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

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A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

A Realistic Evaluation of Semi-Supervised Learning for Fine-...

引用

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

作者： Su, Jong-Chyi Cheng, Zezhou Maji, Subhransu Univ Massachusetts Amherst Amherst MA 01003 USA

ISBN: (纸本)9781665445092

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that recently proposed SSL methods provide significant benefits, and can effectively use out-of-class data to improve performance when deep networks are trained from scratch. Yet their performance pales in comparison to a transfer learning baseline, an alternative approach for learning from a few examples. Furthermore, in the transfer setting, while existing SSL methods provide improvements, the presence of out-of-class is often detrimental. In this setting, standard fine-tuning followed by distillation-based self-training is the most robust. Our work suggests that semi-supervised learning with experts on realistic datasets may require different strategies than those currently prevalent in the literature.

关键词： Fungi Technological innovation Transfer learning Taxonomy Benchmark testing Semisupervised learning pattern recognition

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DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yuqing Wang Yizhi Wang Longhui Yu Yuesheng Zhu Zhouhui Lian Wangxuan Institute of Computer Technology Peking University China School of Electronic and Computer Engineering Peking University China

Vector font synthesis is a challenging and ongoing problem in the fields of computer vision and computer Graphics. The recently-proposed DeepVecFont [27] achieved state-of-the-art performance by exploiting information of both the image and sequence modalities of vector fonts. However, it has limited capability for handling long sequence data and heavily relies on an image-guided outline refinement post-processing. Thus, vector glyphs synthesized by DeepVecFont still often contain some distortions and artifacts and cannot rival human-designed results. To address the above problems, this paper proposes an enhanced version of DeepVecFont mainly by making the following three novel technical contributions. First, we adopt Transformers instead of RNNs to process sequential data and design a relaxation representation for vector outlines, markedly improving the model's capability and stability of synthesizing long and complex outlines. Second, we propose to sample auxiliary points in addition to control points to precisely align the generated and target Bézier curves or lines. Finally, to alleviate error accumulation in the sequential generation process, we develop a context-based self-refinement module based on another Transformer-based decoder to remove artifacts in the initially synthesized glyphs. Both qualitative and quantitative results demonstrate that the proposed method effectively resolves those intrinsic problems of the original DeepVecFont and outperforms existing approaches in generating English and Chinese vector fonts with complicated structures and diverse styles.

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PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Seg...

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

作者： Douillard, Arthur Chen, Yifu Dapogny, Arnaud Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Datakalab Paris France Valeoai Paris France

ISBN: (纸本)9781665445092

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r.t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks(1).

关键词： Learning systems computer vision Semantics Benchmark testing Predictive models Market research Stability analysis

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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

Linguistic Structures as Weak Supervision for Visual Scene G...

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

作者： Ye, Keren Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

Prior work in scene graph generation requires categorical supervision at the level of triplets-subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

关键词： Location awareness Visualization computer vision Blogs Linguistics pattern recognition Noise measurement

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Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging

Rotation Coordinate Descent for Fast Globally Optimal Rotati...

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

作者： Parra, Alvaro Chng, Shin-Fang Chin, Tat-Jun Eriksson, Anders Reid, Ian Univ Adelaide Sch Comp Sci Adelaide SA Australia Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9781665445092

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. However, generic solvers for SDP are rather slow in practice, even on rotation averaging instances of moderate size, thus developing specialised algorithms is vital. In this paper, we present a fast algorithm that achieves global optimality called rotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which solves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD directly maintains and updates all valid rotations throughout the iterations. This obviates the need to store a large dense semidefinite matrix. We mathematically prove the convergence of our algorithm and empirically show its superior efficiency over state-of-the-art global methods on a variety of problem configurations. Maintaining valid rotations also facilitates incorporating local optimisation routines for further speed-ups. Moreover, our algorithm is simple to implement(1).

关键词： computer vision Coordinate measuring machines Convex functions pattern recognition Noise measurement Velocity measurement Rotation measurement

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MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Tran...

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

作者： Wang, Huiyu Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detection, non-maximum suppression, thing-stuff merging, etc. Although these sub-tasks are tackled by area experts, they fail to comprehensively solve the target task. By contrast, our MaX-DeepLab directly predicts class-labeled masks with a mask transformer, and is trained with a panoptic quality inspired loss via bipartite matching. Our mask transformer employs a dual-path architecture that introduces a global memory path in addition to a CNN path, allowing direct communication with any CNN layers. As a result, MaX-DeepLab shows a significant 7.1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time. A small variant of MaX-DeepLab improves 3.0% PQ over DETR with similar parameters and M-Adds. Furthermore, MaX-DeepLab, without test time augmentation, achieves new state-of-the-art 51.3% PQ on COCO test-dev set.

关键词： computer vision Merging Pipelines computer architecture Transformers pattern recognition Task analysis

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Understanding Object Dynamics for Interactive Image-to-Video Synthesis

Understanding Object Dynamics for Interactive Image-to-Video...

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

作者： Blattmann, Andreas Milbich, Timo Dorkenwald, Michael Ommer, Bjoern Heidelberg Univ Interdisciplinary Ctr Sci Comp HCI Heidelberg Germany

ISBN: (纸本)9781665445092

What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks. Project page is available st https://***/3cxfa2L.

关键词： Training computer vision Limiting Shape Computational modeling Video sequences Data models

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Automated Log-Scale Quantization for Low-Cost Deep Neural Networks

Automated Log-Scale Quantization for Low-Cost Deep Neural Ne...

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

作者： Oh, Sangyun Sim, Hyeonuk Lee, Sugil Lee, Jongeun UNIST Dept Elect Engn Ulsan South Korea UNIST Dept Comp Sci & Engn Ulsan South Korea

ISBN: (纸本)9781665445092

Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly outperforms the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results.

关键词： Training Deep learning Image segmentation computer vision Quantization (signal) Semantics computer architecture

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Dynamic Inference with Grounding Based vision and Language Models

Dynamic Inference with Grounding Based Vision and Language M...

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conference on computer vision and pattern recognition (cvpr)

作者： Burak Uzkent Amanmeet Garg Wentao Zhu Keval Doshi Jingru Yi Xiaolong Wang Mohamed Omar Amazon Prime Video

Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, there exists a large amount of computational redundancy in these large models which skips their run-time efficiency. To address this problem, we propose dynamic inference for grounding based vision and language models conditioned on the input image-text pair. We first design an approach to dynamically skip multihead self-attention and feed forward network layers across two backbones and multimodal network. Additionally, we propose dynamic token pruning and fusion for two backbones. In particular, we remove redundant tokens at different levels of the backbones and fuse the image tokens with the language tokens in an adaptive manner. To learn policies for dynamic inference, we train agents using reinforcement learning. In this direction, we replace the CNN backbone in a recent grounding-based vision and language model, MDETR, with a vision transformer and call it ViTMDETR. Then, we apply our dynamic inference method to ViTMDETR, called D-ViTDMETR, and perform experiments on image-language tasks. Our results show that we can improve the run-time efficiency of the state-of-the-art models MDETR and GLIP by up to ~ 50% on Referring Expression Comprehension and Segmentation, and VQA with only maximum ~ 0.3% accuracy drop.

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General Multi-label Image Classification with Transformers

General Multi-label Image Classification with Transformers

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

作者： Lanchantin, Jack Wang, Tianlu Ordonez, Vicente Qi, Yanjun Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665445092

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the label state during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News-500, and CUB image datasets.

关键词： Training Deep learning Visualization Genomics Transformers Encoding pattern recognition

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