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You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified vision Language Model

You Need Multiple Exiting: Dynamic Early Exiting for Acceler...

引用

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

作者： Tang, Shengkun Wang, Yaqing Kong, Zhenglun Zhang, Tianchi Li, Yao Ding, Caiwen Wang, Yanzhi Liang, Yi Xu, Dongkuan North Carolina State Univ Raleigh NC 27695 USA Google Res New York NY USA Univ Connecticut Mansfield PA USA Univ Michigan Ann Arbor MI USA Univ N Carolina Chapel Hill NC USA Northeastern Univ Boston MA USA

ISBN: (纸本)9798350301298

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full computation of the large-scale model, not all of inputs need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot be applied to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder layers. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To address this issue, we propose a novel early exiting strategy for unified vision language models, which allows to dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely MuE. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce expected inference time by up to 50% and 40% while maintaining 99% and 96% performance respectively.

关键词： Efficient and scalable vision

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DropKey for vision Transformer

DropKey for Vision Transformer

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

作者： Li, Bonan Hu, Yinhan Nie, Xuecheng Han, Congying Jiang, Xiangjian Guo, Tiande Liu, Luocji Univ Chinese Acad Sci Beijing Peoples R China Meitu Inc MT Lab Beijing Peoples R China Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information;Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training;Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.

关键词： computer vision theory

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GAN-based vision Transformer for High-Quality Thermal Image Enhancement

GAN-based Vision Transformer for High-Quality Thermal Image ...

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

作者： Marnissi, Mohamed Amine Fathallah, Abir Univ Sfax Ecole Natl Ingn Sfax Sfax 3038 Tunisia Inst Polytech Paris Samovar CNRS Telecom SudParis 9 Rue Charles Fourier F-91011 Evry France

ISBN: (纸本)9798350302493

Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector.

关键词： Image enhancement

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NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer

NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mou...

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

作者： Manne, Sai Kumar Reddy Martin, Brendan Roy, Tyler Neilson, Ryan Peters, Rebecca Chillara, Meghana Lary, Christine W. Motyl, Katherine J. Wan, Michael Northeastern Univ Boston MA 02115 USA MaineHlth Inst Res Scarborough ME USA Univ Maine Orono ME 04469 USA Tufts Univ Sch Med Medford MA 02155 USA

ISBN: (纸本)9798350365474

Osteoclast cell image analysis plays a key role in osteoporosis research, but it typically involves extensive manual image processing and hand annotations by a trained expert. In the last few years, a handful of machine learning approaches for osteoclast image analysis have been developed, but none have addressed the full instance segmentation task required to produce the same output as that of the human expert led process. Furthermore, none of the prior, fully automated algorithms have publicly available code, pretrained models, or annotated datasets, inhibiting reproduction and extension of their work. We present a new dataset with similar to 2 x 10(5) expert annotated mouse osteoclast masks, together with a deep learning instance segmentation method which works for both in vitro mouse osteoclast cells on plastic tissue culture plates and human osteoclast cells on bone chips. To our knowledge, this is the first work to automate the full osteoclast instance segmentation task. Our method achieves a performance of 0.82 mAP(0.5) (mean average precision at intersection-over-union threshold of 0.5) in cross validation for mouse osteoclasts. We present a novel nuclei-aware osteoclast instance segmentation training strategy (NOISe) based on the unique biology of osteoclasts, to improve the model's generalizability and boost the mAP(0.5) from 0.60 to 0.82 on human osteoclasts. We publish our annotated mouse osteoclast image dataset, instance segmentation models, and code at ***/michaelwwan/noise to enable reproducibility and to provide a public tool to accelerate osteoporosis research(1).

关键词： computer vision instance segmentation medical imaging microscope image analysis osteoporosis

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MammalNet: A Large-scale Video Benchmark for Mammal recognition and Behavior Understanding

MammalNet: A Large-scale Video Benchmark for Mammal Recognit...

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

作者： Chen, Jun Hu, Ming Coker, Darren J. Berumen, Michael L. Costelloe, Blair Beery, Sara Rohrbach, Anna Elhoseiny, Mohamed King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Max Planck Inst Anim Behav Radolfzell am Bodensee Germany Univ Konstanz Constance Germany MIT Cambridge MA USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350301298

Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of animals and their behaviors is critical for capitalizing on the large unlabeled datasets generated by modern video devices and for accelerating monitoring efforts at scale. However, the development of automated recognition systems is currently hindered by a lack of appropriately labeled datasets. Existing video datasets 1) do not classify animals according to established biological taxonomies;2) are too small to facilitate large-scale behavioral studies and are often limited to a single species;and 3) do not feature temporally localized annotations and therefore do not facilitate localization of targeted behaviors within longer video sequences. Thus, we propose MammalNet, a new large-scale animal behavior dataset with taxonomy-guided annotations of mammals and their common behaviors. MammalNet contains over 18K videos totaling 539 hours, which is similar to 10 times larger than the largest existing animal behavior dataset [36]. It covers 17 orders, 69 families, and 173 mammal categories for animal categorization and captures 12 high-level animal behaviors that received focus in previous animal behavior studies. We establish three benchmarks on MammalNet: standard animal and behavior recognition, compositional low-shot animal and behavior recognition, and behavior detection. Our dataset and code have been made available at: https://***.

关键词： Datasets and evaluation

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Towards Characterizing the Semantic Robustness of Face recognition

Towards Characterizing the Semantic Robustness of Face Recog...

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

作者： Perez, Juan C. Alfarra, Motasem Thabet, Ali Arbelaez, Pablo Ghanem, Bernard King Abdullah Univ Sci & Technol King WI 23955 USA Univ Ios Andes Ctr Res & Format Artificial Intelligence Bogota Colombia

ISBN: (纸本)9798350302493

Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input. Our methodology causes FRMs to malfunction by designing adversarial attacks that search for identity-preserving modifications to faces. In particular, given a face, our attacks find identity-preserving variants of the face such that an FRM fails to recognize the images belonging to the same identity. We model these identity-preserving semantic modifications via direction- and magnitude-constrained perturbations in the latent space of StyleGAN. We further propose to characterize the semantic robustness of an FRM by statistically describing the perturbations that induce the FRM to malfunction. Finally, we combine our methodology with a certification technique, thus providing (i) theoretical guarantees on the performance of an FRM, and (ii) a formal description of how an FRM may model the notion of face identity.

关键词： Face recognition

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Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering

Prompting Large Language Models with Answer Heuristics for K...

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

作者： Shao, Zhenwei Yu, Zhou Wang, Meng Yu, Jun Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3 [3]) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet-a conceptually simple framework designed to prompt GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively.

关键词： and reasoning language vision

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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

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

作者： Alzahrani, Mona Usman, Muhammad Anwar, Saeed Helmy, Tarek KFUPM Dept Informat & Comp Sci Dhahran Saudi Arabia Jouf Univ Coll Comp & Informat Sci Sakaka Saudi Arabia KFUPM SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia KFUPM Ctr Intelligent Secure Syst Dhahran Saudi Arabia

ISBN: (纸本)9798350365474

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-theart performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective MultiView Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： 3D Object Classification 3D Object recognition Multi-View Conventional Neural Network Multi-View Object Classification

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Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

Unlearnable Clusters: Towards Label-agnostic Unlearnable Exa...

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

作者： Zhang, Jiaming Ma, Xingjun Yi, Qi Sang, Jitao Jiang, Yu-Gang Wang, Yaowei Xu, Changsheng Beijing Jiaotong Univ Beijing Peoples R China Fudan Univ Shanghai Peoples R China Chinese Acad Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage vision-and-Language Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at https://***/jiamingzhang94/Unlearnable-Clusters.

关键词： computer vision for social good

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SynthVSR: Scaling Up Visual Speech recognition With Synthetic Supervision

SynthVSR: Scaling Up Visual Speech Recognition With Syntheti...

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

作者： Liu, Xubo Lakomkin, Egor Vougioukas, Konstantinos Ma, Pingchuan Chen, Honglie Xie, Ruiming Doulaty, Morrie Moritz, Niko Kolar, Jachym Petridis, Stavros Pantic, Maja Fuegen, Christian Univ Surrey Guildford Surrey England Meta AI New York NY 58051 USA

ISBN: (纸本)9798350301298

Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed SynthVSR, substantially improves the performance of VSR systems with synthetic lip movements. The key idea behind SynthVSR is to leverage a speech-driven lip animation model that generates lip movements conditioned on the input speech. The speech-driven lip animation model is trained on an unlabeled audio-visual dataset and could be further optimized towards a pre-trained VSR model when labeled videos are available. As plenty of transcribed acoustic data and face images are available, we are able to generate large-scale synthetic data using the proposed lip animation model for semi-supervised VSR training. We evaluate the performance of our approach on the largest public VSR benchmark - Lip Reading Sentences 3 (LRS3). SynthVSR achieves a WER of 43.3% with only 30 hours of real labeled data, outperforming off-the-shelf approaches using thousands of hours of video. The WER is further reduced to 27.9% when using all 438 hours of labeled data from LRS3, which is on par with the state-of-the-art self-supervised AV-HuBERT method. Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16.9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90,000 hours). Finally, we perform extensive ablation studies to understand the effect of each component in our proposed method.

关键词： and reasoning language vision

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