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Making vision Transformers Efficient from A Token Sparsification View

Making Vision Transformers Efficient from A Token Sparsifica...

引用

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

作者： Chang, Shuning Wang, Pichao Lin, Ming Wang, Fan Zhang, David Junhao Jin, Rong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

The quadratic computational complexity to the number of tokens limits the practical applications of vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vision transformer, and (iii) non-general-purpose networks for downstream tasks. In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks. The semantic tokens represent cluster centers, and they are initialized by pooling image tokens in space and recovered by attention, which can adaptively represent global or local semantic information. Due to the cluster properties, a few semantic tokens can attain the same effect as vast image tokens, for both global and local vision transformers. For instance, only 16 semantic tokens on DeiT-(Tiny,Small,Base) can achieve the same accuracy with more than 100% inference speed improvement and nearly 60% FLOPs reduction;on Swin-(Tiny,Small,Base), we can employ 16 semantic tokens in each window to further speed it up by around 20% with slight accuracy increase. Besides great success in image classification, we also extend our method to video recognition. In addition, we design a STViT-R(ecovery) network to restore the detailed spatial information based on the STViT, making it work for downstream tasks, which is powerless for previous token sparsification methods. Experiments demonstrate that our method can achieve competitive results compared to the original networks in object detection and instance segmentation, with over 30% FLOPs reduction for backbone.

关键词： Deep learning architectures and techniques

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Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

Generating Features with Increased Crop-related Diversity fo...

引用

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

作者： Xu, Jingyi Le, Hieu Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350301298

Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this crop-related variability requires abundant training data, which is not available in few-shot settings. To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity. The main idea is to transform the latent space such latent codes with different norms represent different crop-related variations. This allows us to generate features with increased crop-related diversity in difficulty levels by simply varying the latent norm. In particular, each latent code is rescaled such that its norm linearly correlates with the IoU score of the input crop w.r.t. the ground-truth box. Here the IoU score is a proxy that represents the difficulty level of the crop. We train this VAE model on base classes conditioned on the semantic code of each class and then use the trained model to generate features for novel classes. In our experiments our generated features consistently improve state-of-the-art few-shot object detection methods on the PASCAL VOC and MS COCO datasets.

关键词： detection recognition: Categorization retrieval

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DynamicDet: A Unified Dynamic Architecture for Object Detection

DynamicDet: A Unified Dynamic Architecture for Object Detect...

引用

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

作者： Lin, Zhihao Wang, Yongtao Zhang, Jinhe Chu, Xiaojie Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://***/VDIGPKU/DynamicDet.

关键词： detection recognition: Categorization retrieval

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Layout-based Causal Inference for Object Navigation

Layout-based Causal Inference for Object Navigation

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

作者： Zhang, Sixian Song, Xinhang Li, Weijie Bai, Yubing Yu, Xinyao Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on helping the agent infer the likely location of the goal when the layout gap between the unseen environments of the test and the prior knowledge obtained in training is minor. However, when the layout gap is significant, the experience exerts a negative effect on navigation. Motivated by keeping the positive effect and removing the negative effect of the experience, we propose the layout-based soft Total Direct Effect (L-sTDE) framework based on the causal inference to adjust the prediction of the navigation policy. In particular, we propose to calculate the layout gap which is defined as the KL divergence between the posterior and the prior distribution of the object layout. Then the sTDE is proposed to appropriately control the effect of the experience based on the layout gap. Experimental results on AI2THOR, RoboTHOR, and Habitat demonstrate the effectiveness of our method. The code is available at https://***/sxzhang/***.

关键词： Embodied vision: Active agents simulation

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Weak-shot Object Detection through Mutual Knowledge Transfer

Weak-shot Object Detection through Mutual Knowledge Transfer

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

作者： Du, Xuanyi Wan, Weitao Sun, Chong Li, Chen WeChat Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity.

关键词： detection recognition: Categorization retrieval

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Raw Image Reconstruction with Learned Compact Metadata

Raw Image Reconstruction with Learned Compact Metadata

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

作者： Wang, Yufei Yu, Yi Yang, Wenhan Guo, Lanqing Chau, Lap-Pui Kot, Alex C. Wen, Bihan Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with the improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images. The code will be released at https://***/wyf0912/R2LCM.

关键词： Low-level vision

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Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer

Recurrent Homography Estimation Using Homography-Guided Imag...

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

作者： Cao, Si-Yuan Zhang, Runmin Luo, Lun Yu, Beinan Sheng, Zehua Li, Junwei Shen, Hui-Liang Zhejiang Univ Ningbo Innovat Ctr Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350301298

We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global -> nonlocal -> local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://***/imdumpl78/RHWF.

关键词： Low-level vision

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Correspondence Transformers with Asymmetric Feature Learning and Matching Flow Super-Resolution

Correspondence Transformers with Asymmetric Feature Learning...

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

作者： Sun, Yixuan Zhao, Dongyang Yin, Zhangyue Huang, Yiwen Gui, Tao Zhang, Wenqiang Ge, Weifeng Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9798350301298

This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://***/YXSUNMADMAX/ACTR.

关键词： detection recognition: Categorization retrieval

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Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Video Dehazing via a Multi-Range Temporal Alignment Network ...

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

作者： Xu, Jiaqi Hu, Xiaowei Zhu, Lei Dou, Qi Dai, Jifeng Qiao, Yu Heng, Pheng-Ann Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method.

关键词： Low-level vision

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Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices

Abstract Visual Reasoning: An Algebraic Approach for Solving...

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

作者： Xu, Jingyi Vaidya, Tushar Wu, Yufei Chandra, Saket Lai, Zhangsheng Chong, Kai Fong Ernest Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore Singapore Polytech Singapore Singapore

ISBN: (纸本)9798350301298

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Grobner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall 93.2% accuracy, which significantly outperforms the current state-of-the-art accuracy of 77.0% and exceeds human performance at 84.4% accuracy.

关键词： and reasoning language vision

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