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共 20950 条记录，以下是421-430 订阅

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The Neglected Tails in vision-Language Models

The Neglected Tails in Vision-Language Models

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

作者： Parashar, Shubham Lin, Zhiqiu Liu, Tian Dong, Xiangjue Li, Yanan Ramanan, Deva Caverlee, James Kong, Shu Texas A&M Univ College Stn TX 77840 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Zhejiang Lab Hangzhou Peoples R China Univ Macau Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive accuracy on ImageNet (60-80%), its performance drops below 10% for more than ten concepts like night snake, presumably due to their limited presence in the pretraining data. However, measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets, such as LAION, exhibit a long-tailed concept distribution, yielding biased performance in VLMs. We also find that downstream applications of VLMs, including visual chatbots (e.g., GPT-4V) and text-to-image models (e.g., Stable Diffusion), often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs, we propose REtrieval-Augmented Learning (REAL). First, instead of prompting VLMs using the original class names, REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second, REAL trains a linear classifier on a small yet balanced set of pretraining data re-trieved using concept synonyms. REAL surpasses the previous zero-shot SOTA, using 400x less storage and 10,000x less training time!

关键词： Long tailed recognition vision-Language Models Zero-Shot recognition

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Real-World Mobile Image Denoising Dataset with Efficient Baselines

Real-World Mobile Image Denoising Dataset with Efficient Bas...

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

作者： Flepp, Roman Ignatov, Andrey Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Wurzburg Comp Vis Lab Wurzburg Germany AI Witchlabs Ltd Zollikerberg Switzerland

ISBN: (纸本)9798350353006

The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware, the mobile camera sensor area cannot be increased significantly due to physical constraints, leading to a pixel size of 0.6-2.0 mu m, which results in strong image noise even in moderate lighting conditions. In the era of deep learning, one can train a CNN model to perform robust image denoising. However, there is still a lack of a substantially diverse dataset for this task. To address this problem, we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400,000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally, we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore, we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results, while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs, demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors, showing its high generalizability. The datasets, code and models can be found on the official project website (1,2).

关键词： Computational Photography computer vision Dataset Deep Learning Denoising Denoising Dataset Efficient Denoising Model Image Denoising Image Enhancement Image Restoration Mobile Image Denoising

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

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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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InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models

InteractDiffusion: Interaction Control in Text-to-Image Diff...

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

作者： Hoe, Jiun Tian Jiang, Xudong Chan, Chee Seng Tan, Yap-Peng Hu, Weipeng Nanyang Technol Univ Sch EEE Singapore Singapore Univ Malaya CISiP Kuala Lumpur Malaysia

ISBN: (纸本)9798350353013;9798350353006

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions, enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications, such as creating realistic scenes with interacting characters. In this work, we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information, consisting of a triplet label (person, action, object) and corresponding bounding boxes. We propose a pluggable interaction control model, called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically, we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens, thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models, which outperforms existing baselines by a large margin in HOI detection score, as well as fidelity in FID and KID. Project page: https://***/interactdiffusion.

关键词： computer vision conditional image generation diffusion model generative ai human-object interaction image generation

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Modality-Collaborative Test-Time Adaptation for Action recognition

Modality-Collaborative Test-Time Adaptation for Action Recog...

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

作者： Xiong, Baochen Yang, Xiaoshan Song, Yaguang Wang, Yaowei Xu, Changsheng Chinese Acad Sci CASIA Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Video-based Unsupervised Domain Adaptation (VUDA) method improves the generalization of the video model, enabling it to be applied to action recognition tasks in different environments. However, these methods require continuous access to source data during the adaptation process, which are impractical in real scenarios where the source videos are not available with concerns in transmission efficiency or privacy issues. To address this problem, in this paper, we focus on the Multimodal Video Test-Time Adaptation (MVTTA) task. Existing image-based TTA methods cannot be directly applied to this task because videos have domain shifts in multimodal and temporal, which brings difficulties to adaptation. To address the above challenges, we pro-pose a Modality-Collaborative Test-Time Adaptation (MC-TTA) Network. MC-TTA contains maintain teacher and student memory banks respectively for generating pseudo-prototypes and target-prototypes. In the teacher model, we propose Self-assembled Source-friendly Feature Reconstruction (SSFR) to encourage the teacher memory bank to store features that are more likely to be consistent with the source distribution. Through multimodal prototype alignment and cross-modal relative consistency, our method can effectively alleviate domain shift in videos. We evaluate the proposed model on four public video datasets. The results show that our model outperforms existing state-of-the-art methods.

关键词： Multi-modal learning

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Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization

Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential ...

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

作者： Singha, Mainak Jha, Ankit Bose, Shirsha Nair, Ashwin Abdar, Moloud Banerjee, Biplab Aisin Corp Kariya Aichi Japan Indian Inst Technol Bombay Maharashtra India Tech Univ Munich Munich Germany IISER Thiruvananthapuram Thiruvananthapuram Kerala India Deakin Univ Geelong Vic Australia

ISBN: (纸本)9798350353006

We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls, we introduce ODG-CLIP, harnessing the semantic prowess of the vision-language model, CLIP. Our framework brings forth three primary innovations: Firstly, distinct from prevailing paradigms, we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples, and to train this, we employ a readily accessible stable diffusion model, elegantly generating proxy images for the open class. Secondly, aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity, we devise a novel visual style-centric prompt learning mechanism. Finally, we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains, especially for the shared classes. Through rigorous testing on diverse datasets, covering closed and open-set DG contexts, ODG-CLIP demonstrates clear supremacy, consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://***/mainaksingha01/ODG-CLIP.

关键词： open domain generalization vision-language models

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Logarithmic Lenses: Exploring Log RGB Data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

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

作者： Maxwell, Bruce A. Singhania, Sumegha Patel, Avnish Kumar, Rahul Fryling, Heather Li, Sihan Sun, Haonan He, Ping Li, Zewen Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors;differences and sums correspond to ratios and products. Fea-tures in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： computer vision data set image classification physics-based vision

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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

作者： Zhang, Quan Wang, Lei Patel, Vishal M. Xie, Xiaohua Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pazhou Lab HuangPu Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA

ISBN: (纸本)9798350353006

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： image recognition person re-identification transformer

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