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Continual Forgetting for Pre-trained vision Models

Continual Forgetting for Pre-trained Vision Models

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

作者： Zhao, Hongbo Ni, Bolin Fang, Junsong Wang, Yuxi Chen, Yuntao Meng, Gaofeng Zhang, Zhaoxiang Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot Hong Kong Inst Sci & Innovat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests originate at any time from both users and model owners. These requests usually form a sequence. Therefore, under such a setting, selective information is expected to be continuously removed from a pre-trained model while maintaining the rest. We define this problem as continual forgetting and identify two key challenges. (i) For unwanted knowledge, efficient and effective deleting is crucial. (ii) For remaining knowledge, the impact brought by the forgetting procedure should be minimal. To address them, we propose Group Sparse LoRA (GS-LoRA). Specifically, towards (i), we use LoRA modules to fine-tune the FFN layers in Transformer blocks for each forgetting task independently, and towards (ii), a simple group sparse regularization is adopted, enabling automatic selection of specific LoRA groups and zeroing out the others. GS-LoRA is effective, parameter-efficient, data-efficient, and easy to implement. We conduct extensive experiments on face recognition, object detection and image classification and demonstrate that GS-LoRA manages to forget specific classes with minimal impact on other classes. Codes will be released on https://***/bjzhb666/GS-LoRA.

关键词： Continual Forgetting Machine Unlearning

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On Scaling up a Multilingual vision and Language Model

On Scaling up a Multilingual Vision and Language Model

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

作者： Chen, Xi Djolonga, Josip Padlewski, Piotr Mustafa, Basil Changpinyo, Soravit Wu, Jialin Ruiz, Carlos Riquelme Goodman, Sebastian Wang, Xiao Tay, Yi Shakeri, Siamak Dehghani, Mostafa Salz, Daniel Lucic, Mario Tschannen, Michael Nagrani, Arsha Hu, Hexiang Joshi, Mandar Pang, Bo Montgomery, Ceslee Pietrzyk, Paulina Ritter, Marvin Piergiovanni, A. J. Minderer, Matthias Pavetic, Filip Waters, Austin Li, Gang Alabdulmohsin, Ibrahim Beyer, Lucas Amelot, Julien Lee, Kenton Steiner, Andreas Peter Li, Yang Keysers, Daniel Arnab, Anurag Xu, Yuanzhong Rong, Keran Kolesnikov, Alexander Seyedhosseini, Mojtaba Angelova, Anelia Zhai, Xiaohua Houlsby, Neil Soricut, Radu Google Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

We explore the boundaries of scaling up a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.

关键词： language multimodal pretraining vision

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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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Towards Explainable Visual Vessel recognition Using Fine-Grained Classification and Image Retrieval

Towards Explainable Visual Vessel Recognition Using Fine-Gra...

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

作者： Karus, Heiko Schwenker, Friedhelm Munz, Michael Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Ulm Univ Ulm Germany Ulm Univ Appl Sci Ulm Germany

ISBN: (纸本)9798350365474

The precise recognition of vessel types is critical for applications in maritime surveillance, but manual visual inspection is slow and error-prone. Automated fine-grained object recognition helps to quickly and accurately categorize vessels, as long as they overcome challenges of modern deep and machine learning-based methods such as class imbalance or intransparency. This paper utilizes recent literature to work towards this task considering two different problem formulations: fine-grained classification and image retrieval. We create a novel dataset called Military MARVEL consisting of 15,858 images of 137 military vessel classes to conduct the extensive, survey-like experiments. On two maritime datasets, we demonstrate that end-to-end fine-grained classification leads to slightly higher accuracy for the price of less flexibility compared to image retrieval. Heatmap-based eXplainable Artificial Intelligence methods are combined with the vessel recognition approaches to assess the achieved transparency quantitatively and qualitatively. Notably, quantitative measures are consistent with qualitative evaluation, especially in fine-grained classification. Our Military MARVEL dataset is available at https://***/HensoldtOptronicsCV/FineGrainedVesselrecognition.

关键词： Military photography

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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...

引用

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

引用

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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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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NICE: cvpr 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

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

作者： Kim, Taehoon Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Marsden, Mark Sala, Alessandra Kim, Seung Hwan Han, Bohyung Lee, Kyoung Mu Lee, Honglak Bae, Kyounghoon Wu, Xiangyu Gao, Yi Zhang, Hailiang Yang, Yang Guo, Weili Lu, Jianfeng Oh, Youngtaek Cho, Jae Won Kim, Dong-Jin Kweon, In So Kim, Junmo Kang, Wooyoung Jhoo, Won Young Roh, Byungseok Mun, Jonghwan Oh, Solgil Ak, Kenan Emir Lee, Gwang-Gook Xu, Yan Shen, Mingwei Hwang, Kyomin Shin, Wonsik Lee, Kamin Park, Wonhark Lee, Dongkwan Kwak, Nojun Wang, Yujin Wang, Yimu Gu, Tiancheng Lv, Xingchang Sun, Mingmao

ISBN: (纸本)9798350365474

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Image captioning Multimodal representation vision-language models

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AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...

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

作者： Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA

ISBN: (纸本)9798350353006

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents are unavoidable. The retrieval-based approach alleviates the limitation by identifying reports which are relevant to the input to assist the generation. To achieve clinically accurate report retrieval, we make reference to clinicians' diagnostic steps of examining a radiology image where anatomical and diagnostic details are typically focused, and propose a novel hierarchical visual concept representation called anatomy-aware hierarchical vision encoding (AHIVE). To learn AHIVE, we first derive a methodology to extract hierarchical diagnostic descriptions from radiology reports and develop a CLIP-based framework for the model training. Also, the hierarchical architecture of AHIVE is designed to support interactive report retrieval so that report revision made at one layer can be propagated to the subsequent ones to trigger other necessary revisions. We conduct extensive experiments and show that AHIVE can outperform the SOTA vision-language retrieval methods in terms of clinical accuracy by a large margin. We provide also a case study to illustrate how it enables interactive report retrieval.

关键词： image-to-text retrieval interactive retrieval system radiology imaging radiology report retrieval

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Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for vision Applications

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse...

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

作者： Xiong, Yuwen Li, Zhiqi Chen, Yuntao Wang, Feng Zhu, Xizhou Luo, Jiapeng Wang, Wenhai Lu, Tong Li, Hongsheng Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Univ Toronto Toronto ON Canada Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Chinese Acad Sci CAIR HKISI Beijing Peoples R China Tsinghua Univ Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed, with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks, including image classification, instance and semantic segmentation, and notably, image generation. When integrated into generative models like U-Net in the latent diffusion model, DCNv4 outperforms its baseline, underscoring its possibility to enhance generative models. In practical applications, replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.

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