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DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

DYNAFED: Tackling Client Data Heterogeneity with Global Dyna...

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

作者： Pi, Renjie Zhang, Weizhong Xie, Yueqi Gao, Jiahui Wang, Xiaoyu Kim, Sunghun Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Fudan Univ Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. Unfortunately, this reduces to regular training, which compromises clients' privacy and conflicts with the purpose of FL. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy. We unearth such knowledge from the dynamics of the global model's trajectory. Specifically, we first reserve a short trajectory of global model snapshots on the server. Then, we synthesize a small pseudo dataset such that the model trained on it mimics the dynamics of the reserved global model trajectory. Afterward, the synthesized data is used to help aggregate the deflected clients into the global model. We name our method DYNAFED, which enjoys the following advantages: 1) we do not rely on any external on-server dataset, which requires no additional cost for data collection;2) the pseudo data can be synthesized in early communication rounds, which enables DYNAFED to take effect early for boosting the convergence and stabilizing training;3) the pseudo data only needs to be synthesized once and can be directly utilized on the server to help aggregation in subsequent rounds. Experiments across extensive benchmarks are conducted to showcase the effectiveness of DYNAFED. We also provide insights and understanding of the underlying mechanism of our method.

关键词： accountability ethics in vision fairness privacy Transparency

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Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence

Auto-CARD: Efficient and Robust Codec Avatar Driving for Rea...

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

作者： Fu, Yonggan Li, Yuecheng Li, Chenghui Saragih, Jason Zhang, Peizhao Dai, Xiaoliang Lin, Yingyan (Celine) Georgia Inst Technol Atlanta GA 30332 USA Meta Pittsburgh PA USA

ISBN: (纸本)9798350301298

Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras with a quality level that can match the realism of the avatar's human appearance. To this end, we propose a framework called Auto-CARD, which for the first time enables real-time and robust driving of Codec Avatars when exclusively using merely on-device computing resources. This is achieved by minimizing two sources of redundancy. First, we develop a dedicated neural architecture search technique called AVE-NAS for avatar encoding in AR/VR, which explicitly boosts both the searched architectures' robustness in the presence of extreme facial expressions and hardware friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal redundancy in consecutively captured images during continuous rendering and develop a mechanism dubbed LATEX to skip the computation of redundant frames. Specifically, we first identify an opportunity from the linearity of the latent space derived by the avatar decoder and then propose to perform adaptive latent extrapolation for redundant frames. For evaluation, we demonstrate the efficacy of our Auto-CARD framework in real-time Codec Avatar driving settings, where we achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even better animation quality than state-of-the-art avatar encoder designs.

关键词： vision applications and systems

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Revisiting the Stack-Based Inverse Tone Mapping

Revisiting the Stack-Based Inverse Tone Mapping

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

作者： Zhang, Ning Ye, Yuyao Zhao, Yang Wang, Ronggang Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up and three exposure-down), which may introduce unnecessary computational cost or reconstruct incorrect results. On the other hand, they neglect the connections between the upexposure and down-exposure models and thus fail to fully excavate effective features. In this paper, we revisit the stack-based ITM approaches and propose a novel method to reconstruct HDR radiance from a single image, which only needs to estimate two exposure images. At first, we design the exposure adaptive block that can adaptively adjust the exposure based on the luminance distribution of the input image. Secondly, we devise the cross-model attention block to connect the exposure adjustment models. Thirdly, we propose an end-to-end ITM pipeline by incorporating the multi-exposure fusion model. Furthermore, we propose and open a multi-exposure dataset that indicates the optimal exposure-up/down levels. Experimental results show that the proposed method outperforms some state-of-the-art methods.

关键词： Low-level vision

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FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection

FrustumFormer: Adaptive Instance-aware Resampling for Multi-...

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

作者： Wang, Yuqi Chen, Yuntao Zhang, Zhaoxiang Chinese Acad Sci CASIA CRIPAC Inst Automat Beijing Peoples R China UCAS Sch Artificial Intelligence Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via 3D projection, treating all pixels or grids equally. However, choosing what to transform is also important but has rarely been discussed before. The pixels of a moving car are more informative than the pixels of the sky. To fully utilize the information contained in images, the view transformation should be able to adapt to different image regions according to their contents. In this paper, we propose a novel framework named FrustumFormer, which pays more attention to the features in instance regions via adaptive instance-aware resampling. Specifically, the model obtains instance frustums on the bird's eye view by leveraging image view object proposals. An adaptive occupancy mask within the instance frustum is learned to refine the instance location. Moreover, the temporal frustum intersection could further reduce the localization uncertainty of objects. Comprehensive experiments on the nuScenes dataset demonstrate the effectiveness of FrustumFormer, and we achieve a new state-ofthe-art performance on the benchmark. Codes and models will be made available at https://***/Robertwyq/Frustum.

关键词： detection recognition: Categorization retrieval

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ProD: Prompting-to-disentangle Domain Knowledge for Cross-domain Few-shot Image Classification

ProD: Prompting-to-disentangle Domain Knowledge for Cross-do...

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

作者： Ma, Tianyi Sun, Yifan Yang, Zongxin Yang, Yi Univ Technol Sydney Ultimo Australia Baidu Inc Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy. To mitigate the domain gap, we propose a prompting-to-disentangle (ProD) method through a novel exploration with the prompting mechanism. ProD adopts the popular multi-domain training scheme and extracts the backbone feature with a standard Convolutional Neural Network. Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature. Specifically, ProD concatenates a DG and a DS prompt to the backbone feature and feeds them into a lightweight transformer. The DG prompt is learnable and shared by all the training domains, while the DS prompt is generated from the domain-of-interest on the fly. As a result, the transformer outputs DG and DS features in parallel with the two prompts, yielding the disentangling effect. We show that: 1) Simply sharing a single DG prompt for all the training domains already improves generalization towards the novel test domain. 2) The cross-domain generalization can be further reinforced by making the DG prompt neutral towards the training domains. 3) When inference, the DS prompt is generated from the support samples and can capture test domain knowledge through the prompting mechanism. Combining all three benefits, ProD significantly improves cross-domain few-shot classification. For instance, on CUB, ProD improves the 5-way 5-shot accuracy from 73.56% (baseline) to 79.19%, setting a new state of the art.

关键词： detection recognition: Categorization retrieval

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Event-based Shape from Polarization

Event-based Shape from Polarization

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

作者： Muglikar, Manasi Bauersfeld, Leonard Moeys, Diederik Paul Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland SONY R&D Ctr Europe Adv Sensors & Modelling Grp SL1 Terre Haute IN USA

ISBN: (纸本)9798350301298

State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world datasets. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP.

关键词： Physics-based vision and shape-from-X

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CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not

CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, ...

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

作者： Sain, Aneeshan Bhunia, Ayan Kumar Chowdhury, Pinaki Nath Koley, Subhadeep Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford England

ISBN: (纸本)9798350301298

In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the category setting and the fine-grained setting ("all"). At the very core of our solution is a prompt learning setup. First we show just via factoring in sketch-specific prompts, we already have a category-level ZS-SBIR system that overshoots all prior arts, by a large margin (24.8%) - a great testimony on studying the CLIP and ZS-SBIR synergy. Moving onto the fine-grained setup is however trickier, and requires a deeper dive into this synergy. For that, we come up with two specific designs to tackle the fine-grained matching nature of the problem: (i) an additional regularisation loss to ensure the relative separation between sketches and photos is uniform across categories, which is not the case for the gold standard standalone triplet loss, and (ii) a clever patch shuffling technique to help establishing instance-level structural correspondences between sketch-photo pairs. With these designs, we again observe significant performance gains in the region of 26.9% over previous state-of-the-art. The take-home message, if any, is the proposed CLIP and prompt learning paradigm carries great promise in tackling other sketch-related tasks (not limited to ZS-SBIR) where data scarcity remains a great challenge. Project page: https:// ***/ Sketch LVM/

关键词： vision applications and systems

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Hierarchical Video-Moment Retrieval and Step-Captioning

Hierarchical Video-Moment Retrieval and Step-Captioning

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

作者： Zola, Abhay Cho, Jaemin Kottur, Satwik Chen, Xilun Oguz, Barlas Mehdad, Yashar Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA Meta Al Redmond WA USA

ISBN: (纸本)9798350301298

There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summaries. Such an end-to-end setup would allow for many interesting applications, e.g., a text-based search that finds a relevant video from a video corpus, extracts the most relevant moment from that video, and segments the moment into important steps with captions. To address this, we present the HIREST (HIerarchical REtrieval and STep-captioning) dataset and propose a new benchmark that covers hierarchical information retrieval and visual/textual stepwise summarization from an instructional video corpus. HIREST consists of 3.4K text-video pairs from an instructional video dataset, where 1.1K videos have annotations of moment spans relevant to text query and breakdown of each moment into key instruction steps with caption and timestamps (totaling 8.6K step captions). Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks. In moment segmentation, models break down a video moment into instruction steps and identify start-end boundaries. In step captioning, models generate a textual summary for each step. We also present starting point task-specific and end-to-end joint baseline models for our new benchmark. While the baseline models show some promising results, there still exists large room for future improvement by the community.(1)

关键词： and reasoning language vision

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S³C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S<SUP>3</SUP>C: Semi-Supervised VQA Natural Language Explana...

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

作者： Suo, Wei Sun, Mengyang Liu, Weisong Gao, Yiqi Wang, Peng Zhang, Yanning Wu, Qi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning ((SC)-C-3), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the (SC)-C-3 can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

关键词： and reasoning language vision

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Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval

Cross-Modal Implicit Relation Reasoning and Aligning for Tex...

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

作者： Jiang, Ding Ye, Mang Wuhan Univ Inst Artificial Intelligence Hubei Key Lab Multimedia & Network Commun Natl Engn Res Ctr Multimedia SoftwareSch Comp Sc Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (纸本)9798350301298

Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods.

关键词： and reasoning language vision

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