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Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

Learning on Gradients: Generalized Artifacts Representation ...

引用

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

作者： Tan, Chuangchuang Zhao, Yao Wei, Shikui Gu, Guanghua Wei, Yunchao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao Peoples R China

ISBN: (纸本)9798350301298

Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. The code is released at https://***/chuangchuangtan/LGrad.

关键词： detection recognition: Categorization retrieval

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From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models

From Images to Textual Prompts: Zero-shot Visual Question An...

引用

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

作者： Guo, Jiaxian Li, Junnan Li, Dongxu Tiong, Anthony Meng Huat Li, Boyang Tao, Dacheng Hoi, Steven Univ Sydney Sydney NSW Australia Salesforce Res San Francisco CA USA Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zero-shot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://***/salesforce/LAVIS/ tree/main/projects/img2llm-vqa.

关键词： language reasoning vision

来源：评论

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You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos

You Can Ground Earlier than See: An Effective and Efficient ...

引用

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

作者： Fang, Xiang Liu, Daizong Zhou, Pan Nan, Guoshun Huazhong Univ Sci & Technol Hubei Key Lab Distributed Syst Secur Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan Peoples R China Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made decent success, they only focus on high-level visual features extracted from the consecutive decoded frames and fail to handle the compressed videos for query modelling, suffering from insufficient representation capability and significant computational complexity during training and testing. In this paper, we pose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input. To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding. Particularly, instead of encoding the whole decoded frames like previous works, we capture the appearance representation by only learning the I-frame feature to reduce delay or latency. Besides, we explore the motion information not only by learning the motion vector feature, but also by exploring the relations of neighboring frames via the residual feature. In this way, a three-branch spatial-temporal attention layer with an adaptive motion-appearance fusion module is further designed to extract and aggregate both appearance and motion information for the final grounding. Experiments on three challenging datasets shows that our TCSF achieves better performance than other state-of-the-art methods with lower complexity.

关键词： language reasoning vision

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RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

RepMode: Learning to Re-parameterize Diverse Experts for Sub...

引用

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

作者： Zhou, Donghao Gu, Chunbin Xu, Junde Liu, Furui Wang, Qiong Chen, Guangyong Heng, Pheng-Ann Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vis & Virtual Real Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

关键词： cell microscopy Medical and biological vision

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MIST : Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering

MIST : Multi-modal Iterative Spatial-Temporal Transformer fo...

引用

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

作者： Gao, Difei Zhou, Luowei Ji, Lei Zhu, Linchao Yang, Yi Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore Microsoft Albuquerque NM USA Microsoft Res Asia Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Google Brain Mountain View CA USA

ISBN: (纸本)9798350301298

To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must. Existing multi-modal VQA models achieve promising performance on images or short video clips, especially with the recent success of large-scale multimodal pre-training. However, when extending these methods to long-form videos, new challenges arise. On the one hand, using a dense video sampling strategy is computationally prohibitive. On the other hand, methods relying on sparse sampling struggle in scenarios where multievent and multi-granularity visual reasoning are required. In this work, we introduce a new model named Multimodal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. Specifically, MIST decomposes traditional dense spatial-temporal self-attention into cascaded segment and region selection modules that adaptively select frames and image regions that are closely relevant to the question itself. Visual concepts at different granularities are then processed efficiently through an attention module. In addition, MIST iteratively conducts selection and attention over multiple layers to support reasoning over multiple events. The experimental results on four VideoQA datasets, including AGQA, NExT-QA, STAR, and Env-QA, show that MIST achieves state-of-the-art performance and is superior at efficiency. The code is available at ***/showlab/mist.

关键词： language reasoning vision

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Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-trained Language Models with Multimodal Prompts t...

引用

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Hessel, Jack Park, Jae Sung Lu, Ximing Zellers, Rowan Ammanabrolu, Prithviraj Le Bras, Ronan Kim, Gunhee Choi, Yejin Allen Inst Artificial Intelligence Seattle WA USA OpenAI Seattle WA USA Yonsei Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Univ Washington Paul G Allen Sch Comp Sci Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 [7] manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning;these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

关键词： and reasoning language vision

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All-in-one Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations

All-in-one Image Restoration for Unknown Degradations Using ...

引用

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

作者： Park, Dongwon Lee, Byung Hyun Chun, Se Young Seoul Natl Univ INMC Seoul South Korea Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea UNIST Dept EE Ulsan South Korea

ISBN: (纸本)9798350301298

Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time. To mitigate this issue, image restorations for known and unknown multiple degradations have recently been investigated, showing promising results, but require large networks or have sub-optimal architectures for potential interference among different degradations. Here, inspired by the filter attribution integrated gradients (FAIG), we propose an adaptive discriminative filter-based model for specific degradations (ADMS) to restore images with unknown degradations. Our method allows the network to contain degradation-dedicated filters only for about 3% of all network parameters per each degradation and to apply them adaptively via degradation classification (DC) to explicitly disentangle the network for multiple degradations. Our proposed method has demonstrated its effectiveness in comparison studies and achieved state-of-the-art performance in all-in-one image restoration benchmark datasets of both Rain-Noise-Blur and Rain-Snow-Haze.

关键词： Low-level vision

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SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations

SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sk...

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

作者： Li, Pu Guo, Jianwei Zhang, Xiaopeng Yan, Dong-Ming Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECADNet, an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models in a self-supervised manner. Drawing inspiration from the modeling language that is most commonly used in modern CAD software, we propose to learn 2D sketches and 3D extrusion parameters from raw shapes, from which a set of extrusion cylinders can be generated by extruding each sketch from a 2D plane into a 3D body. By incorporating the Boolean operation (i.e., union), these cylinders can be combined to closely approximate the target geometry. We advocate the use of implicit fields for sketch representation, which allows for creating CAD variations by interpolating latent codes in the sketch latent space. Extensive experiments on both ABC and Fusion 360 datasets demonstrate the effectiveness of our method, and show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction. We further apply our approach to CAD editing and single-view CAD reconstruction. Code will be released at https://***/BunnySoCrazy/SECAD-Net.

关键词： vision + graphics

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LINe: Out-of-Distribution Detection by Leveraging Important Neurons

LINe: Out-of-Distribution Detection by Leveraging Important ...

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

作者： Ahn, Yong Hyun Park, Gyeong-Moon Kim, Seong Tae Kyung Hee Univ Dept Artificial Intelligence Seoul South Korea Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301298

It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specificfeatures equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and imageNet datasets. Code is available on https://***/LINe-OOD

关键词： detection recognition: Categorization retrieval

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Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

Query-Dependent Video Representation for Moment Retrieval an...

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

作者： Moon, WonJun Hyun, Sangeek Park, SangUk Park, Dongchan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea Pyler Seoul South Korea

ISBN: (纸本)9798350301298

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at ***/wjun0830/QD-DETR.

关键词： detection recognition: Categorization retrieval

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