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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

引用

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

作者： Choi, Haram Lee, Jeongmin Yang, Jihoon Sogang Univ Dept Comp Sci & Engn Seoul South Korea LG Innotek Seoul South Korea

ISBN: (纸本)9798350301298

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

关键词： Low-level vision

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Context De-confounded Emotion recognition

Context De-confounded Emotion Recognition

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

作者： Yang, Dingkang Chen, Zhaoyu Wang, Yuzheng Wang, Shunli Li, Mingcheng Liu, Siao Zhao, Xiao Huang, Shuai Dong, Zhiyan Zhai, Peng Zhang, Lihua Fudan Univ Acad Engn & Technol Shanghai Peoples R China Inst Meta Med IPASS Boston MA 02108 USA Jilin Prov Key Lab Intelligence Sci & Engn Changchun Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350301298

Context-Aware Emotion recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.

关键词： vision applications and systems

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Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

Weakly supervised segmentation with point annotations for hi...

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

作者： Zhang, Hongrun Burrows, Liam Meng, Yanda Sculthorpe, Declan Mukherjee, Abhik Coupland, Sarah E. Chen, Ke Zheng, Yalin Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England Univ Liverpool Dept Math Sci Liverpool Merseyside England Univ Liverpool Ctr Math Imaging Techn Liverpool Merseyside England Univ Nottingham Biodiscovery Inst Sch Med Nottingham England Univ Liverpool Inst Syst Mol & Integrat Biol Liverpool Merseyside England

ISBN: (纸本)9798350301298

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://***/hrzhang1123/CVM_WS_Segmentation.

关键词： cell microscopy Medical and biological vision

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TrojViT: Trojan Insertion in vision Transformers

TrojViT: Trojan Insertion in Vision Transformers

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

作者： Zheng, Mengxin Lou, Qian Jiang, Lei Indiana Univ Bloomington Bloomington IN 47405 USA Univ Cent Florida Orlando FL USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Compared to CNNs capturing pixel-wise local features by convolutions, ViTs extract global context information through patches and attentions. Naively transplanting CNN-specific backdoor attacks to ViTs yields only a low clean data accuracy and a low attack success rate. In this paper, we propose a stealth and practical ViT-specific backdoor attack TrojViT. Rather than an area-wise trigger used by CNN-specific backdoor attacks, TrojViT generates a patch-wise trigger designed to build a Trojan composed of some vulnerable bits on the parameters of a ViT stored in DRAM memory through patch salience ranking and attention-target loss. TrojViT further uses parameter distillation to reduce the bit number of the Trojan. Once the attacker inserts the Trojan into the ViT model by flipping the vulnerable bits, the ViT model still produces normal inference accuracy with benign inputs. But when the attacker embeds a trigger into an input, the ViT model is forced to classify the input to a predefined target class. We show that flipping only few vulnerable bits identified by TrojViT on a ViT model using the well-known RowHammer can transform the model into a backdoored one. We perform extensive experiments of multiple datasets on various ViT models. TrojViT can classify 99.64% of test images to a target class by flipping 345 bits on a ViT for ImageNet.

关键词： accountability ethics in vision fairness privacy Transparency

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Are We Ready for vision-Centric Driving Streaming Perception? The ASAP Benchmark

Are We Ready for Vision-Centric Driving Streaming Perception...

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

作者： Wang, Xiaofeng Zhu, Zheng Zhang, Yunpeng Huang, Guan Ye, Yun Xu, Wenbo Chen, Ziwei Wang, Xingang Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China PhiGent Robot Beijing Peoples R China Southeast Univ Dhaka Bangladesh

ISBN: (纸本)9798350301298

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://***/JeffWang987/ASAP.

关键词： Autonomous driving

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InternImage: Exploring Large-Scale vision Foundation Models with Deformable Convolutions

InternImage: Exploring Large-Scale Vision Foundation Models ...

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

作者： Wang, Wenhai Dai, Jifeng Chen, Zhe Huang, Zhenhang Li, Zhiqi Zhu, Xizhou Hu, Xiaowei Lu, Tong Lu, Lewei Li, Hongsheng Wang, Xiaogang Qiao, Yu Shanghai AI Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.

关键词： detection recognition: Categorization retrieval

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Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization

Where is my Wallet? Modeling Object Proposal Sets for Egocen...

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

作者： Xu, Mengmeng Li, Yanghao Fu, Cheng-Yang Ghanem, Bernard Xiang, Tao Perez-Rua, Juan-Manuel Meta AI Menlo Pk CA 94025 USA KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350301298

This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current query-conditioned model design and visual query datasets. Then, we directly tackle such biases at both frame and object set levels. Concretely, our method solves these issues by expanding limited annotations and dynamically dropping object proposals during training. Additionally, we propose a novel transformer-based module that allows for object-proposal set context to be considered while incorporating query information. We name our module Conditioned Contextual Transformer or CocoFormer. Our experiments show the proposed adaptations improve egocentric query detection, leading to a better visual query localization system in both 2D and 3D configurations. Thus, we can improve frame-level detection performance from 26.28% to 31.26% in AP, which correspondingly improves the VQ2D and VQ3D localization scores by significant margins. Our improved context-aware query object detector ranked first and second respectively in the VQ2D and VQ3D tasks in the 2nd Ego4D challenge. In addition to this, we showcase the relevance of our proposed model in the Few-Shot Detection (FSD) task, where we also achieve SOTA results. Our code is available at https: //***/facebookresearch/vq2d_cvpr.

关键词： detection recognition: Categorization retrieval

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Self-supervised Implicit Glyph Attention for Text recognition

Self-supervised Implicit Glyph Attention for Text Recognitio...

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

作者： Guan, Tongkun Gu, Chaochen Tu, Jingzheng Yang, Xue Feng, Qi Zhao, Yudi Shen, Wei Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9798350301298

The attention mechanism has become the de facto module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and or character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SIGA). SIGA delineates the glyph structures of text images by jointly self-supervised text segmentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks.

关键词： detection recognition: Categorization retrieval

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Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

Collaborative Noisy Label Cleaner: Learning Scene-aware Trai...

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

作者： Gan, Bei Shu, Xiujun Qiao, Ruizhi Wu, Haoqian Chen, Keyu Li, Hanjun Ren, Bo Tencent YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://***/TencentYoutuResearch/HighlightDetection-CLC.

关键词： vision applications and systems

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AltFreezing for More General Video Face Forgery Detection

AltFreezing for More General Video Face Forgery Detection

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

作者： Wang, Zhendong Bao, Jianmin Zhou, Wengang Wangi, Weilun Li, Hougiang Univ Sci & Technol China EELS Dept CAS Key Lab GIPAS Langfang Hebei Peoples R China Microsoft Res Asia Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection. A simple idea is to leverage a spatiotemporal model (3D ConvNet). However, we find that it may easily rely on one type of artifact and ignore the other. To address this issue, we present a novel training strategy called AltFreezing for more general face forgery detection. The AltFreezing aims to encourage the model to detect both spatial and temporal artifacts. It divides the weights of a spatiotemporal network into two groups: spatial-related and temporal-related. Then the two groups of weights are alternately frozen during the training process so that the model can learn spatial and temporal features to distinguish real or fake videos. Furthermore, we introduce various video-level data augmentation methods to improve the generalization capability of the forgery detection model. Extensive experiments show that our framework outperforms existing methods in terms of generalization to unseen manipulations and datasets.

关键词： accountability ethics in vision fairness privacy Transparency

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