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共 22907 条记录，以下是461-470 订阅

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SelfOcc: Self-Supervised vision-Based 3D Occupancy Prediction

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Predictio...

引用

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

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Borui Zhou, Jie Lu, Jiwen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350353006

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://***/huang-yh/SelfOcc.

关键词： 3D reconstruction autonomous driving NeRF occupancy self-supervised

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

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

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization

Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential ...

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

作者： Singha, Mainak Jha, Ankit Bose, Shirsha Nair, Ashwin Abdar, Moloud Banerjee, Biplab Aisin Corp Kariya Aichi Japan Indian Inst Technol Bombay Maharashtra India Tech Univ Munich Munich Germany IISER Thiruvananthapuram Thiruvananthapuram Kerala India Deakin Univ Geelong Vic Australia

ISBN: (纸本)9798350353006

We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls, we introduce ODG-CLIP, harnessing the semantic prowess of the vision-language model, CLIP. Our framework brings forth three primary innovations: Firstly, distinct from prevailing paradigms, we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class. Secondly, aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity, we devise a novel visual style-centric prompt learning mechanism. Finally, we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains, especially for the shared classes. Through rigorous testing on diverse datasets, covering closed and open-set DG contexts, ODG-CLIP demonstrates clear supremacy, consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://***/mainaksingha01/ODG-CLIP.

关键词： open domain generalization vision-language models

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Taming Self-Training for Open-Vocabulary Object Detection

Taming Self-Training for Open-Vocabulary Object Detection

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

作者： Zhao, Shiyu Schulter, Samuel Zhao, Long Zhang, Zhixing Kumar, Vijay B. G. Suh, Yumin Chandraker, Manmohan Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD. This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs. To address these challenges, we propose SAS-Det that tames self-training for OVD from two key perspectives. First, we present a split-and-fusion (SAF) head that splits a standard detection into an open-branch and a closed-branch. This design can reduce noisy supervision from pseudo boxes. Moreover, the two branches learn complementary knowledge from different training data, significantly enhancing performance when fused together. Second, in our view, unlike in closed-set tasks, the PL distributions in OVD are solely determined by the teacher model. We introduce a periodic update strategy to decrease the number of updates to the teacher, thereby decreasing the frequency of changes in PL distributions, which stabilizes the training process. Extensive experiments demonstrate SAS-Det is both efficient and effective. SAS-Det outperforms recent models of the same scale by a clear margin and achieves 37.4 AP50 and 29.1 APr on novel categories of the COCO and LVIS benchmarks, respectively. Code is available at https://***/xiaofeng94/SAS-Det.

关键词： open-vocabulary object detection pseudo label self-training vision and language

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Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds ...

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

作者： Koch, Sebastian Vaskevicius, Narunas Colosi, Mirco Hermosilla, Pedro Ropinski, Timo Bosch Ctr Artificial Intelligence Stuttgart Germany Robert Bosch Corp Res Stuttgart Germany Univ Ulm Ulm Germany TU Vienna Vienna Austria

ISBN: (纸本)9798350353006

Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG, an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models. This enables us to predict 3D scene graphs from 3D point clouds in a zero-shot manner by querying object classes from an open vocabulary and predicting the inter-object relationships from a grounded LLM with scene graph features and queried object classes as context. Open3DSG is the first 3D point cloud method to predict not only explicit open-vocabulary object classes, but also open-set relationships that are not limited to a predefined label set, making it possible to express rare as well as specific objects and relationships in the predicted 3D scene graph. Our experiments show that Open3DSG is effective at predicting arbitrary object classes as well as their complex inter-object relationships describing spatial, supportive, semantic and comparative relationships.

关键词： 3d scene graphs 3d scene representation 3d scene understanding open-vocabulary open-world point cloud scene graph vision-language vlm

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Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery

Rethinking Transformers Pre-training for Multi-Spectral Sate...

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

作者： Noman, Mubashir Naseer, Muzammal Cholakkal, Hisham Anwar, Rao Muhammad Khan, Salman Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remote sensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remote sensing imagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multiscale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at https://***/techmn/satmae_pp.

关键词： multi-scale mask auto encoder multi-spectral imagery remote sensing scene classification self-supervised learning vision Transformers

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement

UVEB: A Large-scale Benchmark and Baseline Towards Real-Worl...

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

作者： Xie, Yaofeng Kong, Lingwei Chen, Kai Zheng, Ziqiang Yu, Xiao Yu, Zhibin Zheng, Bing Ocean Univ China Coll Elect Engn Qingdao Peoples R China Ocean Univ China Sanya Oceanog Inst Key Lab Ocean Observat & Informat Hainan Prov Qingdao Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Learning-based underwater image enhancement (UIE) methods have made great progress. However, the lack of large-scale and high-quality paired training samples has become the main bottleneck hindering the development of UIE. The inter-frame information in underwater videos can accelerate or optimize the UIE process. Thus, we constructed the first large-scale high-resolution underwater video enhancement benchmark (UVEB) to promote the development of underwater vision. It contains 1,308 pairs of video sequences and more than 453,000 high-resolution with 38% Ultra-High-Definition (UHD) 4K frame pairs. UVEB comes from multiple countries, containing various scenes and video degradation types to adapt to diverse and complex underwater environments. We also propose the first supervised underwater video enhancement method, UVE-Net. UVE-Net converts the current frame information into convolutional kernels and passes them to adjacent frames for efficient inter-frame information exchange. By fully utilizing the redundant degraded information of underwater videos, UVE-Net completes video enhancement better. Experiments show the effective network design and good performance of UVE-Net.

关键词： underwater video dataset underwater video enhancement underwater vision video processing

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Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Chat-UniVi: Unified Visual Representation Empowers Large Lan...

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

作者： Jin, Peng Takanobu, Ryuichi Zhang, Wancai Cao, Xiaochun Yuan, Li Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Ai Sci AI4S Preferred Program Shenzhen Grad Sch Shenzhen Peoples R China Nari Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Tech Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multi-modal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably, ChatUniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://***/PKU-YuanGroup/Chat-UniVi.

关键词： Image Understanding Large Language Models Video Understanding vision Language Model

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