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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022"

共 11141 条记录，以下是21-30 订阅

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Non-autoregressive Sequence-to-Sequence vision-Language Models

Non-autoregressive Sequence-to-Sequence Vision-Language Mode...

引用

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

作者： Shi, Kunyu Dong, Qi Goncalves, Luis Tu, Zhuowen Soatto, Stefano AWS AI Labs Seattle WA 98101 USA

ISBN: (纸本)9798350353006

Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model, trained with a Query-CTC loss, that marginalizes over multiple inference paths in the decoder. This allows us to model the joint distribution of tokens, rather than restricting to conditional distribution as in an autoregressive model. The resulting model, NARVL, achieves performance on-par with its state-of-the-art autoregressive counterpart, but is faster at inference time, reducing from the linear complexity associated with the sequential generation of tokens to a paradigm of constant time joint inference.

关键词： CTC Non-autoregressive vision language models

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Spectral and Polarization vision: Spectro-polarimetric Real-world Dataset

Spectral and Polarization Vision: Spectro-polarimetric Real-...

引用

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

作者： Jeon, Yujin Cho, Eunsue Kim, Youngchan Moon, Yunseong Omer, Khalid Heide, Felix Baek, Seung-Hwan POSTECH Pohang South Korea Meta Menlo Pk CA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Image datasets are essential not only in validating existing methods in computer vision but also in developing new methods. Many image datasets exist, consisting of trichromatic intensity images taken with RGB cameras, which are designed to replicate human vision. However, polarization and spectrum, the wave properties of light that animals in harsh environments and with limited brain capacity often rely on, remain underrepresented in existing datasets. Although there are previous spectro-polarimetric datasets, they have insufficient object diversity, limited illumination conditions, linear-only polarization data, and inadequate image count. Here, we introduce two spectro-polarimetric datasets, consisting of trichromatic Stokes images and hyperspectral Stokes images. These datasets encompass both linear and circular polarization;they introduce multiple spectral channels;and they feature a broad selection of real-world scenes. With our dataset in hand, we analyze the spectro-polarimetric image statistics, develop efficient representations of such high-dimensional data, and evaluate spectral dependency of shape-from-polarization methods. As such, the proposed dataset promises a foundation for data-driven spectro-polarimetric imaging and vision research.

关键词： Computational Imaging

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Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models

Perturbing Attention Gives You More Bang for the Buck: Subtl...

引用

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

作者： Xu, Jingyao Lu, Yuetong Li, Yandong Lu, Siyang Wang, Dongdong Wei, Xiang Beijing Jiaotong Univ Beijing Peoples R China Google Res Mountain View CA USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9798350353006

Diffusion models ( DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high- quality and realistic data samples. However, their widespread use has also brought forth new challenges in model security, which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT, a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change, allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers, thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and out- performs baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.

关键词： Adversarial Attack computer vision

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A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neu...

引用

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

作者： Ma, Ruichen Qiao, Guanchao Liu, Yian Meng, Liwei Ning, Ning Liu, Yang Hu, Shaogang Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and non-hardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

关键词： Binary neural networks Hardware-friendly structure Multiplication-free structure pattern recognition

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Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

引用

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

作者： Park, Sungho Byun, Hy Eran Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Despite the remarkable success of vision Transformers (ViT) across diverse fields in computer vision, they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address this, Visual Prompt Tuning (VPT) incorporates learnable parameters in the input space of ViT. While freezing the ViT backbone and tuning only the prompts, it exhibits superior performances to full fine-tuning. However, despite the outstanding advantage, we point out that VPT may lead to serious unfairness in downstream classification. Initially, we investigate the causes of unfairness in VPT, identifying the biasedly pre-trained ViT as a principal factor. Motivated by this observation, we propose a Fair Visual Prompt Tuning (Fair-VPT) which removes biased information in the pre-trained ViT while adapting it to downstream classification tasks. To this end, we categorize prompts into "cleaner prompts" and "target prompts". Based on this, we encode the class token in two different ways by either masking or not masking the target prompts in the self-attention process. These encoded tokens are trained with distinct objective functions, resulting in the inclusion of different information in the target and cleaner prompts. Moreover, we introduce a disentanglement loss based on contrastive learning to further decorrelate them. In experiments across diverse benchmarks, the proposed method demonstrates the most superior performance in terms of balanced classification accuracy and fairness.

关键词： FAI Fair-VPT Fairness Large vision Model vision Transformer Visual Prompt Tuning ViT VPT

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Samsung Res China Nanjing Peoples R China Nankai Univ Tianjin Peoples R China

ISBN: (纸本)9798350365474

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and cvpr 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： computer vision flare removal image restoration

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Instance-Aware Group Quantization for vision Transformers

Instance-Aware Group Quantization for Vision Transformers

引用

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

作者： Jaehyeon, Moon Kim, Dohyung Cheon, Junyong Ham, Bumsub Yonsei Univ Seoul South Korea Articron Bogota Colombia

ISBN: (纸本)9798350353006

Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision transformers (ViTs), however, incurs severe performance degradation, mainly due to the differences in architectures between CNNs and ViTs. In particular, the distribution of activations for each channel vary drastically according to input instances, making PTQ methods for CNNs inappropriate for ViTs. To address this, we introduce instance-aware group quantization for ViTs (IGQ-ViT). To this end, we propose to split the channels of activation maps into multiple groups dynamically for each input instance, such that activations within each group share similar statistical properties. We also extend our scheme to quantize softmax attentions across tokens. In addition, the number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models, under a bit-operation (BOP) constraint. We show extensive experimental results on image classification, object detection, and instance segmentation, with various transformer architectures, demonstrating the effectiveness of our approach.

关键词： Post-training quantization ViT

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

引用

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

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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CPLIP: Zero-Shot Learning for Histopathology with Comprehensive vision-Language Alignment

CPLIP: Zero-Shot Learning for Histopathology with Comprehens...

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

作者： Javed, Sajid Mahmood, Arif Ganapathil, Iyyakutti Iyappan Dharej, Fayaz Ali Werghil, Naoufel Bennamoun, Mohammed Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol C2PS Abu Dhabi U Arab Emirates Informat Technol Univ Punjab Lahore Pakistan Univ Western Australia Perth WA Australia

ISBN: (纸本)9798350353006

This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks, CPLIP shows notable improvements in zero-shot learning scenarios, outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication, the code for CPLIP is available on GitHub at https://***/

关键词： Cancer Detection Computational Pathology Contrastive Loss Histopathology Many-to-Many vision-Language Alignment vision Language Modeling Whole Slide Image Zero-shot Learning

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Efficient vision-Language Pre-training by Cluster Masking

Efficient Vision-Language Pre-training by Cluster Masking

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

作者： Wei, Zihao Pan, Zixuan Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350353006

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training, we randomly mask clusters of visually similar image patches, as measured by their raw pixel intensities. This provides an extra learning signal, beyond the contrastive training itself, since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks, finding that it outperforms other masking strategies, such as FLIP, on the quality of the learned representation.

关键词： Contrastive Learning

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