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Neural Video Compression with Diverse Contexts

Neural Video Compression with Diverse Contexts

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Jiahao Li, Bin Lu, Yan Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://***/microsoft/DCVC.

关键词： Low-level vision

来源：评论

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Learning to Detect and Segment for Open Vocabulary Object Detection

Learning to Detect and Segment for Open Vocabulary Object De...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Tao Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9798350301298

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

关键词： detection recognition: Categorization retrieval

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are finetuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to finetune the ViT-B variants of VFMs with a 16 x 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page(1).

关键词： benchmark semantic segmentation vision foundation models

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Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

Adaptive Spot-Guided Transformer for Consistent Local Featur...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Jiahuan Chang, Jiahao He, Jianfeng Zhang, Tianzhu Yu, Jiyang Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Deep Space Explorat Lab Nanjing Jiangsu Peoples R China China Acad Space Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://***.

关键词： Low-level vision

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POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Fan, Zhiwen Pan, Panwang Wang, Peihao Jiang, Yifan Xu, Dejia Wang, Zhangyang ByteDance Beijing Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied agents and downstream 3D vision tasks. These limitations mainly come from the necessity of 3D models, closed-category detection, and a large number of densely annotated support views. To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE). The proposed approach POPE enables zero-shot 6DoF object pose estimation for any target object in any scene, while only a single reference is adopted as the support view. To achieve this, POPE lever-ages the power of the pre-trained large-scale 2D foundation model, employs a framework with hierarchical feature representation and 3D geometry principles. Moreover, it estimates the relative camera pose between object prompts and the target object in new views, enabling both two-view and multi-view 6DoF pose estimation tasks. Comprehensive experimental results demonstrate that POPE exhibits unrivaled robust performance in zero-shot settings, by achieving a significant reduction in the averaged Median Pose Error by 52.38% and 50.47% on the LINEMOD [22] and OnePose [54] datasets, respectively. We also conduct more challenging testings in causally captured images (see Figure 1), which further demonstrates the robustness of POPE.

关键词： Object recognition

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TOPLight: Lightweight Neural Networks with Task-Oriented Pretraining for Visible-Infrared recognition

TOPLight: Lightweight Neural Networks with Task-Oriented Pre...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Hao Cheng, Xu Peng, Wei Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing Peoples R China Stanford Univ Dept Psychiat & Behav Sci Stanford CA USA

ISBN: (纸本)9798350301298

Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images. Most existing works achieve promising results by transfer learning, such as pretraining on the ImageNet, based on advanced neural architectures like ResNet and ViT. However, such methods ignore the negative influence of the pretrained colour prior knowledge, as well as their heavy computational burden makes them hard to deploy in actual scenarios with limited resources. In this paper, we propose a novel task-oriented pretrained lightweight neural network (TOPLight) for VI recognition. Specifically, the TOPLight method simulates the domain conflict and sample variations with the proposed fake domain loss in the pretraining stage, which guides the network to learn how to handle those difficulties, such that a more general modality-shared feature representation is learned for the heterogeneous images. Moreover, an effective fine-grained dependency reconstruction module (FDR) is developed to discover substantial pattern dependencies shared in two modalities. Extensive experiments on VI person reidentification and VI face recognition datasets demonstrate the superiority of the proposed TOPLight, which significantly outperforms the current state of the arts while demanding fewer computational resources.

关键词： body gesture Humans: Face movement pose

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Explaining Image Classifiers with Multiscale Directional Image Representation

Explaining Image Classifiers with Multiscale Directional Ima...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kolek, Stefan Windesheim, Robert Andrade-Loarca, Hector Kutyniok, Gitta Levie, Ron Ludwig Maximilians Univ Munchen Dept Math Munich Germany Univ Tromso Dept Phys & Technol Tromso Norway Technion Israel Inst Technol Dept Math Haifa Israel

ISBN: (纸本)9798350301298

Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shear-let transform - a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constraints that protect against undesirable fine-grained explanation artifacts. However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier. ShearletX solves this problem by avoiding smoothness regularization all together, replacing it by shearlet sparsity constraints. The resulting explanations consist of a few edges, textures, and smooth parts of the original image, that are the most relevant for the decision of the classifier. To support our method, we propose a mathematical definition for explanation artifacts and an information theoretic score to evaluate the quality of mask explanations. We demonstrate the superiority of ShearletX over previous mask based explanation methods using these new metrics, and present exemplary situations where separating fine-detail patterns allows explaining phenomena that were not explainable before.

关键词： Explainable computer vision

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Wenjin Li, Keyi Yang, Sen Yuan, Sifan Marsic, Ivan Sippel, Genevieve J. Kim, Mary S. Burd, Randall S. Rutgers State Univ New Brunswick NJ 08901 USA Waymo Mountain View CA USA Childrens Natl Hosp Washington DC USA

ISBN: (纸本)9798350365474

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： medical activity recognition self-supervised learning video understanding

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Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization

Learning to Exploit the Sequence-Specific Prior Knowledge fo...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Qin, Haina Han, Longfei Xiong, Weihua Wang, Juan Ma, Wentao Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China Zeku Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks.

关键词： Low-level vision

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Learning Steerable Function for Efficient Image Resampling

Learning Steerable Function for Efficient Image Resampling

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Jiacheng Chen, Chang Huang, Wei Lang, Zhiqiang Song, Fenglong Yan, Youliang Xiong, Zhiwei Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x2 upsampling on Manga109.

关键词： Low-level vision

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