantics

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