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DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection

DiffAM: Diffusion-based Adversarial Makeup Transfer for Faci...

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

作者： Sun, Yuhao Yu, Lingyun Xie, Hongtao Li, Jiaming Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

With the rapid development of face recognition (FR) systems, the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However, the generated adversarial examples, i.e., the protected face images, tend to suffer from subpar visual quality and low transferability. In this paper, we propose a novel face protection approach, dubbed DiffAM, which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific, we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer, makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then, with this relationship, a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain, achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://***/HansSunY/DiffAM.

关键词： adversarial attack diffusion models face recognition

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

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

作者： Koyun, Onur Can Toreyin, Behcet Ugur Istanbul Tech Univ Dept Artificial Intelligence & Data Engn Signal Proc Computat Intelligence Res Grp SP4CING Inst Informat Istanbul Turkiye

ISBN: (纸本)9798350365474

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT's performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： CNN computer vision deep learning Efficient Parameter Transformer vision Transformer ViT

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Language Models as Black-Box Optimizers for vision-Language Models

Language Models as Black-Box Optimizers for Vision-Language ...

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

作者： Liu, Shihong Yu, Samuel Lin, Zhiqiu Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.

关键词： generative models large language models prompting text-to-image generation vision-language models

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GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting...

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

作者： Zheng, Shunyuan Zhou, Boyao Shao, Ruizhi Liu, Boning Zhang, Shengping Nie, Liqiang Liu, Yebin Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed. The code is available at https://***/aipixel/GPS-Gaussian.

关键词： Rendering (computer graphics)

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MAPLM: A Real-World Large-Scale vision-Language Benchmark for Map and Traffic Scene Understanding

MAPLM: A Real-World Large-Scale Vision-Language Benchmark fo...

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

作者： Cao, Xu Zhou, Tong Ma, Yunsheng Ye, Wenqian Cui, Can Tang, Kun Cao, Zhipeng Liang, Kaizhao Wang, Ziran Rehg, James M. Zheng, Chao Tencent T Lab Palo Alto CA 94306 USA Univ Illinois Champaign IL USA Purdue Univ W Lafayette IN USA Univ Virginia Charlottesville VA USA SambaNova Syst Inc Palo Alto CA USA

ISBN: (纸本)9798350353006

vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However, current benchmark datasets lack multi-modal point cloud, image, and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper, we propose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically, we annotate and leverage large-scale, broad-coverage traffic and map data extracted from huge HD map annotations, and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful representations from MAPLM-QA, there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research, we will release our visual-language QA data, and the baseline models at ***/LLVM-AD/MAPLM.

关键词： High-definition (HD) Map Large Language Model Multimodal Learning vision-Language Model Visual Question Answering

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Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of t...

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

作者： Wang, Hongjie Dedhia, Bhishma Jha, Niraj K. Princeton Univ Princeton NJ 08540 USA

ISBN: (纸本)9798350353006

Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However, most token pruning methods require computationally expensive fine-tuning, which is undesirable in many edge deployment cases. In this work, we propose Zero-TPrune, the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead, Zero-TPrune can prune large models at negligible computational cost, switch between different pruning configurations at no computational cost, and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning, Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state- of-the-art pruning methods that require fine-tuning, Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods, Zero-TPrune reduces accuracy loss by up to 49% with similar FLOPs budgets. Project webpage: https://***/zerotprune.

关键词： attention efficiency token pruning vision transformer zero-shot

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Pre-training vision Models with Mandelbulb Variations

Pre-training Vision Models with Mandelbulb Variations

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

作者： Chiche, Benjamin Naoto Horikawa, Yuto Fujita, Ryo Rist Inc 830 Hongakujimae-ChoGojo-DoriShimogyo-Ku Kyoto 6008102 Japan

ISBN: (纸本)9798350353006

The use of models that have been pre-trained on natural image datasets like ImageNet may face some limitations. First, this use may be restricted due to copyright and license on the training images, and privacy laws. Second, these datasets and models may incorporate societal and ethical biases. Formula-driven supervised learning (FDSL) enables model pre-training to circumvent these issues. This consists of generating a synthetic image dataset based on mathematical formulae and pre-training the model on it. In this work, we propose novel FDSL datasets based on Mandelbulb Variations. These datasets contain RGB images that are projections of colored objects deriving from the 3D Mandelbulb fractal. Pre-training ResNet-50 on one of our proposed datasets MandelbulbVAR-1k enables an average top-1 accuracy over target classification datasets that is at least 1% higher than pre-training on existing FDSL datasets. With regard to anomaly detection on MVTec AD, pre-training the WideResNet-50 backbone on MandelbulbVAR-1k enables PatchCore to achieve 97.2% average image-level AUROC. This is only 1.9% lower than pre-training on ImageNet-1k (99.1%) and 4.5% higher than pre-training on the second-best performing FDSL dataset i.e. VisualAtom-1k (92.7%). Regarding vision Transformer (ViT) pre-training, another dataset that we propose and coin MandelbulbVAR-Hybrid-21k enables ViT-Base to achieve 82.2% top-1 accuracy on ImageNet-1k, which is 0.4% higher than pre-training on ImageNet-21k (81.8%) and only 0.1% lower than pre-training on VisualAtom-1k (82.3%).

关键词： anomaly detection classification convolutional neural network Formula-driven supervised learning fractal mandelbulb pre-training vision transformer

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DRESS: Instructing Large vision-Language Models to Align and Interact with Humans via Natural Language Feedback

DRESS: Instructing Large Vision-Language Models to Align and...

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

作者： Chen, Yangyi Sikka, Karan Cogswell, Michael Ji, Heng Divakaran, Ajay SRI Int Menlo Pk CA 94025 USA Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

We present DRESS, a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First, prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback, they are still prone to generate unhelpful, hallucinated, or harmful responses. Second, while the visual instruction tuning data is generally structured in a multi-turn dialogue format, the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these, we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF, we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%), honest (11.52%), and harmless (21.03%) responses, and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs.

关键词： Alignment Interaction Large vision Language Models Natural Language Feedback

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TIM: A Time Interval Machine for Audio-Visual Action recognition

TIM: A Time Interval Machine for Audio-Visual Action Recogni...

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

作者： Chalk, Jacob Huh, Jaesung Kazakos, Evangelos Zisserman, Andrew Damen, Dima Univ Bristol Bristol Avon England Univ Oxford VGG Oxford England Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9798350353006

Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events and distinct labels. We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events. We propose the Time Interval Machine (TIM) where a modality-specific time interval poses as a query to a transformer encoder that ingests a long video input. The encoder then attends to the specified interval, as well as the surrounding context in both modalities, in order to recognise the ongoing action. We test TIM on three long audio-visual video datasets: EPIC-KITCHENS, Perception Test, and AVE, reporting state-of-the-art (SOTA) for recognition. On EPIC-KITCHENS, we beat previous SOTA that utilises LLMs and significantly larger pre- training by 2.9% top-1 action recognition accuracy. Additionally, we show that TIM can be adapted for action detection, using dense multi-scale interval queries, outperforming SOTA on EPIC-KITCHENS-100 for most metrics, and showing strong performance on the Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at: https://***/JacobChalk/TIM.

关键词： action detection action recognition audio-visual learning egocentric videos video understanding

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Color Shift Estimation-and-Correction for Image Enhancement

Color Shift Estimation-and-Correction for Image Enhancement

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

作者： Li, Yiyu Xu, Ke Hancke, Gerhard Petrus Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate color tone distortion in underexposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and over-exposed regions display opposite color tone distribution shifts, which may not be easily normalized in joint modeling as they usually do not have "normal-exposed" regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the bright-ened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened ( or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches. Project web-page: https://***/yiyulics/CSEC.

关键词： computer vision Exposure Correction Image Enhancement Low-level vision Low-light Image Enhancement

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