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Finding Facial Forgery Artifacts with Parts-Based Detectors

Finding Facial Forgery Artifacts with Parts-Based Detectors

引用

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

作者： Schwarcz, Steven Chellappa, Rama Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665448994

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deep-fake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.

关键词： Deep learning computer vision Social networking (online) Face recognition conferences Neural networks Detectors

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Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for recognition and Retrieval

Dual Pose-invariant Embeddings: Learning Category and Object...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Rohan Sarkar Avinash Kak Electrical and Computer Engineering Purdue University USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In the context of pose-invariant object recognition and retrieval, we demonstrate that it is possible to achieve significant improvements in performance if both the category-based and the object-identity-based embed-dings are learned simultaneously during training. In hind-sight, that sounds intuitive because learning about the cat-egories is more fundamental than learning about the indi-vidual objects that correspond to those categories. How-ever, to the best of what we know, no prior work in pose-invariant learning has demonstrated this effect. This paper presents an attention-based dual-encoder architecture with specially designed loss functions that optimize the inter-and intra-class distances simultaneously in two different embedding spaces, one for the category embeddings and the other for the object level embeddings. The loss functions we have proposed are pose-invariant ranking losses that are designed to minimize the intra-class distances and maximize the inter-class distances in the dual representation spaces. We demonstrate the power of our approach with three challenging multi-view datasets, Model Net-40, ObjectPI, and FG3D. With our dual approach, for single-view object recognition, we outperform the previous best by 20.0% on ModelNet40, 2.0% on ObjectPI, and 46.5% on FG3D. On the other hand, for single-view object retrieval, we outperform the previous best by 33.7% on ModelNet40, 18.8% on ObjectPI, and 56.9% on FG3D.

关键词： Training computer vision computer architecture pattern recognition Object recognition

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Diversifying Sample Generation for Accurate Data-Free Quantization

Diversifying Sample Generation for Accurate Data-Free Quanti...

引用

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

作者： Zhang, Xiangguo Qin, Haotong Ding, Yifu Gong, Ruihao Yan, Qinghua Tao, Renshuai Li, Yuhang Yu, Fengwei Liu, Xianglong Beihang Univ Beijing Peoples R China Yale Univ New Haven CT 06520 USA SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated with synthetic data perform close to those calibrated with real data and even outperform them on W4A4.

关键词： computer vision Quantization (signal) Neural networks Training data Network architecture Data models pattern recognition

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

引用

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs

VX2TEXT: End-to-End Learning of Video-Based Text Generation ...

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

作者： Lin, Xudong Bertasius, Gedas Wang, Jue Chang, Shih-Fu Parikh, Devi Torresani, Lorenzo Columbia Univ New York NY 10027 USA Facebook AI Menlo Pk CA USA Georgia Tech Atlanta GA USA Dartmouth Hanover NH USA

ISBN: (纸本)9781665445092

We present VX2TEXT, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, which have been shown to be effective at modeling language, each modality is first converted into a set of language embeddings by a learnable tokenizer. This allows our approach to perform multimodal fusion in the language space, thus eliminating the need for ad-hoc cross-modal fusion modules. To address the non-differentiability of tokenization on continuous inputs (e.g., video or audio), we utilize a relaxation scheme that enables end-to-end training. Furthermore, unlike prior encoder-only models, our network includes an autoregressive decoder to generate open-ended text from the multimodal embeddings fused by the language encoder. This renders our approach fully generative and makes it directly applicable to different "video+x to text" problems without the need to design specialized network heads for each task. The proposed framework is not only conceptually simple but also remarkably effective: experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks-captioning, question answering and audio-visual scene-aware dialog.

关键词： Training Computational modeling Semantics computer architecture Fasteners Transformers Knowledge discovery

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Deeply Shape-guided Cascade for Instance Segmentation

Deeply Shape-guided Cascade for Instance Segmentation

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

作者： Ding, Hao Qiao, Siyuan Yuille, Alan Shen, Wei Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages. Although modern instance segmentation cascades achieve leading performance, they mainly make use of a unidirectional relationship, i.e., mask segmentation can benefit from iteratively refined bounding box detection. In this paper, we investigate an alternative direction, i.e., how to take the advantage of precise mask segmentation for bounding box detection in a cascade architecture. We propose a Deeply Shape-guided Cascade (DSC) for instance segmentation, which iteratively imposes the shape guidances extracted from mask prediction at previous stage on bounding box detection at current stage. It forms a bi-directional relationship between the two tasks by introducing three key components: (1) Initial shape guidance: A mask-supervised Region Proposal Network (mPRN) with the ability to generate class-agnostic masks;(2) Explicit shape guidance: A mask-guided regionof-interest (RoI) feature extractor, which employs mask segmentation at previous stage to focus feature extraction at current stage within a region aligned well with the shape of the instance-of-interest rather than a rectangular RoI;(3) Implicit shape guidance: A feature fusion operation which feeds intermediate mask features at previous stage to the bounding box head at current stage. Experimental results show that DSC outperforms the state-of-the-art instance segmentation cascade, Hybrid Task Cascade (HTC), by a large margin and achieves 51.8 box AP and 45.5 mask AP on COCO test-dev. The code is released at: https://***/hding2455/DSC.

关键词： Feedback loop computer vision Codes Shape Bidirectional control Feature extraction pattern recognition

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MESA: Matching Everything by Segmenting Anything

MESA: Matching Everything by Segmenting Anything

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yesheng Zhang Xu Zhao Department of Automation Shanghai Jiao Tong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Feature matching is a crucial task in the field of computer vision, which involves finding correspondences between images. Previous studies achieve remarkable performance using learning-based feature comparison. However, the pervasive presence of matching redundancy between images gives rise to unnecessary and error-prone computations in these methods, imposing limitations on their accuracy. To address this issue, we propose MESA, a novel approach to establish precise area (or region) matches for efficient matching redundancy reduction. MESA first leverages the advanced image understanding capability of SAM, a state-of-the-art foundation model for image segmentation, to obtain image areas with implicit semantic. Then, a multi-relational graph is proposed to model the spatial structure of these areas and construct their scale hierarchy. Based on graphical models derived from the graph, the area matching is reformulated as an energy minimization task and effectively resolved. Extensive experiments demonstrate that MESA yields substantial precision improvement for multiple point matchers in indoor and outdoor downstream tasks, e.g. +13.61% for DKM in indoor pose estimation.

关键词： Image segmentation computer vision Graphical models Accuracy Redundancy Semantics Pose estimation

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Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting

Cross-Modal Collaborative Representation Learning and a Larg...

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

作者： Liu, Lingbo Chen, Jiaqi Wu, Hefeng Li, Guanbin Li, Chenglong Lin, Liang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Anhui Univ Hefei Peoples R China DarkMatter AI Res Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited information of RGB images and cannot well discover potential pedestrians in unconstrained scenarios. In this work, we find that incorporating optical and thermal information can greatly help to recognize pedestrians. To promote future researches in this field, we introduce a large-scale RGBT Crowd Counting (RGBT-CC) benchmark, which contains 2,030 pairs of RGB-thermal images with 138,389 annotated people. Furthermore, to facilitate the multimodal crowd counting, we propose a cross-modal collaborative representation learning framework, which consists of multiple modality-specific branches, a modality-shared branch, and an Information Aggregation-Distribution Module (IADM) to capture the complementary information of different modalities fully. Specifically, our IADM incorporates two collaborative information transfers to dynamically enhance the modality-shared and modality-specific representations with a dual information propagation mechanism. Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting. Moreover, the proposed approach is universal for multimodal crowd counting and is also capable to achieve superior performance on the ShanghaiTechRGBD [22] dataset. Finally, our source code and benchmark have been released at http://***/RGBT_Crowd_***.

关键词： computer vision Codes Collaboration Benchmark testing Optical imaging pattern recognition Task analysis

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Boosting Unconstrained Face recognition with Auxiliary Unlabeled Data

Boosting Unconstrained Face Recognition with Auxiliary Unlab...

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

作者： Shi, Yichun Jain, Anil K. Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665448994

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since the faces in these datasets usually contain limited degree and types of variation, the resulting trained models generalize poorly to more realistic unconstrained face datasets. While collecting labeled faces with larger variations could be helpful, it is practically infeasible due to privacy and labor cost. In comparison, it is easier to acquire a large number of unlabeled faces from different domains, which could be used to regularize the learning of face representations. We present an approach to use such unlabeled faces to learn generalizable face representations, where we assume neither the access to identity labels nor domain labels for unlabeled images. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can (i) lead to an appreciable gain in recognition performance and (ii) outperform the supervised baseline when combined with less than half of the labeled data. Compared with the state-of-the-art face recognition methods, our method further improves their performance on challenging benchmarks, such as IJBB, IJB-C and IJB-S.

关键词： Training Visualization Privacy computer vision Protocols Face recognition conferences

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A vision Check-up for Language Models

A Vision Check-up for Language Models

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conference on computer vision and pattern recognition (cvpr)

作者： Pratyusha Sharma Tamar Rott Shaham Manel Baradad Adrián Rodriíuez-Muñoz Shivam Duggal Phillip Isola Antonio Torralba Stephanie Fu MIT CSAIL UC Berkeley

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

What does learning to model relationships between strings teach Large Language Models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels, we use code to represent images in our study. Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore, experiments on self-supervised visual representation learning, utilizing images generated with text models, highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.

关键词： Representation learning Visualization Analytical models computer vision Codes Image synthesis Computational modeling

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