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An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

An Actor-centric Causality Graph for Asynchronous Temporal I...

引用

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

作者： Xie, Zhao Gao, Tian Wu, Kewei Chang, Jiao Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.

关键词： detection recognition: Categorization retrieval

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Balanced Product of Calibrated Experts for Long-Tailed recognition

Balanced Product of Calibrated Experts for Long-Tailed Recog...

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

作者： Aimar, Emanuel Sanchez Jonnarth, Arvi Felsberg, Michael Kuhlmann, Marco Linkoping Univ Dept Elect Engn Linkoping Sweden Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Husqvarna Grp Huskvarna Sweden Univ KwaZulu Natal Durban South Africa

ISBN: (纸本)9798350301298

Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://***/emasa/BalPoE-CalibratedLT.

关键词： continual low-shot meta or long-tail learning Transfer

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OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

OReX: Object Reconstruction from Planar Cross-sections Using...

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

作者： Sawdayee, Haim Vaxman, Amir Bermano, Amit H. Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9798350301298

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.

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

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Detecting Everything in the Open World: Towards Universal Object Detection

Detecting Everything in the Open World: Towards Universal Ob...

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

作者： Wang, Zhenyu Li, Yali Chen, Xi Lim, Ser-Nam Torralba, Antonio Zhao, Hengshuang Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Meta AI New York NY USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has the ability to recognize enormous categories in the open world. The critical points for the universality of UniDetector are: 1) it leverages images of multiple sources and heterogeneous label spaces for training through the alignment of image and text spaces, which guarantees sufficient information for universal representations. 2) it generalizes to the open world easily while keeping the balance between seen and unseen classes, thanks to abundant information from both vision and language modalities. 3) it further promotes the generalization ability to novel categories through our proposed decoupling training manner and probability calibration. These contributions allow UniDetector to detect over 7k categories, the largest measurable category size so far, with only about 500 classes participating in training. Our UniDetector behaves the strong zero-shot generalization ability on large-vocabulary datasets - it surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images. On 13 public detection datasets with various scenes, UniDetector also achieves state-of-the-art performance with only a 3% amount of training data. (1)

关键词： detection recognition: Categorization retrieval

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Meta-Explore: Exploratory Hierarchical vision-and-Language Navigation Using Scene Object Spectrum Grounding

Meta-Explore: Exploratory Hierarchical Vision-and-Language N...

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

作者： Hwang, Minyoung Jeong, Jaeyeon Kim, Minsoo Oh, Yoonseon Oh, Songhwai Seoul Natl Univ Elect & Comp Engn Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Hanyang Univ Dept Elect Engn Seoul South Korea Seoul Natl Univ Interdisciplinary Major Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350301298

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rateby17.1% andSPLby20.6% against the state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/***

关键词： Robotics

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Bit-shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization

Bit-shrinking: Limiting Instantaneous Sharpness for Improvin...

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

作者： Lin, Chen Peng, Bo Li, Zheyang Tan, Wenming Ren, Ye Xiao, Jun Pu, Shiliang Hikvis Res Inst Hangzhou Peoples R China Zhe Jiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is an effective compression method to reduce the model size and computational cost. However, quantizing a model into a low-bit one, e.g., lower than 4, is difficult and often results in non-negligible performance degradation. To address this, we investigate the loss landscapes of quantized networks with various bit-widths. We show that the network with more ragged loss surface, is more easily trapped into bad local minima, which mostly appears in low-bit quantization. A deeper analysis indicates, the ragged surface is caused by the injection of excessive quantization noise. To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise. To smooth the rugged loss surface, we propose to limit the sharpness term small and stable during optimization. Instead of directly optimizing the target bit network, we design a self-adapted shrinking scheduler for the bit-width in continuous domain from high bit-width to the target by limiting the increasing sharpness term within a proper range. It can be viewed as iteratively adding small "instant" quantization noise and adjusting the network to eliminate its impact. Widely experiments including classification and detection tasks demonstrate the effectiveness of the Bit-shrinking strategy in PTQ. On the vision Transformer models, our INT8 and INT6 models drop within 0.5% and 1.5% Top-1 accuracy, respectively. On the traditional CNN networks, our INT4 quantized models drop within 1.3% and 3.5% Top-1 accuracy on ResNet18 and MobileNetV2 without fine-tuning, which achieves the state-of-the-art performance.

关键词： Efficient and scalable vision

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Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior

Uncertainty-Aware Unsupervised Image Deblurring with Deep Re...

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

作者： Tang, Xiaole Zhao, Xile Liu, Jun Wang, Jianli Miao, Yuchun Zeng, Tieyong Univ Elect Sci & Technol China Chengdu Peoples R China Northeast Normal Univ Changchun Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the clear image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to model-driven and data-driven methods in terms of image quality and the robustness to different types of kernel error.

关键词： Explainable computer vision

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Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP

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

作者： Liang, Feng Wu, Bichen Dai, Xiaoliang Li, Kunpeng Zhao, Yinan Zhang, Hang Zhang, Peizhao Vajda, Peter Marculescu, Diana Univ Texas Austin Austin TX 78712 USA Meta Real Labs Burlingame CA USA Cruise Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage methods first generate class-agnostic mask proposals and then leverage pre-trained vision-language models, e.g., CLIP, to classify masked regions. We identify the performance bottleneck of this paradigm to be the pre-trained CLIP model, since it does not perform well on masked images. To address this, we propose to finetune CLIP on a collection of masked image regions and their corresponding text descriptions. We collect training data by mining an existing image-caption dataset (e.g., COCO Captions), using CLIP to match masked image regions to nouns in the image captions. Compared with the more precise and manually annotated segmentation labels with fixed classes (e.g., COCO-Stuff), we find our noisy but diverse dataset can better retain CLIP's generalization ability. Along with finetuning the entire model, we utilize the "blank" areas in masked images using a method we dub mask prompt tuning. Experiments demonstrate mask prompt tuning brings significant improvement without modifying any weights of CLIP, and it can further improve a fully finetuned model. In particular, when trained on COCO and evaluated on ADE20K-150, our best model achieves 29.6% mIoU, which is +8.5% higher than the previous state-of-the-art. For the first time, open-vocabulary generalist models match the performance of supervised specialist models in 2017 without dataset specific adaptations.

关键词： and reasoning language vision

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MAGVLT: Masked Generative vision-and-Language Transformer

MAGVLT: Masked Generative Vision-and-Language Transformer

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

作者： Kim, Sungwoong Jo, Daejin Lee, Donghoon Kim, Jongmin Korea Univ Dept Artificial Intelligence Seoul South Korea Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350301298

While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unified generative vision-and-language (VL) model that can produce both images and text sequences. Especially, we propose a generative VL transformer based on the non-autoregressive mask prediction, named MAGVLT, and compare it with an autoregressive generative VL transformer (ARGVLT). In comparison to ARGVLT, the proposed MAGVLT enables bidirectional context encoding, fast decoding by parallel token predictions in an iterative refinement, and extended editing capabilities such as image and text infilling. For rigorous training of our MAGVLT with image-text pairs from scratch, we combine the image-to-text, text-to-image, and joint image-and-text mask prediction tasks. Moreover, we devise two additional tasks based on the step-unrolled mask prediction and the selective prediction on the mixture of two image-text pairs. Experimental results on various downstream generation tasks of VL benchmarks show that our MAGVLT outperforms ARGVLT by a large margin even with significant inference speedup. Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderatesized model (fewer than 500M parameters) even without the use of monomodal data and networks.

关键词： Multi-modal learning

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Generalized Decoding for Pixel, Image, and Language

Generalized Decoding for Pixel, Image, and Language

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

作者： Zou, Xueyan Dou, Zi-Yi Yang, Jianwei Gan, Zhe Li, Linjie Li, Chunyuan Dai, Xiyang Behl, Harkirat Wang, Jianfeng Yuan, Lu Peng, Nanyun Wang, Lijuan Lee, Yong Jae Gao, Jianfeng Univ Wisconsin Madison Madison WI 53706 USA UCLA Los Angeles CA 90024 USA Microsoft Res Redmond Redmond WA USA Microsoft Cloud & AI Redmond WA USA

ISBN: (纸本)9798350301298

We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Without any pseudo-labeling, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level understanding. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on seven datasets;(2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks;and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing shown in Fig. 1). Code, demo, video and visualization are available at: https://***.

关键词： and reasoning language vision

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