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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

引用

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

来源：评论

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

引用

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

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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Proceedings - 2013 ieee conference on computer vision and pattern recognition workshops, CVPRW 2013

Proceedings - 2013 IEEE Conference on Computer Vision and Pa...

引用

2013 ieee conference on computer vision and pattern recognition workshops, CVPRW 2013

ISBN: (纸本)9780769549903

The proceedings contain 156 papers. The topics discussed include: real-time mobile food recognition system;style finder: fine-grained clothing style detection and retrieval;stereo camera tracking for mobile devices;towards auto-calibration of smart phones using orientation sensors;detection of moving objects with non-stationary cameras in 5.8ms: bringing motion detection to your mobile device;mobile video capture of multi-page documents;collision detection for visually impaired from a body-mounted camera;video demo: an egocentric vision based assistive co-robot;mobile exergames - burn calories while playing games on a smartphone;a mobile vision system for fast and accurate ellipse detection;stabilization of magnified videos on a mobile device for visually impaired;and an augmented linear discriminant analysis approach for identifying identical twins with the aid of facial asymmetry features.

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Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension

Extend Your Own Correspondences: Unsupervised Distant Point ...

引用

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

作者： Liu, Quan Zhul, Hongzi Wang, Zhenxi Zhou, Yunsong Chang, Shan Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China Donghua Univ Shanghai Peoples R China

ISBN: (纸本)9798350353006

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self- supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly, to enable high-fidelity correspondence label generation, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

关键词： 3D vision Autonomous Driving Point Cloud Registration Unsupervised Learning

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BEHAVIOR vision Suite: Customizable Dataset Generation via Simulation

BEHAVIOR Vision Suite: Customizable Dataset Generation via S...

引用

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

作者： Ge, Yunhao Tang, Yihe Xu, Jiashu Gokmen, Cem Li, Chengshu Ai, Wensi Martinez, Benjamin Jose Aydin, Arman Anvari, Mona Chakravarthy, Ayush K. Yu, Hong-Xing Wong, Josiah Srivastava, Sanjana Lee, Sharon Zhang, Shengxin Itti, Laurent Li, Yunzhu Martin-Martins, Roberto Liu, Miao Zhang, Pengchuan Zhang, Ruohan Fei-Fei, Li Wu, Jiajun Stanford Univ Stanford CA 94305 USA Univ Southern Calif Los Angeles CA 90007 USA Harvard Univ Cambridge MA 02138 USA Meta GenAI Menlo Pk CA USA Meta FAIR Menlo Pk CA USA Univ Texas Austin Austin TX USA Univ Illinois Urbana IL USA

ISBN: (纸本)9798350353006

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as "filled" and "folded"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://***/

关键词： benchmarking embodied AI simulation synthetic data generation

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COTR: Compact Occupancy TRansformer for vision-based 3D Occupancy Prediction

COTR: Compact Occupancy TRansformer for Vision-based 3D Occu...

引用

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

作者： Ma, Qihang Tan, Xin Qu, Yanyun Ma, Lizhuang Zhang, Zhizhong Xie, Yuan East China Normal Univ Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing Peoples R China Xiamen Univ Fujian Peoples R China

ISBN: (纸本)9798350353006

The autonomous driving community has shown significant interest in 3D occupancy prediction, driven by its exceptional geometric perception and general object recognition capabilities. To achieve this, current works try to construct a Tri-Perspective View (TPV) or Occupancy (OCC) representation extending from the Bird-Eye-View perception. However, compressed views like TPV representation lose 3D geometry information while raw and sparse OCC representation requires heavy but redundant computational costs. To address the above limitations, we propose Compact Occupancy TRansformer (COTR), with a geometry-aware occupancy encoder and a semantic-aware group decoder to reconstruct a compact 3D OCC representation. The occupancy encoder first generates a compact geometrical OCC feature through efficient explicit-implicit view transformation. Then, the occupancy decoder further enhances the semantic discriminability of the compact OCC representation by a coarse-to-fine semantic grouping strategy. Empirical experiments show that there are evident performance gains across multiple baselines, e.g., COTR outperforms baselines with a relative improvement of 8%-15%, demonstrating the superiority of our method. The code is available at https://***/NotACracker/COTR.

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Constellations: A novel dataset for studying iterative inference in humans and AI

Constellations: A novel dataset for studying iterative infer...

引用

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

作者： Khajuria, Tarun Tulver, Kadi Luik, Taavi Aru, Jaan Univ Tartu Inst Comp Sci Tartu Estonia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under complex viewing conditions, human perception relies on generating hypotheses and revising them in an iterative fashion. We developed novel visual stimuli to study such iterative inference in humans and AI. In these stimuli, called "constellations", all local information about the object has been removed and it can only be recognized when taking into account the global pattern. We here describe the dataset and demonstrate that humans indeed use an iterative process of generating hypotheses and refining them to solve these images. We also provide code that allows researchers to create their own constellation images. The constellation dataset allows researchers to develop sketching algorithms for guessing the hidden object. As such algorithms used by humans appear to be iterative in nature, this dataset will facilitate the study of iterative inference in minds and machines.

关键词： Visualization Image recognition Machine vision conferences Refining Iterative algorithms Inference algorithms

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LAMP: Learn A Motion pattern for Few-Shot Video Generation

LAMP: Learn A Motion Pattern for Few-Shot Video Generation

引用

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

作者： Wu, Ruiqi Chen, Liangyu Yang, Tong Guo, Chunle Li, Chongyi Zhang, Xiangyu Nankai Univ CS VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China MEGVII Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we present a few-shot text-to-video framework, LAMP, which enables a text-to-image diffusion model to Learn A specific Motion pattern with 8 16 videos on a single GPU. Unlike existing methods, which require a large number of training resources or learn motions that are precisely aligned with template videos, it achieves a trade-off between the degree of generation freedom and the resource costs for model training. Specifically, we design a motion-content decoupled pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pre-trained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos without computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rqwu. ***/projects/LAMP.

关键词： Image coding

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Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval

Do You Remember? Dense Video Captioning with Cross-Modal Mem...

引用

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

作者： Kim, Minkuk Kim, Hyeon Bae Moon, Jinyoung Choi, Jinwoo Kim, Seong Tae Kyung Hee Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9798350353006

There has been significant attention to the research on dense video captioning, which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However, addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study, we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features, the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model with-out extensive pretraining from a large video dataset. Our code is available at https://***/ailab-kyunghee/CM2_DVC.

关键词： Dense Video Captioning Launguage Reasoning Retrieval Agumented Generation Video Captioning vision

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Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language

Separating the "Chirp" from the "Chat": Self-supervised Visu...

引用

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

作者： Hamilton, Mark Zisserman, Andrew Hershey, John R. Freeman, William T. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Microsoft Redmond WA 98052 USA Google Oxford England

ISBN: (纸本)9798350353006

We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn "global" audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://***/denseav

关键词： audio audio-visual contrastive learning language multimodal object discovery semantic segmentation sound unsupervised learning vision visual grounding

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