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RelTransformer: A Transformer-Based Long-Tail Visual Relationship recognition

RelTransformer: A Transformer-Based Long-Tail Visual Relatio...

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

作者： Chen, Jun Agarwal, Aniket Abdelkarim, Sherif Zhu, Deyao Elhoseiny, Mohamed King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Indian Inst Technol Chennai Tamil Nadu India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. These relationships typically have a long-tail distribution due to their compositional nature. This problem gets more severe when the vocabulary becomes large, rendering this task very challenging. This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. The method, called RelTransformer, represents each image as a fully-connected scene graph and restructures the whole scene into the relation-triplet and global-scene contexts. It directly passes the message from each element in the relation-triplet and global-scene contexts to the target relation via self-attention. We also design a learnable memory to augment the long-tail relation representation learning. Through extensive experiments, we find that our model generalizes well on many VRR benchmarks. Our model outperforms the best-performing models on two large-scale long-tail VRR benchmarks, VG8K-LT (+2.0% overall acc) and GQA-LT (+26.0% overall acc), both having a highly skewed distribution towards the tail. It also achieves strong results on the VG200 relation detection task.

关键词： Representation learning Visualization Vocabulary Image recognition Tail Benchmark testing Transformers

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Simulated Adversarial Testing of Face recognition Models

Simulated Adversarial Testing of Face Recognition Models

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

作者： Ruiz, Nataniel Kortylewski, Adam Qiu, Weichao Xie, Cihang Bargal, Sarah Adel Yuille, Alan Sclaroff, Stan Boston Univ Boston MA 02215 USA Johns Hopkins Univ Baltimore MD 21218 USA Huawei Shenzhen Peoples R China UC Santa Cruz Santa Cruz Do Sul RS Brazil

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be revealed at test time in the real world. The risks involved in such failures can be loss of profits, loss of time or even loss of life in certain critical applications. In order to alleviate this issue, simulators can be controlled in a fine-grained manner using interpretable parameters to explore the semantic image manifold. In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios. We apply this method in a face recognition setup. We show that certain weaknesses of models trained on real data can be discovered using simulated samples. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected spaces in the latent space of the simulator. We present a method to find these adversarial regions as opposed to the typical adversarial points found in the adversarial example literature.

关键词： Training Manifolds Shape Face recognition Semantics Nose Machine learning

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Reinforcement Cutting-Agent Learning for Video Object Segmentation 31

Reinforcement Cutting-Agent Learning for Video Object Segmen...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Han, Junwei Yang, Le Zhang, Dingwen Chang, Xiaojun Liang, Xiaodan Northwestern Polytech Univ Xian Shaanxi Peoples R China Xidian Univ Xian Shaanxi Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781538664209

Video object segmentation is a fundamental yet challenging task in computer vision community. In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework. Essentially, learning agents for segmentation is nontrivial as segmentation is a nearly continuous decision-making process, where the number of the involved agents (pixels or superpixels) and action steps from the seed (super) pixels to the whole object mask might be incredibly huge. To overcome this difficulty, this paper simplifies the learning of segmentation agents to the learning of a cutting-agent, which only has a limited number of action units and can converge in just a few action steps. The basic assumption is that object segmentation mainly relies on the interaction between object regions and their context. Thus, with an optimal object (box) region and context (box) region, we can obtain the desirable segmentation mask through further inference. Based on this assumption, we establish a novel reinforcement cutting-agent learning framework, where the cutting-agent consists of a cutting-policy network and a cutting-execution network. The former learns policies for deciding optimal object-context box pair, while the latter executes the cutting function based on the inferred object-context box pair. With the collaborative interaction between the two networks, our method can achieve the outperforming VOS performance on two public benchmarks, which demonstrates the rationality of our assumption as well as the effectiveness of the proposed learning framework.

关键词： Object segmentation Task analysis Search problems computer vision Decision making Tracking

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Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person Re-identification by Local Maximal Occurrence Represe...

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ieee conference on computer vision and pattern recognition (cvpr)

作者： Liao, Shengcai Hu, Yang Zhu, Xiangyu Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China

ISBN: (纸本)9781467369640

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.

关键词： Discriminant analysis

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PIGEON: Predicting Image Geolocations

PIGEON: Predicting Image Geolocations

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

作者： Haas, Lukas Skreta, Michal Alberti, Silas Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350353006

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy, success in prior literature is constrained to narrow distributions of images of landmarks, and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. Additionally, our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization;the first model, PIGEON, is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans, ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers, winning all six games. Our second model, PIGEOTTO, differs in that it is trained on a dataset of images from Flickr and Wikipedia, achieving state-of-the-art results on a wide range of image geolocalization benchmarks, outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate, planet-scale image geolocalization systems. Our code is available on GitHub.(1)

关键词： Blind Experiment CLIP Clustering computer vision GeoGuessr Haversine Image Geolocalization Location Refinement Multi-Modal Multi-Task Multi-Task Pretraining Photo Geolocalization Pretraining Retrieval Semantic Geocells Visual Place recognition Voronoi

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Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors

Effective Snapshot Compressive-spectral Imaging via Deep Den...

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

作者： Qiu, Haiquan Wang, Yao Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Shanghai Data Technol Co Ltd Shanghai Peoples R China Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9781665445092

Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-and-play framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-and-play algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://***/ucker/SCI-TVFFDNet.

关键词： Image coding TV Noise reduction Imaging Sensors pattern recognition Optimization

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SMD-Nets: Stereo Mixture Density Networks

SMD-Nets: Stereo Mixture Density Networks

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

作者： Tosi, Fabio Liao, Yiyi Schmitt, Carolin Geiger, Andreas Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy Univ Tubingen Autonomous Vis Grp MPI IS Tubingen Germany

ISBN: (纸本)9781665445092

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.

关键词： Solid modeling Three-dimensional displays Uncertainty Superresolution Estimation pattern recognition Spatial resolution

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Camouflaged Object Segmentation with Distraction Mining

Camouflaged Object Segmentation with Distraction Mining

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

作者： Mei, Haiyang Ji, Ge-Peng Wei, Ziqi Yang, Xin Wei, Xiaopeng Fan, Deng-Ping Dalian Univ Technol Dalian Peoples R China Wuhan Univ Wuhan Peoples R China Tsinghua Univ Beijing Peoples R China IIAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.

关键词： Measurement Frequency modulation MIMICs Refining Object segmentation Real-time systems pattern recognition

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Connecting What to Say With Where to Look by Modeling Human Attention Traces

Connecting What to Say With Where to Look by Modeling Human ...

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

作者： Meng, Zihang Yu, Licheng Zhang, Ning Berg, Tamara Damavandi, Babak Singh, Vikas Bearman, Amy Univ Wisconsin Madison WI 53705 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [31], where each word of a given caption is paired with a mouse trace segment. We propose two novel tasks: (1) predict a trace given an image and caption (i.e., visual grounding), and (2) predict a caption and a trace given only an image. Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded. We present a novel model architecture that is jointly trained on dual tasks (controlled trace generation and controlled caption generation). To evaluate the quality of the generated traces, we propose a local bipartite matching (LBM) distance metric which allows the comparison of two traces of different lengths. Extensive experiments show our model is robust to the imperfect training data and outperforms the baselines by a clear margin. Moreover, we demonstrate that our model pre-trained on the proposed tasks can be also beneficial to the downstream task of COCO's guided image captioning. Our code(1) and project page2 are publicly available.

关键词： Measurement Visualization Grounding Unified modeling language Training data computer architecture Transformers

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Progressive Teacher-student Learning for Early Action Prediction 32

Progressive Teacher-student Learning for Early Action Predic...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xionghui Hu, Jian-Fang Lai, Jianhuang Zhang, Jianguo Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Univ Dundee Dundee Scotland Guangdong Prov Key Lab Informat Secur Technol Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728132938

The goal of early action prediction is to recognize actions from partially observed videos with incomplete action executions, which is quite different from action recognition. Predicting early actions is very challenging since the partially observed videos do not contain enough action information for recognition. In this paper, we aim at improving early action prediction by proposing a novel teacher-student learning framework. Our framework involves a teacher model for recognizing actions from full videos, a student model for predicting early actions from partial videos, and a teacher-student learning block for distilling progressive knowledge from teacher to student, crossing different tasks. Extensive experiments on three public action datasets show that the proposed progressive teacher-student learning framework can consistently improve performance of early action prediction model. We have also reported the state-of-the-art performances for early action prediction on all of these sets.

关键词： Action recognition Deep Learning Video Analytics vision Applications and Systems

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