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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11897 条记录，以下是1761-1770 订阅

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Differentiable Stereopsis: Meshes from multiple views using differentiable rendering

Differentiable Stereopsis: Meshes from multiple views using ...

引用

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

作者： Goel, Shubham Gkioxari, Georgia Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and modern differentiable rendering to build an end-to-end model which predicts textured 3D meshes of objects with varying topologies and shape. We frame stereopsis as an optimization problem and simultaneously update shape and cameras via simple gradient descent. We run an extensive quantitative analysis and compare to traditional multi-view stereo techniques and state-of-the-art learning based methods. We show compelling reconstructions on challenging real-world scenes and for an abundance of object types with complex shape, topology and texture.(1)

关键词： Solid modeling Three-dimensional displays Shape Statistical analysis Predictive models Rendering (computer graphics) Cameras

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MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

MIST: Medical Image Segmentation Transformer with Convolutio...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rahman, Md Motiur Shokouhmand, Shiva Bhatt, Smriti Faezipour, Miad Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350318920;9798350318937

One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose a Medical Image Segmentation Transformer (MIST) incorporating a novel Convolutional Attention Mixing (CAM) decoder to address this issue. MIST has two parts- a pre-trained multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded feature representation is passed through the CAM decoder for segmenting the images. In the CAM decoder, an attention-mixer combining multi-head self-attention, spatial attention, and squeeze and excitation attention modules is introduced to capture long-range dependencies in all spatial dimensions. Moreover, to enhance spatial information gain, deep and shallow convolutions are used for feature extraction and receptive field expansion, respectively. The integration of low-level and high-level features from different network stages is enabled by skip connection, allowing MIST to suppress unnecessary information. The experiments show that our MIST transformer with CAM decoder outperforms the state-of-the-art models specifically designed for medical image segmentation on the ACDC and Synapse datasets. Our results also demonstrate that adding the CAM decoder with a hierarchical transformer improves the segmentation performance significantly. Our model with data and code is publicly available on GitHub(1).

关键词： Algorithms Applications Biomedical / healthcare / medicine Image recognition and understanding

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Diffuse and Restore: A Region-Adaptive Diffusion Model for Identity-Preserving Blind Face Restoration

Diffuse and Restore: A Region-Adaptive Diffusion Model for I...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Suin, Maitreya Nair, Nithin Gopalakrishnan Lau, Chun Pong Patel, Vishal M. Chellappa, Rama

ISBN: (纸本)9798350318920;9798350318937

Blind face restoration (BFR) from severely degraded face images in the wild is a highly ill-posed problem. Due to the complex unknown degradation, existing generative works typically struggle to restore realistic details when the input is of poor quality. Recently, diffusion-based approaches were successfully used for high-quality image synthesis. But, for BFR, maintaining a balance between the fidelity of the restored image and the reconstructed identity information is important. Minor changes in certain facial regions may alter the identity or degrade the perceptual quality. With this observation, we present a conditional diffusion-based framework for BFR. We alleviate the drawbacks of existing diffusion-based approaches and design a region-adaptive strategy. Specifically, we use an identity preserving conditioner network to recover the identity information from the input image as much as possible and use that to guide the reverse diffusion process, specifically for important facial locations that contribute the most to the identity. This leads to a significant improvement in perceptual quality as well as face-recognition scores over existing GAN and diffusion-based restoration models. Our approach achieves superior results to prior art on a range of real and synthetic datasets, particularly for severely degraded face images.

关键词： 3D Algorithms Algorithms Algorithms Biometrics body pose etc. face Generative models for image gesture Low-level and physics-based vision video

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Fine-tuning Image Transformers using Learnable Memory

Fine-tuning Image Transformers using Learnable Memory

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

作者： Sandler, Mark Zhmoginov, Andrey Vladymyrov, Max Jackson, Andrew Google Inc Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper we propose augmenting vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer we introduce a set of learnable embedding vectors that provide contextual information useful for specific datasets. We call these "memory tokens". We show that augmenting a model with just a handful of such tokens per layer significantly improves accuracy when compared to conventional head-only fine-tuning, and performs only slightly below the significantly more expensive full fine-tuning. We then propose an attention-masking approach that enables extension to new downstream tasks, with a computation reuse. In this setup in addition to being parameters efficient, models can execute both old and new tasks as a part of single inference at a small incremental cost.

关键词： Deep learning Adaptation models computer vision Costs Computational modeling Memory management Transformers

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Cross-view Transformers for real-time Map-view Semantic Segmentation

Cross-view Transformers for real-time Map-view Semantic Segm...

引用

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

作者： Zhou, Brady Krahenbuhl, Philipp UT Austin Austin TX 78712 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present cross-view transformers, an efficientattention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in realtime. The presented architecture performs at state-of-theart on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://***/bradyz/ cross_view_transformers.

关键词： Convolutional codes Image segmentation computer vision Navigation Semantics computer architecture Transformers

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Learning with Neighbor Consistency for Noisy Labels

Learning with Neighbor Consistency for Noisy Labels

引用

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

作者： Iscen, Ahmet Valmadre, Jack Arnab, Anurag Schmid, Cordelia Google Res Meylan France Univ Adelaide AIML Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-Webvision, Web vision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them.

关键词： Training Deep learning computer vision Stochastic processes computer architecture Semisupervised learning Prediction algorithms

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Rethink Cross-Modal Fusion in Weakly-Supervised Audio-Visual Video Parsing

Rethink Cross-Modal Fusion in Weakly-Supervised Audio-Visual...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Xu, Yating Hu, Conghui Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9798350318920;9798350318937

Existing works on weakly-supervised audio-visual video parsing adopt hybrid attention network (HAN) as the multi-modal embedding to capture the cross-modal context. It embeds the audio and visual modalities with a shared network, where the cross-attention is performed at the input. However, such an early fusion method highly entangles the two non-fully correlated modalities and leads to sub-optimal performance in detecting single-modality events. To deal with this problem, we propose the messenger-guided mid-fusion transformer to reduce the uncorrelated cross-modal context in the fusion. The messengers condense the full cross-modal context into a compact representation to only preserve useful cross-modal information. Furthermore, due to the fact that microphones capture audio events from all directions, while cameras only record visual events within a restricted field of view, there is a more frequent occurrence of unaligned cross-modal context from audio for visual event predictions. We thus propose cross-audio prediction consistency to suppress the impact of irrelevant audio information on visual event prediction. Experiments consistently illustrate the superior performance of our framework compared to existing state-of-the-art methods.

关键词： Algorithms Algorithms Video recognition and understanding vision + language and/or other modalities

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Reversible vision Transformers

Reversible Vision Transformers

引用

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

作者： Mangalam, Karttikeya Fan, Haoqi Li, Yanghao Wu, Chao-Yuan Xiong, Bo Feichtenhofer, Christoph Malik, Jitendra Facebook AI Res Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Reversible vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory footprint from the depth of the model, Reversible vision Transformers enable memory efficient scaling of transformer architectures. We adapt two popular models, namely vision Transformer and Multiscale vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible vision Transformers achieve a reduced memory footprint of up to 15.5x at identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 3.9 x over their non-reversible counterparts. Code and models are available at https://***/facebookresearch/mvit.

关键词： Training Adaptation models Visualization Computational modeling Memory management Object detection Transformers

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Understanding Dark Scenes by Contrasting Multi-Modal Observations

Understanding Dark Scenes by Contrasting Multi-Modal Observa...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Dong, Xiaoyu Yokoya, Naoto Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Understanding dark scenes based on multi-modal image data is challenging, as both the visible and auxiliary modalities provide limited semantic information for the task. Previous methods focus on fusing the two modalities but neglect the correlations among semantic classes when minimizing losses to align pixels with labels, resulting in inaccurate class predictions. To address these issues, we introduce a supervised multi-modal contrastive learning approach to increase the semantic discriminability of the learned multi-modal feature spaces by jointly performing cross-modal and intra-modal contrast under the supervision of the class correlations. The cross-modal contrast encourages same-class embeddings from across the two modalities to be closer and pushes different-class ones apart. The intra-modal contrast forces same-class or different-class embeddings within each modality to be together or apart. We validate our approach on a variety of tasks that cover diverse light conditions and image modalities. Experiments show that our approach can effectively enhance dark scene understanding based on multi-modal images with limited semantics by shaping semantic-discriminative feature spaces. Comparisons with previous methods demonstrate our state-of-the-art performance. Code and pretrained models are available at https://***/palmdong/SMMCL.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Foundation Model Assisted Weakly Supervised Semantic Segmentation

Foundation Model Assisted Weakly Supervised Semantic Segment...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yang, Xiaobo Gong, Xiaojin Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350318920;9798350318937

This work aims to leverage pre-trained foundation models, such as contrastive language-image pre-training (CLIP) and segment anything model (SAM), to address weakly supervised semantic segmentation (WSSS) using image-level labels. To this end, we propose a coarse-to-fine framework based on CLIP and SAM for generating high-quality segmentation seeds. Specifically, we construct an image classification task and a seed segmentation task, which are jointly performed by CLIP with frozen weights and two sets of learnable task-specific prompts. A SAM-based seeding (SAMS) module is designed and applied to each task to produce either coarse or fine seed maps. Moreover, we design a multi-label contrastive loss supervised by image-level labels and a CAM activation loss supervised by the generated coarse seed map. These losses are used to learn the prompts, which are the only parts need to be learned in our framework. Once the prompts are learned, we input each image along with the learned segmentation-specific prompts into CLIP and the SAMS module to produce high-quality segmentation seeds. These seeds serve as pseudo labels to train an off-the-shelf segmentation network like other two-stage WSSS methods. Experiments show that our method achieves the state-of-the-art performance on PASCAL VOC 2012 and competitive results on MS COCO 2014. Our code will be released upon acceptance.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

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