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VarifocalNet: An IoU-aware Dense Object Detector

VarifocalNet: An IoU-aware Dense Object Detector

引用

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

作者： Zhang, Haoyang Wang, Ying Dayoub, Feras Sunderhauf, Niko Queensland Univ Technol Australian Ctr Robot Vis Brisbane Qld Australia Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9781665445092

Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by similar to 2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors.

关键词： Location awareness Training computer vision Codes Detectors computer architecture Benchmark testing

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Stability-driven Contact Reconstruction From Monocular Color Images

Stability-driven Contact Reconstruction From Monocular Color...

引用

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

作者： Zhao, Zimeng Zuo, Binghui Xie, Wei Wang, Yangang Southeast Univ Nanjing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Physical contact provides additional constraints for hand-object state reconstruction as well as a basis for further understanding of interaction affordances. Estimating these severely occluded regions from monocular images presents a considerable challenge. Existing methods optimize the hand-object contact driven by distance threshold or prior from contact-labeled datasets. However, due to the number of subjects and objects involved in these indoor datasets being limited, the learned contact patterns could not be generalized easily. Our key idea is to reconstruct the contact pattern directly from monocular images, and then utilize the physical stability criterion in the simulation to optimize it. This criterion is defined by the resultant forces and contact distribution computed by the physics engine. Compared to existing solutions, our framework can be adapted to more personalized hands and diverse object shapes. Furthermore, an interaction dataset with extra physical attributes is created to verify the sim-to-real consistency of our methods. Through comprehensive evaluations, hand-object contact can be reconstructed with both accuracy and stability by the proposed framework.

关键词： Three-dimensional displays Shape Stability criteria Pipelines pattern recognition Image reconstruction Physics

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HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features

HoHoNet: 360 Indoor Holistic Understanding with Latent Horiz...

引用

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

作者： Sun, Cheng Sun, Min Chen, Hwann-Tzong Natl Tsing Hua Univ Hsinchu Taiwan ASUS AICS Dept Singapore Singapore Joint Res Ctr AI Technol & All Vista Healthcare Taipei Taiwan Aeolus Robot San Francisco CA USA

ISBN: (纸本)9781665445092

We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHoNet advances in two important aspects. First, the deep architecture is redesigned to run faster with improved accuracy. Second, we propose a novel horizon-to-dense module, which relaxes the per-column output shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoNet is fast: It runs at 52 FPS and 110 FPS with ResNet-50 and ResNet-34 backbones respectively, for modeling dense modalities from a high-resolution 512 x 1024 panorama. HoHoNet is also accurate. On the tasks of layout estimation and semantic segmentation, HoHoNet achieves results on par with current state-of-the-art. On dense depth estimation, HoHoNet outperforms all the prior arts by a large margin.

关键词： Image segmentation computer vision Shape Layout Semantics Estimation Deep architecture

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SynSin: End-to-end View Synthesis from a Single Image

SynSin: End-to-end View Synthesis from a Single Image

引用

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

作者： Wiles, Olivia Gkioxari, Georgia Szeliski, Richard Johnson, Justin Univ Oxford Oxford England Facebook AI Res Berkeley CA USA Facebook Menlo Pk CA USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728171685

View synthesis allows for the generation of new views of a scene given one or more images. This is challenging;it requires comprehensively understanding the 3D scene from images. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task using a single image at test time;it is trained on real images without any ground-truth 3D information. To this end, we introduce a novel differentiable point cloud renderer that is used to transform a latent 3D point cloud of features into the target view. The projected features are decoded by our refinement network to inpaint missing regions and generate a realistic output image. The 3D component inside of our generative model allows for interpretable manipulation of the latent feature space at test time, e.g. we can animate trajectories from a single image. Additionally, we can generate high resolution images and generalise to other input resolutions. We outperform baselines and prior work on the Matterport, Replica, and RealEstate10K datasets.

关键词： computer vision

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Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation

Improving Weakly Supervised Visual Grounding by Contrastive ...

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

作者： Wang, Liwei Huang, Jing Li, Yin Xu, Kun Yang, Zhengyuan Yu, Dong Chinese Univ Hong Kong Hong Kong Peoples R China Univ Illinois Champaign IL USA Univ Wisconsin Madison Madison WI USA Tencent AI Lab Bellevue WA USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a contrastive learning framework that accounts for both region-phrase and image-sentence matching. Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed. Importantly, our region-phrase score function is learned by distilling from soft matching scores between the detected object names and candidate phrases within an image-sentence pair, while the image-sentence score function is supervised by ground-truth image-sentence pairs. The design of such score functions removes the need of object detection at test time, thereby significantly reducing the inference cost. Without bells and whistles, our approach achieves state-of-the-art results on visual phrase grounding, surpassing previous methods that require expensive object detectors at test time.

关键词： Training Visualization Technological innovation computer vision Costs Grounding Detectors

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3D CNNs with Adaptive Temporal Feature Resolutions

3D CNNs with Adaptive Temporal Feature Resolutions

引用

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

作者： Fayyaz, Mohsen Bahrami, Emad Diba, Ali Noroozi, Mehdi Adeli, Ehsan Van Gool, Luc Gall, Juergen Univ Bonn Bonn Germany Katholieke Univ Leuven Leuven Belgium Bosch Ctr Artificial Intelligence Renningen Baden Wurttembe Germany Stanford Univ Stanford CA 94305 USA Swiss Fed Inst Technol Zurich Switzerland Univ Bonn Comp Vis Grp Bonn Germany

ISBN: (纸本)9781665445092

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the stateof-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCFI01, and HMDB5I.

关键词： computer vision Three-dimensional displays Costs Adaptive systems computer architecture Computational efficiency pattern recognition

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RegionGPT: Towards Region Understanding vision Language Model

RegionGPT: Towards Region Understanding Vision Language Mode...

引用

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

作者： Guo, Qiushan De Mello, Shalini Yin, Hongxu Byeon, Wonmin Cheung, Ka Chun Yu, Yizhou Luo, Ping Liu, Sifei Univ Hong Kong Hong Kong Peoples R China NVIDIA San Francisco CA USA

ISBN: (纸本)9798350353006

vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions. To address this, we introduce RegionGPT (short as RGPT), a novel framework designed for complex region-level captioning and understanding. RGPT enhances the spatial awareness of regional representation with simple yet effective modifications to existing visual encoders in VLMs. We further improve performance on tasks requiring a specific output scope by integrating task-guided instruction prompts during both training and inference phases, while maintaining the model's versatility for general-purpose tasks. Additionally, we develop an automated region caption data generation pipeline, enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks, including but not limited to complex region descriptions, reasoning, object classification, and referring expressions comprehension. Code will be released at the project page.

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Representation Compensation Networks for Continual Semantic Segmentation

Representation Compensation Networks for Continual Semantic ...

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

作者： Zhang, Chang-Bin Xiao, Jia-Wen Liu, Xialei Chen, Ying-Cong Cheng, Ming-Ming Nankai Univ CS TMCC Tianjin Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting. We propose to use a structural re-parameterization mechanism, named representation compensation (RC) module, to decouple the representation learning of both old and new knowledge. The RC module consists of two dynamically evolved branches with one frozen and one trainable. Besides, we design a pooled cube knowledge distillation strategy on both spatial and channel dimensions to further enhance the plasticity and stability of the model. We conduct experiments on two challenging continual semantic segmentation scenarios, continual class segmentation and continual domain segmentation. Without any extra computational overhead and parameters during inference, our method outperforms state-of-the-art performance. The code is available at https : // ***/zhangchbin/RCIL..

关键词： Representation learning Deep learning computer vision Codes Computational modeling Semantics Neural networks

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On the Robustness of Language Guidance for Low-Level vision Tasks: Findings from Depth Estimation

On the Robustness of Language Guidance for Low-Level Vision ...

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

作者： Chatterjee, Agneet Gokhale, Tejas Baral, Chitta Yang, Yezhou Arizona State Univ Tempe AZ 85281 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9798350353013;9798350353006

Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the language prior, particularly in terms of generalization and robustness, remains unexplored. In this paper, we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric, three-dimensional spatial relationships, incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data, these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally, to provide a foundation for future research, we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation, our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings. (1)

关键词： Low-level vision robustness vision and language

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ViPLO: vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

ViPLO: Vision Transformer based Pose-Conditioned Self-Loop G...

引用

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

作者： Park, Jeeseung Park, Jin-Woo Lee, Jong-Seok MAy I Inc Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset.

关键词： detection recognition: Categorization retrieval

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