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MMG-Ego4D: Multi-Modal Generalization in Egocentric Action recognition

MMG-Ego4D: Multi-Modal Generalization in Egocentric Action R...

引用

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

作者： Gong, Xinyu Mohan, Sreyas Dhingra, Naina Bazin, Jean-Charles Li, Yilei Wang, Zhangyang Ranjan, Rakesh Meta Real Labs Burlingame CA 94010 USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350301298

In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D [27] dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code are available at https://***/facebookresearch/MMG Ego4D

关键词： Multi-modal learning

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KERM: Knowledge Enhanced Reasoning for vision-and-Language Navigation

KERM: Knowledge Enhanced Reasoning for Vision-and-Language N...

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

作者： Li, Xiangyang Wang, Zihan Yang, Jiahao Wang, Yaowei Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Lab Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350301298

dvision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://***/XiangyangLi20/KERM.

关键词： language reasoning vision

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Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification

Bridging the Gap between Model Explanations in Partially Ann...

引用

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

作者： Kim, Youngwook Kim, Jae Myung Jeong, Jieun Schmid, Cordelia Akata, Zeynep Lee, Jungwoo Seoul Natl Univ Seoul South Korea Univ Tubingen Tubingen Germany HodooAI Lab Ho Chi Minh City Vietnam PSL Res Univ CNRS Ecole Normale Super Inria Paris France MPI Intelligent Syst Stuttgart Germany

ISBN: (纸本)9798350301298

Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https:// ***/youngwk/BridgeGapExplanationPAMC.

关键词： detection recognition: Categorization retrieval

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Flexible-C^m GAN: Towards Precise 3D Dose Prediction in Radiotherapy

Flexible-C<SUP>m</SUP> GAN: Towards Precise 3D Dose Predicti...

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

作者： Gao, Riqiang Lou, Bin Xu, Zhoubing Comaniciu, Dorin Kamen, Ali Siemens Healthineers Digital Technol & Innovat Princeton NJ 08540 USA

ISBN: (纸本)9798350301298

Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-Cm GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-Cm GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches.

关键词： cell microscopy Medical and biological vision

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Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN models

Neural Rate Estimator and Unsupervised Learning for Efficien...

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

作者： Ahuja, Nilesh Datta, Parual Kanzariya, Bhavya Somayazulu, V. Srinivasa Tickoo, Omesh Intel Labs Santa Clara CA 95054 USA IIT Hyderabad Kandi India

ISBN: (纸本)9798350301298

Thanks to advances in computer vision and AI, there has been a large growth in the demand for cloud-based visual analytics in which images captured by a low-powered edge device are transmitted to the cloud for analytics. Use of conventional codecs (JPEG, MPEG, HEVC, etc.) for compressing such data introduces artifacts that can seriously degrade the performance of the downstream analytic tasks. Split-DNN computing has emerged as a paradigm to address such usages, in which a DNN is partitioned into a client-side portion and a server side portion. Low-complexity neural networks called 'bottleneck units' are introduced at the split point to transform the intermediate layer features into a lower-dimensional representation better suited for compression and transmission. Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features. Most works on compression for image analytics use heuristic approaches to estimate the rate, leading to suboptimal performance. We propose a high-quality 'neural rate-estimator' to address this gap. We interpret the lower-dimensional bottleneck output as a latent representation of the intermediate feature and cast the rate-distortion optimization problem as one of training an equivalent variational auto-encoder with an appropriate loss function. We show that this leads to improved rate-distortion outcomes. We further show that replacing supervised loss terms (such as cross-entropy loss) by distillation-based losses in a teacher-student framework allows for unsupervised training of bottleneck units without the need for explicit training labels. This makes our method very attractive for real world deployments where access to labeled training data is difficult or expensive. We demonstrate that our method outperforms several state-of-the-art methods by obtaining improved task accuracy at lower bitrates on image classification and semantic segmentation

关键词： Efficient and scalable vision

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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

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Dynamic Generative Targeted Attacks with pattern Injection

Dynamic Generative Targeted Attacks with Pattern Injection

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

作者： Feng, Weiwei Xu, Nanqing Zhang, Tianzhu Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China

ISBN: (纸本)9798350301298

Adversarial attacks can evaluate model robustness and have been of great concern in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instance-specific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instance-agnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However, they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of the target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversarial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method shows superior performance than 10 existing adversarial attacks against 13 models.

关键词： Adversarial attack and defense

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Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Complementary Intrinsics from Neural Radiance Fields and CNN...

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

作者： Yang, Siqi Cui, Xuanning Zhu, Yongjie Tang, Jiajun Li, Si Yu, Zhaofei Shi, Boxin Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Comp Sci AI Innovat Ctr Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multi-view stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.

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

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Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

Interventional Bag Multi-Instance Learning On Whole-Slide Pa...

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

作者： Lin, Tiancheng Yu, Zhimiao Hu, Hongyu Xu, Yi Chen, Chang Wen Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into capturing spurious correlations between bags and labels. This deficiency is a confounder that limits the performance of existing MIL methods. In this paper, we propose a novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve deconfounded bag-level prediction. Unlike traditional likelihood-based strategies, the proposed scheme is based on the backdoor adjustment to achieve the interventional training, thus is capable of suppressing the bias caused by the bag contextual prior. Note that the principle of IBMIL is orthogonal to existing bag MIL methods. Therefore, IBMIL is able to bring consistent performance boosting to existing schemes, achieving new state-of-the-art performance. Code is available at https://***/HHHedo/IBMIL.

关键词： cell microscopy Medical and biological vision

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Context-aware Pretraining for Efficient Blind Image Decomposition

Context-aware Pretraining for Efficient Blind Image Decompos...

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

作者： Wang, Chao Zheng, Zhedong Quan, Ruijie Sun, Yifan Yang, Yi Zhejiang Univ ReLER CCAI Hangzhou Peoples R China Baidu Inc Beijing Peoples R China Natl Univ Singapore Sch Comp Sea NExT Joint Lab Singapore Singapore

ISBN: (纸本)9798350301298

In this paper, we study Blind Image Decomposition (BID), which is to uniformly remove multiple types of degradation at once without foreknowing the noise type. There remain two practical challenges: (1) Existing methods typically require massive data supervision, making them infeasible to real-world scenarios. (2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task. Therefore, such a pipeline compromises repairing efficiency and authenticity. In an attempt to solve the two challenges in one go, we propose an efficient and simplified paradigm, called Context-aware Pretraining (CP), with two pretext tasks: mixed image separation and masked image reconstruction. Such a paradigm reduces the annotation demands and explicitly facilitates context-aware feature learning. Assuming the restoration process follows a structure-to-texture manner, we also introduce a Context-aware Pretrained network (CPNet). In particular, CPNet contains two transformer-based parallel encoders, one information fusion module, and one multi-head prediction module. The information fusion module explicitly utilizes the mutual correlation in the spatial-channel dimension, while the multi-head prediction module facilitates texture-guided appearance flow. Moreover, a new sampling loss along with an attribute label constraint is also deployed to make use of the spatial context, leading to high-fidelity image restoration. Extensive experiments on both real and synthetic benchmarks show that our method achieves competitive performance for various BID tasks.

关键词： Low-level vision

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