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CREPE: Can vision-Language Foundation Models Reason Compositionally?

CREPE: Can Vision-Language Foundation Models Reason Composit...

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

作者： Ma, Zixian Hong, Jerry Gul, Mustafa Omer Ciandhi, Mona Geo, Irena krishna, Ranjay Stanford Univ Stanford CA 94305 USA Cornell Univ Ithaca NY USA Univ Penn Philadelphia PA USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350301298

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that-across 7 architectures trained with 4 algorithms on massive datasets-they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over 370K image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate 325K, 316K, and 309K hard negative captions for a subset of the pairs. To test productivity, CREPE contains 17K image-text pairs with nine different complexities plus 278K hard negative captions with atomic, swapping and negation foils. The datasets are generated by repurposing the Visual Genome scene graphs and region descriptions and applying handcrafted templates and GPT-3. For systematicity, we find that model performance decreases consistently when novel compositions dominate the retrieval set, with Recall@1 dropping by up to 9%. For productivity, models' retrieval success decays as complexity increases, frequently nearing random chance at high complexity. These results hold regardless of model and training dataset size.

关键词： language reasoning vision

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FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression recognition in Videos

FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expres...

引用

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

作者： Wang, Yan Sun, Yixuan Huang, Yiwen Liu, Zhongying Gao, Shuyong Zhang, Wei Ge, Weifeng Zhang, Wenqiang Fudan Univ Acad Engn & Technol Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Happy" expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frame-works were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on https://***/wangyanckxx/FERV39k.

关键词： computer vision Image recognition Face recognition Manuals computer architecture Benchmark testing Robustness

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Pseudo-labels for Supervised Learning on Dynamic vision Sensor Data, Applied to Object Detection under Ego-motion 31

Pseudo-labels for Supervised Learning on Dynamic Vision Sens...

引用

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

作者： Chen, Nicholas F. Y. DSO Natl Labs 12 Sci Pk Dr Singapore 118225 Singapore

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.

关键词： vision sensors Cameras Vehicle dynamics Object detection Voltage control Supervised learning Automobiles

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IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

IoU Attack: Towards Temporally Coherent Black-Box Adversaria...

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

作者： Jia, Shuai Song, Yibing Ma, Chao Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781665445092

Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers. Assuming that the model structures of deep trackers are known, a variety of white-box attack approaches to visual tracking have demonstrated promising results. However, the model knowledge about deep trackers is usually unavailable in real applications. In this paper, we propose a decision-based black-box attack method for visual object tracking. In contrast to existing black-box adversarial attack methods that deal with static images for image classification, we propose IoU attack that sequentially generates perturbations based on the predicted IoU scores from both current and historical frames. By decreasing the IoU scores, the proposed attack method degrades the accuracy of temporal coherent bounding boxes (i.e., object motions) accordingly. In addition, we transfer the learned perturbations to the next few frames to initialize temporal motion attack. We validate the proposed IoU attack on state-of-the-art deep trackers (i.e., detection based, correlation filter based, and long-term trackers). Extensive experiments on the benchmark datasets indicate the effectiveness of the proposed IoU attack method. The source code is available at https://***/vision-SJTU/IoUattack.

关键词： Deep learning Visualization computer vision Correlation Codes Perturbation methods Robustness

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ADAPT: vision-Language Navigation with Modality-Aligned Action Prompts

ADAPT: Vision-Language Navigation with Modality-Aligned Acti...

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

作者： Lin, Bingqian Zhu, Yi Chen, Zicong Liang, Xiwen Liu, Jianzhuang Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like "walk past the chair". When starting navigation, the instruction-related action prompt set is retrieved from a pre-built action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multi-layer transformer for action prediction. To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CUP) model which has powerful cross-modality alignment ability A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially. Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods.

关键词： Visualization computer vision Adaptation models Navigation Transformers Nonhomogeneous media pattern recognition

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Vector Quantized Diffusion Model for Text-to-Image Synthesis

Vector Quantized Diffusion Model for Text-to-Image Synthesis

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

作者： Gu, Shuyang Chen, Dong Bao, Jianmin Wen, Fang Zhang, Bo Chen, Dongdong Yuan, Lu Guo, Baining Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Redmond WA 98052 USA Microsoft Cloud AI Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-andreplace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality. The code and models are available at https://***/cientgu/VQ-Diffusion.

关键词： Image quality computer vision Image resolution Image synthesis Computational modeling Noise reduction computer architecture

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Flexible-C^m GAN: Towards Precise 3D Dose Prediction in Radiotherapy

Flexible-C<SUP>m</SUP> GAN: Towards Precise 3D Dose Predicti...

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

作者： Gao, Riqiang Lou, Bin Xu, Zhoubing Comaniciu, Dorin Kamen, Ali Siemens Healthineers Digital Technol & Innovat Princeton NJ 08540 USA

ISBN: (纸本)9798350301298

Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-Cm GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-Cm GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches.

关键词： cell microscopy Medical and biological vision

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TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions

TransWeather: Transformer-based Restoration of Images Degrad...

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

作者： Valanarasu, Jeya Maria Jose Yasarla, Rajeev Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to remove all the weather conditions at once. However;it has a large number of parameters as it uses multiple encoders to cater to each weather removal task and still has scope for improvement in its performance. In this work, we focus on developing an efficient solution for the all adverse weather removal problem. To this end, we propose TransWeather, a transformer-based end-to-end model with just a single encoder and a decoder that can restore an image degraded by any weather condition. Specifically, we utilize a novel transformer encoder using intra-patch transformer blocks to enhance attention inside the patches to effectively remove smaller weather degradations. We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand. TransWeather achieves significant improvements across multiple test datasets over both All-in-One network as well as methods fine-tuned for specific tasks. TransWeather is also validated on real world test images and found to be more effective than previous methods. Implementation code can be found in the supplementary document.

关键词： Degradation Visualization Codes Machine vision Snow Transformers Image restoration

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Positional Encoding as Spatial Inductive Bias in GANs

Positional Encoding as Spatial Inductive Bias in GANs

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

作者： Xu, Rui Wang, Xintao Chen, Kai Zhou, Bolei Loy, Chen Change Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improves SinGAN for more versatile image manipulation.

关键词： Training computer vision Image coding Image synthesis computer architecture Generators Encoding

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Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning

Domain-aware Visual Bias Eliminating for Generalized Zero-Sh...

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

作者： Min, Shaobo Yao, Hantao Xie, Hongtao Wang, Chaoqun Zha, Zheng-Jun Zhang, Yongdong Univ Sci & Technol China Hefei Anhui Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781728171685

Generalized zero-shot learning aims to recognize images from seen and unseen domains. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem. In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately. Specifically, we explore cross-attentive second-order visual statistics to compact the semantic-free representation, and design an adaptive margin Softmax to maximize inter-class divergences. Thus, the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images, i.e., domain detection, based on the predicted class entropy. For unseen images, we automatically search an optimal semantic-visual alignment architecture, rather than manual designs, to predict unseen classes. With accurate domain detection, the biased recognition problem towards the seen domain is significantly reduced. Experiments on five benchmarks for classification and segmentation show that DVBE outperforms existing methods by averaged 5.7% improvement.

关键词： Semantics

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