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Revisiting Temporal Modeling for CLIP-Based Image-to-Video Knowledge Transferring

Revisiting Temporal Modeling for CLIP-Based Image-to-Video K...

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conference on computer vision and pattern recognition (CVPR)

作者： Ruyang Liu Jingjia Huang Ge Li Jiashi Feng Xinglong Wu Thomas H. Li School of Electronic and Computer Engineering Peking University Peng Cheng Laboratory ByteDance Inc

Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention for their potential to improve visual representation learning in the video domain. In this paper, based on the CLIP model, we revisit temporal modeling in the context of image-to-video knowledge transferring, which is the key point for extending image-text pretrained models to the video domain. We find that current temporal modeling mechanisms are tailored to either high-level semantic-dominant tasks (e.g., retrieval) or low-level visual pattern-dominant tasks (e.g., recognition), and fail to work on the two cases simultaneously. The key difficulty lies in modeling temporal dependency while taking advantage of both high-level and low-level knowledge in CLIP model. To tackle this problem, we present Spatial-Temporal Auxiliary Network (STAN) - a simple and effective temporal modeling mechanism extending CLIP model to diverse video tasks. Specifically, to realize both low-level and high-level knowledge transferring, STAN adopts a branch structure with decomposed spatial-temporal modules that enable multi-level CLIP features to be spatial-temporally contextualized. We evaluate our method on two representative video tasks: Video-Text Retrieval and Video recognition. Extensive experiments demonstrate the superiority of our model over the state-of-the-art methods on various datasets, including MSR-VTT, DiDeMo, LSMDC, MSVD, Kinetics-400, and Something-Something- V2. Codes will be available at https://***/farewellthree/STAN

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Uncertainty-Aware vision-Based Metric Cross-View Geolocalization

Uncertainty-Aware Vision-Based Metric Cross-View Geolocaliza...

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conference on computer vision and pattern recognition (CVPR)

作者： Florian Fervers Sebastian Bullinger Christoph Bodensteiner Michael Arens Rainer Stiefelhagen Fraunhofer IOSB Karlsruhe Institute of Technology

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r.t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m.

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Analysis and Extensions of Adversarial Training for Video Classification

Analysis and Extensions of Adversarial Training for Video Cl...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kaleab A. Kinfu René Vidal Mathematical Institute for Data Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks that maximize the loss. However, the effectiveness of AT as a defense for video classification has not been thoroughly studied. Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size. Notably, we show that the optimal step size varies linearly with the attack budget. Our second contribution is to show that using a smaller (sub-optimal) attack budget at training time leads to a more robust performance at test time. Based on these findings, we propose three defenses against attacks with variable attack budgets. The first one, Adaptive AT, is a technique where the attack budget is drawn from a distribution that is adapted as training iterations proceed. The second, Curriculum AT, is a technique where the attack budget is increased as training iterations proceed. The third, Generative AT, further couples AT with a denoising generative adversarial network to boost robust performance. Experiments on the UCF101 dataset demonstrate that the proposed methods improve adversarial robustness against multiple attack types.

关键词： Training computer vision conferences Noise reduction Generative adversarial networks Robustness pattern recognition

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The Role of 'Sign' and 'Direction' of Gradient on the Performance of CNN

The Role of 'Sign' and 'Direction' of Gradient on the Perfor...

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

作者： Agarwal, Akshay Singh, Richa Vatsa, Mayank IIIT Delhi Delhi India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

State-of-the-art deep learning models have achieved superlative performance across multiple computer vision applications such as object recognition, face recognition, and digits/character classification. Most of these models highly rely on the gradient information flows through the network for learning. By utilizing this gradient information, a simple gradient sign method based attack is developed to fool the deep learning models. However, the primary concern with this attack is the perceptibility of noise for large degradation in classification accuracy. This research address the question of whether an imperceptible gradient noise can be generated to fool the deep neural networks? For this, the role of sign function in the gradient attack is analyzed. The analysis shows that without-sign function, i.e. gradient magnitude, not only leads to a successful attack mechanism but the noise is also imperceptible to the human observer. Extensive quantitative experiments performed using two convolutional neural networks validate the above observation. For instance, AlexNet architecture yields 63.54% accuracy on the CIFAR-10 database which reduces to 0.0% and 26.39% when sign (i.e., perceptible) and without-sign (i.e., imperceptible) of the gradient is utilized, respectively. Further, the role of the direction of the gradient for image manipulation is studied. When an image is manipulated in the positive direction of the gradient, an adversarial image is generated. On the other hand, if the opposite direction of the gradient is utilized for image manipulation, it is observed that the classification error rate of the CNN model is reduced. On AlexNet, the error rate of 36.46% reduces to 4.29% when images of CIFAR-10 are manipulated in the negative direction of the gradient. To explore other enthusiastic results on multiple object databases, including CIFAR-100, fashion-MNIST, and SVHN, please refer to the full paper.

关键词： Databases Machine learning Computational modeling Object recognition computer vision Training Optimization

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Uni-Perceiver v2: A Generalist Model for Large-Scale vision and vision-Language Tasks

Uni-Perceiver v2: A Generalist Model for Large-Scale Vision ...

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conference on computer vision and pattern recognition (CVPR)

作者： Hao Li Jinguo Zhu Xiaohu Jiang Xizhou Zhu Hongsheng Li Chun Yuan Xiaohua Wang Yu Qiao Xiaogang Wang Wenhai Wang Jifeng Dai CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Xi'an Jiaotong University SIGS Tsinghua University SenseTime Research Shanghai Artificial Intelligence Laboratory Tsinghua University

Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance. In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. The encoded representations are transformed by a task-agnostic decoder. Different tasks are formulated as a unified maximum likelihood estimation problem. We further propose an effective optimization technique named Task-Balanced Gradient Normalization to ensure stable multi-task learning with an unmixed sampling strategy, which is helpful for tasks requiring large batch-size training. After being jointly trained on various tasks, Uni-Perceiver v2 is capable of directly handling downstream tasks without any task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all existing generalist models in both versatility and performance. Meanwhile, compared with the commonly-recognized strong baselines that require tasks-specific fine-tuning, Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.

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PaCa-ViT: Learning Patch-to-Cluster Attention in vision Transformers

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Tran...

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conference on computer vision and pattern recognition (CVPR)

作者： Ryan Grainger Thomas Paniagua Xi Song Naresh Cuntoor Mun Wai Lee Tianfu Wu Department of ECE NC State An Independent Researcher BlueHalo

vision Transformers (ViTs) are built on the assumption of treating image patches as “visual tokens” and learn patch-to-patch attention. The patch embedding based tokenizer has a semantic gap with respect to its counterpart, the textual tokenizer. The patch-to-patch attention suffers from the quadratic complexity issue, and also makes it non-trivial to explain learned ViTs. To address these issues in ViT, this paper proposes to learn Patch-to-Cluster attention (PaCa) in ViT. Queries in our PaCa-ViT starts with patches, while keys and values are directly based on clustering (with a predefined small number of clusters). The clusters are learned end-to-end, leading to better tokenizers and inducing joint clustering-for-attention and attention-for-clustering for better and interpretable models. The quadratic complexity is relaxed to linear complexity. The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks. In experiments, the proposed methods are tested on ImageNet-1k image classification, MS-COCO object detection and instance segmentation and MIT-ADE20k semantic segmentation. Compared with the prior art, it obtains better performance in all the three benchmarks than the SWin [32] and the PVTs [47], [48] by significant margins in ImageNet-1k and MIT-ADE20k. It is also significantly more efficient than PVT models in MS-COCO and MIT-ADE20k due to the linear complexity. The learned clusters are semantically meaningful. Code and model checkpoints are available at https:/***/iVMCL/PaCaViT.

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computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

Computer-aided diagnosis system of lung carcinoma using Conv...

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

作者： Han, Fangjian Yu, Li Jiang, Yi Lensee Biotechnol Co Ltd Ningbo Peoples R China Cent South Univ Xiangya Hosp 2 Changsha Peoples R China

ISBN: (纸本)9781728193601

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.

关键词： Decision making

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Improving vision-and-Language Navigation by Generating Future-View Image Semantics

Improving Vision-and-Language Navigation by Generating Futur...

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conference on computer vision and pattern recognition (CVPR)

作者： Jialu Li Mohit Bansal UNC Chapel Hill

vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both Room-to-Room dataset and CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths. 1 1 Code is available at https://***/jialuli-luka/***.

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Token Contrast for Weakly-Supervised Semantic Segmentation

Token Contrast for Weakly-Supervised Semantic Segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Lixiang Ru Heliang Zheng Yibing Zhan Bo Du Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China JD Explore Academy China

Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure perception of CNN, CAM usually cannot identify the integral object regions. Though the recent vision Transformer (ViT) can remedy this flaw, we observe it also brings the over-smoothing issue, i.e., the final patch tokens incline to be uniform. In this work, we propose Token Contrast (ToCo) to address this issue and further explore the virtue of ViT for WSSS. Firstly, motivated by the observation that intermediate layers in ViT can still retain semantic diversity, we designed a Patch Token Contrast module (PTC). PTC supervises the final patch tokens with the pseudo token relations derived from intermediate layers, allowing them to align the semantic regions and thus yield more accurate CAM. Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics. CTC facilitates the representation consistency between uncertain local regions and global objects by contrasting their class tokens. Experiments on the PASCAL VOC and MS COCO datasets show the proposed ToCo can remarkably surpass other single-stage competitors and achieve comparable performance with state-of-the-art multi-stage methods. Code is available at https://***/rulixiang/ToCo.

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexandre Ramé Arthur Douillard Charles Ollion Sorbonne Universit&#x00E9Heuritech Heuritech

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That’s why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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