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Decoupled Semantic Prototypes enable learning from diverse annotation types for semi-weakly segmentation in expert-driven domains

Decoupled Semantic Prototypes enable learning from diverse a...

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

作者： Reiss, Simon Seibold, Constantin Freytag, Alexander Rodner, Erik Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Carl Zeiss Oberkochen Germany Univ Appl Sci Berlin Berlin Germany

ISBN: (纸本)9798350301298

A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains. However, the transfer to expert-driven domains like microscopy applications or medical healthcare remains difficult as domain experts are a critical factor due to their limited availability for providing pixel-wise annotations. To enable affordable segmentation solutions for such domains, we need training strategies which can simultaneously handle diverse annotation types and are not bound to costly pixel-wise annotations. In this work, we analyze existing training algorithms towards their flexibility for different annotation types and scalability to small annotation regimes. We conduct an extensive evaluation in the challenging domain of organelle segmentation and find that existing semi- and semi-weakly supervised training algorithms are not able to fully exploit diverse annotation types. Driven by our findings, we introduce Decoupled Semantic Prototypes (DSP) as a training method for semantic segmentation which enables learning from annotation types as diverse as image-level-, point-, bounding box-, and pixel-wise annotations and which leads to remarkable accuracy gains over existing solutions for semi-weakly segmentation.

关键词： cell microscopy Medical and biological vision

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METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

METransformer: Radiology Report Generation by Transformer wi...

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

作者： Wang, Zhanyu Liu, Lingqiao Wang, Lei Zhou, Luping Univ Sydney Sydney NSW Australia Univ Adelaide Adelaide SA Australia Univ Wollongong Wollongong NSW Australia

ISBN: (纸本)9798350301298

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance.

关键词： and reasoning language vision

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Visual Atoms: Pre-training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-training Vision Transformers with Sinusoid...

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

作者： Takashima, Sora Hayamizu, Ryo Inoue, Nakamasa Kataoka, Hirokatsu Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350301298

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViTBase, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

关键词： Datasets and evaluation

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Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient vision Transformers

Sparsifiner: Learning Sparse Instance-Dependent Attention fo...

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

作者： Wei, Cong Duke, Brendan Jiang, Ruowei Aarabi, Parham Taylor, Graham W. Shkurti, Florian Univ Toronto Toronto ON Canada Univ Guelph Guelph ON N1G 2W1 Canada Modiface Inc Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350301298

vision Transformers (ViT) have shown competitive advantages in terms of performance compared to convolutional neural networks (CNNs), though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self-attention (MHSA) operations. However, such structured attention patterns limit the token-to-token connections to their spatial relevance, which disregards learned semantic connections from a full attention mask. In this work, we propose an approach to learn instance-dependent attention patterns, by devising a lightweight connectivity predictor module that estimates the connectivity score of each pair of tokens. Intuitively, two tokens have high connectivity scores if the features are considered relevant either spatially or semantically. As each token only attends to a small number of other tokens, the binarized connectivity masks are often very sparse by nature and therefore provide the opportunity to reduce network FLOPs via sparse computations. Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto frontier between FLOPs and top-1 accuracy on ImageNet compared to token sparsity. Our method reduces 48% similar to 69% FLOPs of MHSA while the accuracy drop is within 0.4%. We also show that combining attention and token sparsity reduces ViT FLOPs by over 60%.

关键词： Efficient and scalable vision

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Seeing What You Miss: vision-Language Pre-training with Semantic Completion Learning

Seeing What You Miss: Vision-Language Pre-training with Sema...

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

作者： Ji, Yatai Tu, Rongcheng Jiang, Jie Kong, Weijie Cai, Chengfei Zhao, Wenzhe Wang, Hongfa Yang, Yujiu Liu, Wei Tsinghua Univ Beijing Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

关键词： Multi-modal learning

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Leveraging per Image-Token Consistency for vision-Language Pre-training

Leveraging per Image-Token Consistency for Vision-Language P...

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

作者： Gou, Yunhao Ko, Tom Yang, Hansi Kwok, James Zhang, Yu Wang, Mingxuan Southern Univ Sci & Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China ByteDance Ai Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://***/gyhdog99/epic

关键词： Multi-modal learning

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Make Landscape Flatter in Differentially Private Federated Learning

Make Landscape Flatter in Differentially Private Federated L...

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

作者： Shi, Yifan Liu, Yingqi Wei, Kang Shen, Li Wang, Xueqian Tao, Dacheng Tsinghua Univ Shenzhen Peoples R China JD Explore Acad Beijing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharper loss landscape and have poorer weight perturbation robustness, resulting in severe performance degradation. To alleviate these issues, we propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance. From the theoretical perspective, we analyze in detail how DP-FedSAM mitigates the performance degradation induced by DP. Meanwhile, we give rigorous privacy guarantees with R ' enyi DP and present the sensitivity analysis of local updates. At last, we empirically confirm that our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.

关键词： accountability ethics in vision fairness privacy Transparency

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EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Vis...

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

作者： Wu, Yanmin Cheng, Xinhua Zhang, Renrui Cheng, Zesen Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, Scan-Refer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://***/yanmin-wu/EDA.

关键词： language reasoning vision

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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Visibility Constrained Wide-band Illumination Spectrum Desig...

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

作者： Niu, Muyao Li, Zhuoxiao Zhong, Zhihang Zheng, Yinqiang Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350301298

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://***/MyNiuuu/VCSD.

关键词： Physics-based vision and shape-from-X

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Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices

Abstract Visual Reasoning: An Algebraic Approach for Solving...

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

作者： Xu, Jingyi Vaidya, Tushar Wu, Yufei Chandra, Saket Lai, Zhangsheng Chong, Kai Fong Ernest Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore Singapore Polytech Singapore Singapore

ISBN: (纸本)9798350301298

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Grobner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall 93.2% accuracy, which significantly outperforms the current state-of-the-art accuracy of 77.0% and exceeds human performance at 84.4% accuracy.

关键词： and reasoning language vision

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