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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Real-time Object Detection for Streaming Perception

Real-time Object Detection for Streaming Perception

引用

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

作者： Yang, Jinrong Liu, Songtao Li, Zeming Li, Xiaoping Sun, Jian Huazhong Univ Sci & Technol Wuhan Peoples R China Megvii Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment after processing, streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem. We build a simple and effective framework for streaming perception. It equips a novel Dual-Flow Perception module (DFP), which includes dynamic and static flows to capture the moving trend and basic detection feature for streaming prediction. Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to generate adaptive weights for objects with different moving speeds. Our simple method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline, validating its effectiveness. Our code will be made available at https: //***/yancie- yjr/StreamYOLO.

关键词： Measurement Detectors Streaming media Market research Search problems Real-time systems pattern recognition

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Unlocking the Potential of Pre-trained vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors

Unlocking the Potential of Pre-trained Vision Transformers f...

引用

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

作者： Zhou, Ziqin Xu, Hai-Ming Shu, Yangyang Liu, Lingqiao Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350353013;9798350353006

The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper, we explore to harnesses this emergent feature to tackle few-shot semantic segmentation, a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery, we find that the concept of "relationship descriptors", initially conceived for enhancing the CLIP model for zero-shot semantic segmentation, offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation, extending its application across multiple layers to enhance performance. Building upon this adaptation, we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets, including PASCAL-5(i) and COCO-20(i), we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios, and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques, highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://***/ZiqinZhou66/***.

关键词： Semantic Segmentation

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Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions

Improved Zero-Shot Classification by Adapting VLMs with Text...

引用

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

作者： Saha, Oindrila Van Horn, Grant Maji, Subhransu Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798350353006

The zero-shot performance of existing vision-language models (VLMs) such as CLIP [29] is limited by the availability of large-scale, aligned image and text datasets in specific domains. In this work, we leverage two complementary sources of information-descriptions of categories generated by large language models (LLMs) and abundant, fine- grained image classification datasets-to improve the zero-shot classification performance of VLMs across finegrained domains. On the technical side, we develop methods to train VLMs with this "bag-level" image-text supervision. We find that simply using these attributes at test-time does not improve performance, but our training strategy, for example, on the iNaturalist [41] dataset, leads to an average improvement of 4-5% in zero-shot classification accuracy for novel categories of birds [42] and flowers [23]. Similar improvements are observed in domains where a subset of the categories was used to fine-tune the model. By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories. We systematically evaluate their ability to improve zero-shot cat-egorization in natural domains. Our findings suggest that geographic priors can be just as effective and are complementary to visual appearance. Our method also outperforms prior work on prompt-based tuning of VLMs. We release the benchmark, consisting of 14 datasets at https: //***/cvl-umass/AdaptCLIPZS, which will contribute to future research in zero-shot recognition.

关键词： Fine-grained Classification Large Language Model Multimodal Model vision Language Model Zero Shot Learning

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METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos

METAL: Minimum Effort Temporal Activity Localization in Untr...

引用

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

作者： Zhang, Da Dai, Xiyang Wang, Yuan-Fang Univ Calif Santa Barbara Santa Barbara CA 93106 USA Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781728171685

Existing Temporal Activity Localization (TAL) methods largely adopt strong supervision for model training which requires (1) vast amounts of untrimmed videos per each activity category and (2) accurate segment-level boundary annotations (start time and end time) for every instance. This poses a critical restriction to the current methods in practical scenarios where not only segment-level annotations are expensive to obtain but many activity categories are also rare and unobserved during training. Therefore, Can we learn a TAL model under weak supervision that can localize unseen activity classes? To address this scenario, we define a novel example-based TAL problem called Minimum Effort Temporal Activity Localization (METAL): Given only a few examples, the goal is to find the occurrences of semantically-related segments in an untrimmed video sequence while model training is only supervised by the video-level annotation. Towards this objective, we propose a novel Similarity Pyramid Network (SPN) that adopts the few-shot learning technique of Relation Network and directly encodes hierarchical multi-scale correlations, which we learn by optimizing two complimentary loss functions in an end-to-end manner. We evaluate the SPN on the THUMOS' 14 and ActivityNet datasets, of which we rearrange the videos to fit the METAL setup. Results show that our SPN achieves performance superior or competitive to state-of-the-art approaches with stronger supervision.

关键词： computer vision

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IIRC: Incremental Implicitly-Refined Classification

IIRC: Incremental Implicitly-Refined Classification

引用

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

作者： Abdelsalam, Mohamed Faramarzi, Mojtaba Sodhani, Shagun Chandar, Sarath Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Facebook AI Res Menlo Pk CA USA Ecole Polytech Montreal Montreal PQ Canada Canada CIFAR AI Chair Toronto ON Canada

ISBN: (纸本)9781665445092

We introduce the "Incremental Implicitly-Refined Classification (IIRC)" setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like "bear" and a low-level (fine) label like "polar bear". Only one label is provided at a time, and the model has to figure out the other label if it has already learned it. This setup is more aligned with real-life scenarios, where a learner usually interacts with the same family of entities multiple times, discovers more granularity about them, while still trying not to forget previous knowledge. Moreover, this setup enables evaluating models for some important lifelong learning challenges that cannot be easily addressed under the existing setups. These challenges can be motivated by the example "if a model was trained on the class bear in one task and on polar bear in another task, will it forget the concept of bear, will it rightfully infer that a polar bear is still a bear? and will it wrongfully associate the label of polar bear to other breeds of bear?". We develop a standardized benchmark that enables evaluating models on the IIRC setup. We evaluate several stateof-the-art lifelong learning algorithms and highlight their strengths and limitations. For example, distillation-based methods perform relatively well but are prone to incorrectly predicting too many labels per image. We hope that the proposed setup, along with the benchmark, would provide a meaningful problem setting to the practitioners.

关键词： computer vision Analytical models Computational modeling Benchmark testing Prediction algorithms pattern recognition Classification algorithms

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Structured Multi-Level Interaction Network for Video Moment Localization via Language Query

Structured Multi-Level Interaction Network for Video Moment ...

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

作者： Wang, Hao Zha, Zheng-Jun Li, Liang Liu, Dong Luo, Jiebo Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment construction for both cross-modal understanding and video content comprehension, which are the two crucial challenges for this task. In this paper, we disentangle the activity moment into boundary and content. Based on the explored moment structure, we propose a novel Structured Multi-level Interaction Network (SMIN) to tackle this problem through multi-levels of cross-modal interaction coupled with content-boundary-moment interaction. In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interacting the word-level query with content and boundary, as in a coarse-to-fine manner. For content-boundary-moment interaction, we capture the insightful relations between boundary, content, and the whole moment proposal. Through multi-level interactions, the model obtains robust cross-modal representation for accurate moment localization. Extensive experiments conducted on three benchmarks (i.e., Charades-STA, ActivityNet-Captions, and TACoS) demonstrate the proposed approach outperforms the state-of-the-art methods.

关键词： Location awareness computer vision Natural languages Benchmark testing pattern recognition Proposals Task analysis

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SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

SE-SSD: Self-Ensembling Single-Stage Object Detector From Po...

引用

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

作者： Zheng, Wu Tang, Weiliang Jiang, Li Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in which we design an effective IoU-based matching strategy to filter soft targets from the teacher and formulate a consistency loss to align student predictions with them. Also, to maximize the distilled knowledge for ensembling the teacher, we design a new augmentation scheme to produce shape-aware augmented samples to train the student, aiming to encourage it to infer complete object shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations. Our SE-SSD attains top performance compared with all prior published works. Also, it attains top precisions for car detection in the KITTI benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards1, respectively) with an ultra-high inference speed. The code is available at https://***/Vegeta2020/SE-SSD.

关键词： Training Matched filters computer vision Three-dimensional displays Shape Detectors Object detection

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Large Loss Matters in Weakly Supervised Multi-Label Classification

Large Loss Matters in Weakly Supervised Multi-Label Classifi...

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

作者： Kim, Youngwook Kim, Jae Myung Akata, Zeynep Lee, Jungwoo Seoul Natl Univ Seoul South Korea Univ Tubingen Tubingen Germany Max Planck Inst Intelligent Syst Stuttgart Germany Max Planck Inst Informat Saarbrucken Germany HodooAI Lab Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost. In this work, we first regard unobserved labels as negative labels, casting the WSML task into noisy multi-label classification. From this point of view, we empirically observe that memorization effect, which was first discovered in a noisy multi-class setting, also occurs in a multi-label setting. That is, the model first learns the representation of clean labels, and then starts memorizing noisy labels. Based on this finding, we propose novel methods for WSML which reject or correct the large loss samples to prevent model from memorizing the noisy label. Without heavy and complex components, our proposed methods outperform previous state-of-the-art WSML methods on several partial label settings including Pascal VOC 2012, MS COCO, NUSWIDE, CUB, and OpenImages V3 datasets. Various analysis also show that our methodology actually works well, validating that treating large loss properly matters in a weakly supervised multi-label classification.

关键词： Training computer vision Costs Codes Annotations Computational modeling pattern recognition

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Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Semi-Supervised 3D Hand-Object Poses Estimation with Interac...

引用

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

作者： Liu, Shaowei Jiang, Hanwen Xu, Jiarui Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning. We build a joint learning framework where we perform explicit contextual reasoning between hand and object representations. Going beyond limited 3D annotations in a single image, we leverage the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning. Our method not only improves hand pose estimation in challenging real-world dataset, but also substantially improve the object pose which has fewer ground-truths per instance. By training with large-scale diverse videos, our model also generalizes better across multiple out-of-domain datasets. Project page and code: https://***/Semi-Hand-Object.

关键词： Training computer vision Three-dimensional displays Annotations Filtering Pose estimation Semisupervised learning

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Topology Preserving Local Road Network Estimation from Single Onboard Camera Image

Topology Preserving Local Road Network Estimation from Singl...

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

作者： Can, Yigit Baran Liniger, Alexander Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furthermore, it needs to refer to the ground plane, where also the driving actions are taken. This paper aims at extracting the local road network topology, directly in the bird's-eye-view (BEV), all in a complex urban setting. The only input consists of a single onboard, forward looking camera image. We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points. To better capture topology, we introduce the concept of minimal cycles and their covers. A minimal cycle is the smallest cycle formed by the directed curve segments (between two intersections). The cover is a set of curves whose segments are involved in forming a minimal cycle. We first show that the covers suffice to uniquely represent the road topology. The covers are then used to supervise deep neural networks, along with the lane curve supervision. These learn to predict the road topology from a single input image. The results on the NuScenes and Argoverse benchmarks are significantly better than those obtained with baselines. Code: https://***/ybarancan/TopologicalLaneGraph.

关键词： Image segmentation computer vision Network topology Navigation Roads Neural networks Estimation

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