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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是171-180 订阅

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vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision Transformers are Parameter-Efficient Audio-Visual Lea...

引用

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

作者： Lin, Yan-Bo Sung, Yi-Lin Lei, Jie Bansal, Mohit Bertasius, Gedas UNC Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA

ISBN: (纸本)9798350301298

vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://***/project_page/LAVISH/

关键词： Multi-modal learning

来源：评论

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Robust Single Image Reflection Removal Against Adversarial Attacks

Robust Single Image Reflection Removal Against Adversarial A...

引用

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

作者： Song, Zhenbo Zhang, Zhenyuan Zhang, Kaihao Luo, Wenhan Fan, Zhaoxin Ren, Wenqi Lu, Jianfeng Nanjing Univ Sci & Technol Nanjing Peoples R China Australian Natl Univ Canberra ACT Australia Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.

关键词： Low-level vision

来源：评论

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REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

REVEAL: Retrieval-Augmented Visual-Language Pre-Training wit...

引用

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

作者： Hu, Ziniu Iscen, Ahmet Sun, Chen Wang, Zirui Chang, Kai-Wei Sun, Yizhou Schmid, Cordelia Ross, David A. Fathi, Alireza Univ Calif Los Angeles Los Angeles CA 90024 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. The project page of this work is ***.

关键词： and reasoning language vision

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Contrastive Mean Teacher for Domain Adaptive Object Detectors

Contrastive Mean Teacher for Domain Adaptive Object Detector...

引用

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

作者： Cao, Shengcao Joshi, Dhiraj Gui, Liang-Yan Wang, Yu-Xiong Univ Illinois Champaign IL 61820 USA IBM Res New York NY USA

ISBN: (纸本)9798350301298

Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in unsupervised domain adaptation for object detection, but it struggles with low-quality pseudo-labels. In this work, we identify the intriguing alignment and synergy between mean-teacher self-training and contrastive learning. Motivated by this, we propose Contrastive Mean Teacher (CMT) - a unified, general-purpose framework with the two paradigms naturally integrated to maximize beneficial learning signals. Instead of using pseudo-labels solely for final predictions, our strategy extracts object-level features using pseudo-labels and optimizes them via contrastive learning, without requiring labels in the target domain. When combined with recent mean-teacher self-training methods, CMT leads to new state-of-the-art target-domain performance: 51.9% mAP on Foggy Cityscapes, outperforming the previously best by 2.1% mAP. Notably, CMT can stabilize performance and provide more significant gains as pseudo-label noise increases.

关键词： detection recognition: Categorization retrieval

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Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation

Pseudo-label Guided Contrastive Learning for Semi-supervised...

引用

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9798350301298

Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intraclass compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: GitHub.

关键词： cell microscopy Medical and biological vision

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Transformer-based Unified recognition of Two Hands Manipulating Objects

Transformer-based Unified Recognition of Two Hands Manipulat...

引用

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

作者： Cho, Hoseong Kim, Chanwoo Kim, Jihyeon Lee, Seongyeong Ismayilzada, Elkhan Baek, Seungryul UNIST Ulsan South Korea

ISBN: (纸本)9798350301298

Understanding the hand-object interactions from an egocentric video has received a great attention recently. So far, most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to provide the unified understanding of two hands, an object and their interactions. In this paper, we propose the Transformer-based unified framework that provides better understanding of two hands manipulating objects. In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types. Afterwards, the action class defined by the hand-object interactions is predicted from the entire video based on the estimated information combined with the contact map that encodes the interaction between two hands and an object. Experiments are conducted on H2O and FPHA benchmark datasets and we demonstrated the superiority of our method achieving the state-of-the-art accuracy. Ablative studies further demonstrate the effectiveness of each proposed module.

关键词： body gesture Humans: Face movement pose

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Toward Accurate Post-Training Quantization for Image Super Resolution

Toward Accurate Post-Training Quantization for Image Super R...

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

作者： Tu, Zhijun Hu, Jie Chen, Hanting Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing EDSRx4 to 4-bit with 100 unlabeled images. Our code is available at both PyTorch and MindSpore.

关键词： Low-level vision

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LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

LVQAC: Lattice Vector Quantization Coupled with Spatially Ad...

引用

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

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity.

关键词： Low-level vision

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A Simple Framework for Text-Supervised Semantic Segmentation

A Simple Framework for Text-Supervised Semantic Segmentation

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

作者： Yi, Muyang Cui, Quan Wu, Hao Yang, Cheng Yoshie, Osamu Lu, Hongtao Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China Waseda Univ Tokyo Japan ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at ***/muyangyi/SimSeg.

关键词： and reasoning language vision

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FFF: Fragment-Guided Flexible Fitting for Building Complete Protein Structures

FFF: Fragment-Guided Flexible Fitting for Building Complete ...

引用

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

作者： Chen, Weijie Wang, Xinyan Wang, Yuhang DP Technol Ltd Camarillo CA 93012 USA Peking Univ Ctr Data Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.

关键词： detection recognition: Categorization retrieval

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