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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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MobileOne: An Improved One millisecond Mobile Backbone

MobileOne: An Improved One millisecond Mobile Backbone

引用

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

作者： Vasu, Pavan Kumar Anasosalu Gabriel, James Zhu, Jeff Tuzel, Oncel Ranjan, Anurag Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350301298

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. Code and models are available at https://***/apple/ml-mobileone

关键词： Efficient and scalable vision

来源：评论

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Enhanced Training of Query-Based Object Detection via Selective Query Recollection

Enhanced Training of Query-Based Object Detection via Select...

引用

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

作者： Chen, Fangyi Zhang, Han Hu, Kai Huang, Yu-Kai Zhu, Chenchen Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI San Francisco CA USA

ISBN: (纸本)9798350301298

This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4 similar to 2.8 AP improvement. Code is available at https://***/Fangyi-Chen/SQR

关键词： detection recognition: Categorization retrieval

来源：评论

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Learning to Generate Image Embeddings with User-level Differential Privacy

Learning to Generate Image Embeddings with User-level Differ...

引用

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

作者： Xu, Zheng Collins, Maxwell Wang, Yuxiao Panait, Liviu Oh, Sewoong Augenstein, Sean Liu, Ting Schroff, Florian McMahan, H. Brendan Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of epsilon < 2 while controlling the utility drop within 5%, when millions of users can participate in training

关键词： accountability ethics in vision fairness privacy Transparency

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FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures

FFF: Fragment-Guided Flexible Fitting for Building Complete ...

引用

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

作者： Chen, Weijie Wang, Xinyan Wang, Yuhang DP Technol Ltd Camarillo CA 93012 USA Peking Univ Ctr Data Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.

关键词： detection recognition: Categorization retrieval

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Diversity-Measurable Anomaly Detection

Diversity-Measurable Anomaly Detection

引用

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

作者： Liu, Wenrui Chang, Hong Ma, Bingpeng Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

关键词： detection recognition: Categorization retrieval

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SViTT: Temporal Learning of Sparse Video-Text Transformers

SViTT: Temporal Learning of Sparse Video-Text Transformers

引用

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

作者： Li, Yi Min, Kyle Tripathi, Subarna Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9798350301298

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size;the curse of dimensionality for multi-frame modeling;and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://***/projects/svitt.

关键词： and reasoning language vision

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Generative Bias for Robust Visual Question Answering

Generative Bias for Robust Visual Question Answering

引用

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

作者： Cho, Jae Won Kim, Dong-Jin Ryu, Hyeonggon Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust target model. However, these methods compute the bias for a model simply from the label statistics of the training data or from single modal branches. In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. In particular, GenB employs a generative network to learn the bias in the target model through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT architecture on VQA-CP2.

关键词： language reasoning vision

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Context-Based Trit-Plane Coding for Progressive Image Compression

Context-Based Trit-Plane Coding for Progressive Image Compre...

引用

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

作者： Jeon, Seungmin Choi, Kwang Pyo Park, Youngo Kim, Chang-Su Korea Univ Seoul South Korea Samsung Elect Seoul South Korea

ISBN: (纸本)9798350301298

Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly, e.g. by -14.84% in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. The source codes are available at https://***/seungminjeon-github/CTC.

关键词： Low-level vision

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Feature Aggregated Queries for Transformer-based Video Object Detectors

Feature Aggregated Queries for Transformer-based Video Objec...

引用

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

作者： Cui, Yiming Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9798350301298

Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformer-based object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video object detection. However, those existing Transformer-based video object detectors still follow the same pipeline as those used for classical object detectors, like enhancing the object feature representations by aggregation. In this work, we take a different perspective on video object detection. In detail, we improve the qualities of queries for the Transformer-based models by aggregation. To achieve this goal, we first propose a vanilla query aggregation module that weighted averages the queries according to the features of the neighboring frames. Then, we extend the vanilla module to a more practical version, which generates and aggregates queries according to the features of the input frames. Extensive experimental results validate the effectiveness of our proposed methods: On the challenging ImageNet VID benchmark, when integrated with our proposed modules, the current state-of-the-art Transformer-based object detectors can be improved by more than 2.4% on mAP and 4.2% on AP(50). Code is available at https://***/YimingCuiCuiCui/FAQ.

关键词： detection recognition: Categorization retrieval

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Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

Efficient and Explicit Modelling of Image Hierarchies for Im...

引用

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

作者： Li, Yawei Fan, Yuchen Xiang, Xiaoyu Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Meta Real Labs Menlo Pk CA 33137 USA Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://***/ofsoundof/***.

关键词： Low-level vision

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