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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging

HerosNet: Hyperspectral Explicable Reconstruction and Optima...

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

作者： Zhang, Xuanyu Zhang, Yongbing Xiong, Ruiqin Sun, Qilin Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Harbin Inst Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral cameras that are either slow, expensive, or bulky, reconstructing hyperspectral images (HSIs) from a low-budget snapshot measurement has drawn wide attention. By mapping a truncated numerical optimization algorithm into a network with a fixed number of phases, recent deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI) have achieved remarkable success. However, DUNs are far from reaching the scope of industrial applications limited by the lack of cross-phase feature interaction and adaptive parameter adjustment. In this paper, we propose a novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network for SCI, dubbed HerosNet, which includes several phases under the ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix and contextually adjust the step size in the gradient descent step, and hierarchically fuse and interact the hidden states of previous phases to effectively recover current HSI frames in the proximal mapping step. Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end to further improve the reconstruction performance. Finally, our HerosNet is validated to outperform the state-of-the-art methods on both simulation and real datasets by large margins. The source code is available at https://***/jianzhangcs/HerosNet.

关键词： Photography Deep learning Correlation Fuses Sensors pattern recognition Iterative methods

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Low-Rank Rescaled vision Transformer Fine-Tuning: A Residual Design Approach

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual...

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

作者： Dong, Wei Zhang, Xing Chen, Bihui Yang, Dawei Lin, Zhijun Yang, Qingsen Wang, Peng Yang, Yang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350353006

Parameter-efficient fine-tuning for pre-trained vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***.

关键词： Low-Rank Adaptation Parameter-efficient fine-tuning Singular Value Decomposition

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3D CNNs with Adaptive Temporal Feature Resolutions

3D CNNs with Adaptive Temporal Feature Resolutions

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

作者： Fayyaz, Mohsen Bahrami, Emad Diba, Ali Noroozi, Mehdi Adeli, Ehsan Van Gool, Luc Gall, Juergen Univ Bonn Bonn Germany Katholieke Univ Leuven Leuven Belgium Bosch Ctr Artificial Intelligence Renningen Baden Wurttembe Germany Stanford Univ Stanford CA 94305 USA Swiss Fed Inst Technol Zurich Switzerland Univ Bonn Comp Vis Grp Bonn Germany

ISBN: (纸本)9781665445092

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the stateof-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCFI01, and HMDB5I.

关键词： computer vision Three-dimensional displays Costs Adaptive systems computer architecture Computational efficiency pattern recognition

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The Devil Is in the Details: Window-based Attention for Image Compression

The Devil Is in the Details: Window-based Attention for Imag...

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

作者： Zou, Renjie Song, Chunfeng Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make MI use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://***/Googolxx/STF.

关键词： Representation learning Image coding Redundancy Rate-distortion Transformer cores Transformers Decoding

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Source-Free Domain Adaptation via Distribution Estimation

Source-Free Domain Adaptation via Distribution Estimation

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

作者： Ding, Ning Xu, Yixing Tang, Yehui Xu, Chao Wang, Yunhe Tao, Dacheng Peking Univ Sch Artificial Intelligence Key Lab Machine Percept MOE Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation. Firstly, we produce robust pseudo-labels for target data with spherical k-means clustering, whose initial class centers are the weight vectors (anchors) learned by the classifier of pretrained model. Furthermore, we propose to estimate the class-conditioned feature distribution of source domain by exploiting target data and corresponding anchors. Finally, we sample surrogate features from the estimated distribution, which are then utilized to align two domains by minimizing a contrastive adaptation loss function. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data.

关键词： Representation learning Data privacy computer vision Estimation Training data Benchmark testing Data models

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Not just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

Not just Compete, but Collaborate: Local Image-to-Image Tran...

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

作者： Kim, Daejin Khan, Mohammad Azam Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea Dhaka Power Distribut Co Ltd Dhaka Bangladesh

ISBN: (纸本)9781665445092

Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN.

关键词： Training computer vision Computational modeling computer architecture Predictive models Generative adversarial networks Generators

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Auto-Encoding Scene Graphs for Image Captioning 32

Auto-Encoding Scene Graphs for Image Captioning

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Xu Tang, Kaihua Zhang, Hanwang Cai, Jianfei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781728132938

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoderdecoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation "person on bike", it is natural to replace "on" with "ride" and infer "person riding bike on a road" even the "road" is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph - a directed graph (G) where an object node is connected by adjective nodes and relationship nodes - to represent the complex structural layout of both image (I) and sentence (S). In the textual domain, we use SGAE to learn a dictionary (D) that helps to reconstruct sentences in the S. G. D. S pipeline, where D encodes the desired language prior;in the vision-language domain, we use the shared D to guide the encoder-decoder in the I. G. D. S pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art 127.8 CIDEr-D on the Karpathy split, and a competitive 125.5 CIDEr-D (c40) on the official server even compared to other ensemble models. Code has been made available at: https://***/yangxuntu/SGAE.

关键词： Deep Learning vision + Language

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OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervi...

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

作者： Gidaris, Spyros Bursuc, Andrei Puy, Gilles Komodakis, Nikos Cord, Matthieu Perez, Patrick Valeo Ai Paris France Univ Crete Iraklion Greece Sorbonne Univ Paris France

ISBN: (纸本)9781665445092

Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although effective visual representations should indeed exhibit such invariances, there are other important characteristics, such as encoding contextual reasoning skills, for which alternative reconstruction-based approaches might be better suited. With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image. Our strategy performs an online training of both the teacher network (whose role is to generate the BoW targets) and the student network (whose role is to learn representations), along with an online update of the visual-words vocabulary (used for the BoW targets). This idea effectively enables fully online BoW-guided unsupervised learning. Extensive experiments demonstrate the interest of our BoW-based strategy, which surpasses previous state-of-the-art methods (including contrastive-based ones) in several applications. For instance, in downstream tasks such Pascal object detection, Pascal classification and Places205 classification, our method improves over all prior unsupervised approaches, thus establishing new state-of-the-art results that are also significantly better even than those of supervised pre-training. We provide the implementation code https://***/valeoai/obow.

关键词： Training Vocabulary Visualization Protocols Perturbation methods Object detection pattern recognition

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Investigating Tradeoffs in Real-World Video Super-Resolution

Investigating Tradeoffs in Real-World Video Super-Resolution

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

作者： Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage in-dispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency (Fig. 1). Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length trade-off. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset are publicly available at https://***/ckkelvinchan/RealBasicVSR.

关键词： Degradation Training computer vision Codes Computational modeling Superresolution Video sequences

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