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SDNet: An Extremely Efficient Portrait Matting Model via Self-Distillation

SDNet: An Extremely Efficient Portrait Matting Model via Sel...

引用

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

作者： Li, Ziwen Xu, Bo Xie, Jiake Tang, Yong Lu, Cheng OPPO Dongguan Peoples R China Xpeng Guangzhou Peoples R China Picup AI Hong Kong Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Most existing portrait matting models either require expensive auxiliary information or try to decompose the task into sub-tasks that are usually resource-hungry. These challenges limit its application on low-power computing devices. In addition, mobile networks tend to be less powerful than those cumbersome ones in feature representation mining. In this paper, we propose an extremely efficient portrait matting model via self-distillation (SDNet), that aims to provide a solution to performing accurate and effective portrait matting with limited computing resources. Our SDNet contains only 2M parameters, 2.2% of parameters of MGM, and 1.5% of that of Matteformer. We introduce the training pipeline of self-distillation that can improve our lightweight baseline model without any parameter addition, network modification, or over-parameterized teacher models which need well-pretraining. Extensive experiments demonstrate the effectiveness of our self-distillation method and the lightweight SDNet network. Our SDNet outperforms the state-of-the-art (SOTA) lightweight approaches on both synthetic and real-world images.

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

来源：评论

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Hard Sample-aware Consistency for Low-resolution Facial Expression recognition

Hard Sample-aware Consistency for Low-resolution Facial Expr...

引用

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

作者： Lee, Bokyeung Ko, Kyungdeuk Hong, Jonghwan Ko, Hanseok Korea Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Facial expression recognition (FER) plays a pivotal role in computer vision applications, encompassing video understanding and human-computer interaction. Despite notable advancements in FER, performance still falters when handling low-resolution facial images encountered in real-world scenarios and datasets. While consistency constraint techniques have garnered attention for generating robust convolutional neural network models that accommodate input variations through augmentation, their efficacy is diminished in the realm of low-resolution FER. This decline in performance can be attributed to augmented samples that networks struggle to extract expressive features. In this paper, we identify hard samples that cause an overfitting problem when considering various degrees of resolution and propose novel hard sample-aware consistency (HSAC) loss functions, which include combined attention consistency and label distribution learning. The combined attention consistency aligns an attention map from multi-scale low-resolution images with an appropriate target attention map by combining activation maps from high-resolution and flipped low-resolution images. We measure the classification difficulty for low-resolution face images and adaptively apply label distribution learning by combining the original target and predictions of high-resolution input. Our HSAC empowers the network to achieve generalization by effectively managing hard samples. Extensive experiments on various FER datasets demonstrate the superiority of our proposed method over existing approaches for multi-scale low-resolution images. Furthermore, we achieved a new state-of-the-art performance of 90.97% on the original RAF-DB dataset.

关键词： Algorithms Applications Image recognition and understanding Psychology and cognitive science

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Think before You Simulate: Symbolic Reasoning to Orchestrate Neural Computation for Counterfactual Question Answering

Think before You Simulate: Symbolic Reasoning to Orchestrate...

引用

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

作者： Ishay, Adam Yang, Zhun Lee, Joohyung Kang, Ilgu Lim, Dongjae Arizona State Univ Tempe AZ 85287 USA Samsung Res Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Causal and temporal reasoning about video dynamics is a challenging problem. While neuro-symbolic models that combine symbolic reasoning with neural-based perception and prediction have shown promise, they exhibit limitations, especially in answering counterfactual questions. This paper introduces a method to enhance a neuro-symbolic model for counterfactual reasoning, leveraging symbolic reasoning about causal relations among events. We define the notion of a causal graph to represent such relations and use Answer Set Programming (ASP), a declarative logic programming method, to find how to coordinate perception and simulation modules. We validate the effectiveness of our approach on two benchmarks, CLEVRER and CRAFT. Our enhancement achieves state-of-the-art performance on the CLEVRER challenge, significantly outperforming existing models. In the case of the CRAFT benchmark, we leverage a large pre-trained language model, such as GPT-3.5 and GPT-4, as a proxy for a dynamics simulator. Our findings show that this method can further improve its performance on counterfactual questions by providing alternative prompts instructed by symbolic causal reasoning.

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

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On Manipulating Scene Text in the Wild with Diffusion Models

On Manipulating Scene Text in the Wild with Diffusion Models

引用

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

作者： Santoso, Joshua Simon, Christian Williem Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9798350318920;9798350318937

Diffusion models have gained attention for image editing yielding impressive results in text-to-image tasks. On the downside, one might notice that generated images of stable diffusion models suffer from deteriorated details. This pitfall impacts image editing tasks that require information preservation e.g., scene text editing. As a desired result, the model must show the capability to replace the text on the source image to the target text while preserving the details e.g., color, font size, and background. To leverage the potential of diffusion models, in this work, we introduce Diffusion-BasEd Scene Text manipulation Network so-called DBEST. Specifically, we design two adaptation strategies, namely one-shot style adaptation and text-recognition guidance. In experiments, we thoroughly assess and compare our proposed method against state-of-the-arts on various scene text datasets, then provide extensive ablation studies for each granularity to analyze our performance gain. Also, we demonstrate the effectiveness of our proposed method to synthesize scene text indicated by competitive Optical Character recognition (OCR) accuracy. Our method achieves 94.15% and 98.12% on COCO-text and ICDAR2013 datasets for character-level evaluation.

关键词： 3D Algorithms Algorithms etc. Generative models for image video vision + language and/or other modalities

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Conditional Velocity Score Estimation for Image Restoration

Conditional Velocity Score Estimation for Image Restoration

引用

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

作者： Shi, Ziqiang Liu, Rujie Fujitsu R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

This paper proposes a new image restoration method by introducing a velocity variable on top of the data position during recovery. Under the guidance of the degraded image, it can effectively and dynamically control the direction of the diffusion path in the reverse-time stochastic differential equation (SDE). So the crucial factor is how to combine the degraded signal as a guide in this second-order reverse process with velocity, especially in the moving direction as a diffusion path. To this end, we propose a conditional velocity score approximation (CVSA) method based on the Bayesian principle to approximate the true posterior conditional velocity score by the sum of a priori conditional velocity score and an observation velocity score of the degraded measurement at the current moment. Our method is versatile from two perspectives. It can be used for both non-blind restoration and blind restoration. At the same time, there is almost no requirement for the degradation operator, and both linear and nonlinear tasks are acceptable. In non-blind restoration, including deblurring, inpainting, super-resolution, phase retrieval, and blind restoration, such as deblurring experiments, CVSA is better than other methods and achieves a new state-of-the-art.

关键词： 3D Algorithms Algorithms Algorithms etc. Generative models for image Image recognition and understanding Low-level and physics-based vision video

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Disentangled Pre-training for Image Matting

Disentangled Pre-training for Image Matting

引用

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

作者： Li, Yanda Huang, Zilong Yu, Gang Chen, Ling Wei, Yunchao Jiao, Jianbo Univ Technol Sydney Sydney NSW Australia Tencent Shenzhen Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9798350318920;9798350318937

Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by proposing a self-supervised pre-training approach that can leverage infinite numbers of data to boost the matting performance. The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective. The pre-trained model is then used as an initialisation of the downstream matting task for fine-tuning. Extensive experimental evaluations show that the proposed approach outperforms both the state-of-the-art matting methods and other alternative self-supervised initialisation approaches by a large margin. We also show the robustness of the proposed approach over different backbone architectures. Our project page is available at https://***/dpt_mat/.

关键词： Algorithms Algorithms Image recognition and understanding Low-level and physics-based vision

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CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free

CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semant...

引用

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

作者： Wysoczanska, Monika Ramamonjisoa, Michael Trzcinski, Tomasz Simeoni, Oriane Warsaw Univ Technol Warsaw Poland Meta AI New York NY USA Valeo Ai Prague Czech Republic Tooplooxx Wroclaw Poland IDEAS NCBR Warsaw Poland

ISBN: (纸本)9798350318920;9798350318937

The emergence of CLIP has opened the way for open-world image perception. The zero-shot classification capabilities of the model are impressive but are harder to use for dense tasks such as image segmentation. Several methods have proposed different modifications and learning schemes to produce dense output. Instead, we propose in this work an open-vocabulary semantic segmentation method, dubbed CLIP-DIY, which does not require any additional training or annotations, but instead leverages existing unsupervised object localization approaches. In particular, CLIP-DIY is a multi-scale approach that directly exploits CLIP classification abilities on patches of different sizes and aggregates the decision in a single map. We further guide the segmentation using foreground/background scores obtained using unsupervised object localization methods. With our method, we obtain state-of-the-art zero-shot semantic segmentation results on PASCAL VOC and perform on par with the best methods on COCO.

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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FUSECAP: Leveraging Large Language Models for Enriched Fused Image Captions

FUSECAP: Leveraging Large Language Models for Enriched Fused...

引用

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

作者： Rotstein, Noam Bensaid, David Brody, Shaked Ganz, Roy Kimmel, Ron Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9798350318920;9798350318937

The advent of vision-language pre-training techniques enhanced substantial progress in the development of models for image captioning. However, these models frequently produce generic captions and may omit semantically important image details. This limitation can be traced back to the image-text datasets;while their captions typically offer a general description of image content, they frequently omit salient details. Considering the magnitude of these datasets, manual reannotation is impractical, emphasizing the need for an automated approach. To address this challenge, we leverage existing captions and explore augmenting them with visual details using "frozen" vision experts including an object detector, an attribute recognizer, and an Optical Character Recognizer (OCR). Our proposed method, FUSECAP, fuses the outputs of such vision experts with the original captions using a large language model (LLM), yielding comprehensive image descriptions. We automatically curate a training set of 12M image-enriched caption pairs. These pairs undergo extensive evaluation through both quantitative and qualitative analyses. Subsequently, this data is utilized to train a captioning generation BLIP-based model. This model outperforms current state-of-the-art approaches, producing more precise and detailed descriptions, demonstrating the effectiveness of the proposed data-centric approach. We release this large-scale dataset of enriched image-caption pairs for the community.

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

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Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data

Reverse Knowledge Distillation: Training a Large Model using...

引用

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

作者： Nasser, Sahar Almahfouz Gupte, Nihar Sethi, Amit Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9798350318920;9798350318937

Retinal image matching (RIM) plays a crucial role in monitoring disease progression and treatment response as retina is the only tissue where blood vessels can be directly observed. However, datasets with matched keypoints between temporally separated pairs of images are not available in abundance to train transformer-based models. Firstly, we release keypoint annotations for retinal images from multiple datasets to aid further research on RIM. Secondly, we propose a novel approach based on reverse knowledge distillation to train large models with limited data while preventing overfitting. We propose architectural modifications to a CNN-based semi-supervised method called SuperRetina [22] that helps improve its results on a publicly available dataset. We train a computationally heavier model based on a vision transformer encoder, utilizing the lighter CNN-based model. This approach, which we call reverse knowledge distillation (RKD), further improves the matching results even though it contrasts with the conventional knowledge distillation where lighter models are trained based on heavier ones is the norm. Further, we show that our technique generalizes to other domains, such as facial landmark matching.

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

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Temporal Context Enhanced Referring Video Object Segmentation

Temporal Context Enhanced Referring Video Object Segmentatio...

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

作者： Hu, Xiao Hampiholi, Basavaraj Neumann, Heiko Lang, Jochen Univ Ottawa Ottawa ON Canada Ulm Univ Ulm Germany

ISBN: (纸本)9798350318920;9798350318937

The goal of Referring Video Object Segmentation is to extract an object from a video clip based on a given expression. While previous methods have utilized the transformer's multi-modal learning capabilities to aggregate information from different modalities, they have mainly focused on spatial information and paid less attention to temporal information. To enhance the learning of temporal information, we propose TCE-RVOS with a novel frame token fusion (FTF) structure and a novel instance query transformer (IQT). Our technical innovations maximize the potential information gain of videos over single images. Our contributions also include a new classification of two widely used validation datasets for investigation of challenging cases. Our experimental results demonstrate that TCE-RVOS effectively captures temporal information and outperforms the previous state-of-the-art methods by increasing the J&F score by 4.0 and 1.9 points using ResNet-50 and VSwin-Tiny as the backbone on Ref-Youtube-VOS, respectively, and +2.0 mAP on A2D-Sentences dataset by using VSwin-Tiny backbone. The code is available at https://***/haliphinx/TCE-RVOS

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

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