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LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

LAKE-RED: Camouflaged Images Generation by Latent Background...

引用

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

作者： Zhao, Pancheng Xu, Peng Qin, Pengda Fan, Deng-Ping Zhang, Zhicheng Jia, Guoli Zhou, Bowen Yang, Jufeng Nankai Univ Coll Comp Sci VCIP Tianjin Peoples R China Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China Nankai Univ Coll Comp Sci DISSec Tianjin Peoples R China Nankai Int Adv Res Inst Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying the background manually, thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper, we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge, our contributions mainly include: (1) For the first time, we propose a camouflaged generation paradigm that does not need to receive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation, in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly, to alleviate the task-specific challenges. Moreover, our method is not restricted to specific foreground targets or backgrounds, offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches, generating more realistic camouflage images. Our source code is released on https://***/PanchengZhao/LAKE-RED.

关键词： camouflage object detection camouflaged vision perception diffusion generative model knowledge retrieval reasoning enhancement

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ST2ST: Self-Supervised Test-time Adaptation for Video Action recognition

ST2ST: Self-Supervised Test-time Adaptation for Video Action...

引用

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

作者： Fahim, Masud An-Nur Islam Innat, Mohammed Boutellier, Jani Univ Vaasa Vaasa Finland Khulna Univ Engn & Technol KUET Khulna Bangladesh

ISBN: (纸本)9798350365474

The performance of trained deep neural network (DNN) models relies on the assumption that the test data has largely the same feature distribution as the training data. In deployed video recognition systems, the feature distribution of acquired samples can however become shifted due to environmental conditions (rain, lighting variations) or technological factors such as lossy data compression. To improve action recognition performance under feature distribution shifts, we propose a simple test-time self-distillation strategy where the DNN model goes through an intra-video logit minimization phase. As a result, the model can update its predictions for the given input. The proposed approach is agnostic to the neural network type (CNN, transformer) and applies to various action recognition models. In contrast to many test-time adaption studies, the proposed approach does not require access to the training data. The performance of the proposed method is evaluated with multiple state-of-the-art action recognition models and widely used benchmark datasets Kinetics-400 and Something-Something V2.

关键词： Action recogniton Self-Supervised TTA

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ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

ChAda-ViT : Channel Adaptive Attention for Joint Representat...

引用

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

作者： Bourriez, Nicolas Bendidi, Ihab Cohen, Ethan Watkinson, Gabriel Sanchez, Maxime Bollot, Guillaume Genovesio, Auguste Ecole Normale Super PSL Paris France Minos Biosci Paris France Synsight Eviy France

ISBN: (纸本)9798350353006

Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment. Importantly, the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB, as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field, and current solutions mostly focus on intra-channel spatial attention, often ignoring the relationship between channels, yet crucial in most biological applications. Importantly, the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study, we propose ChAda-ViT, a novel Channel Adaptive vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number, order and type of channels. We also introduce IDR-Cell100k, a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities, with a multitude of channel types, and counts varying from 1 to 10 per experiment. Our architecture, trained in a self-supervised manner, outperforms existing approaches in several biologically relevant downstream tasks. Additionally, it can be used to bridge the gap for the first time between assays with different microscopes, channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. [GRAPHICS] .

关键词： Channel Adaptive Channel Embeddings Computational Biology computer vision Deep Learning Inter-Channel Attention Long Context Microscopy Imaging Self Supervised Learning Transformers vision Transformers

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LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

LayoutDM: Discrete Diffusion Model for Controllable Layout G...

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

作者： Inoue, Naoto Kikuchi, Kotaro Simo-Serra, Edgar Otani, Mayu Yamaguchi, Kota CyberAgent Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350301298

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.(1)

关键词： vision + graphics

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Multiclass Confidence and Localization Calibration for Object Detection

Multiclass Confidence and Localization Calibration for Objec...

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

作者： Pathiraja, Bimsara Gunawardhana, Malitha Khan, Muhammad Haris Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make overconfident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and box localization by leveraging their predictive uncertainties. We perform extensive experiments on several in-domain and out-of-domain detection benchmarks. Results demonstrate that our proposed train-time calibration method consistently outperforms several baselines in reducing calibration error for both in-domain and out-of-domain predictions. Our code and models are available at https: //***/bimsarapathiraja/MCCL

关键词： vision applications and systems

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Plateau-reduced Differentiable Path Tracing

Plateau-reduced Differentiable Path Tracing

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

作者： Fischer, Michael Ritschel, Tobias UCL London England

ISBN: (纸本)9798350301298

Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function, that maps scene parameters to images, with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-reduced gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on. Our code is at ***/mfischerucl/prdpt.

关键词： vision + graphics

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MoPE-CLIP: Structured Pruning for Efficient vision-Language Models with Module-wise Pruning Error Metric

MoPE-CLIP: Structured Pruning for Efficient Vision-Language ...

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

作者： Lin, Haokun Bai, Haoli Liu, Zhili Hou, Lu Sung, Muyi Song, Linqi Wei, Ying Sun, Zhenan Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci CRIPAC Beijing Peoples R China Chinese Acad Sci MAIS Inst Automat Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China City Univ Hong Kong Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. Recent efforts for VLP compression either adopt uni-modal compression metrics resulting in limited performance or involve costly mask-search processes with learnable masks. In this paper, we first propose the Module-wise Pruning Error (MoPE) metric, accurately assessing CLIP module importance by performance decline on cross-modal tasks. Using the MoPE metric, we introduce a unified pruning framework applicable to both pre-training and task-specific fine-tuning compression stages. For pre-training, MoPE-CLIP effectively leverages knowledge from the teacher model, significantly reducing pre-training costs while maintaining strong zero-shot capabilities. For fine-tuning, consecutive pruning from width to depth yields highly competitive task-specific models. Extensive experiments in two stages demonstrate the effectiveness of the MoPE metric, and MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.

关键词： CLIP Model Compression Structured Pruning vision-Language Models

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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

作者： Yu, Jun Wei, Zhihong Cai, Zhongpeng Zhao, Gongpeng Zhang, Zerui Wang, Yongqi Xie, Guochen Zhu, Jichao Zhu, Wangyuan Liu, Qingsong Liang, Jiaen Univ Sci & Technol China Hefei Peoples R China Unisound AI Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at cvpr2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model's generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： Facial Expression recognition Semi-Supervised Learning Temporal Modeling

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ViLa-MIL: Dual-scale vision-Language Multiple Instance Learning for Whole Slide Image Classification

ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learn...

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

作者： Shi, Jiangbo Li, Chen Gong, Tieliang Zheng, Yefeng Fu, Huazhu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Jarvis Res Ctr Tencent YouTu Lab Shenzhen Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350353006

Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However, these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides, which are easily affected by variations in data distribution. Recently, vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However, the previous text prompt lacks the consideration of pathological prior knowledge, therefore does not substantially boost the model's performance. Moreover, the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems, we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically, we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to pro-cess WSI efficiently, for the image branch, we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype;for the text branch, we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL.

关键词： Histopathology Multiple Instance Learning vision-Language Models Whole Slide Image Classification

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

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

作者： Koyun, Onur Can Toreyin, Behcet Ugur Istanbul Tech Univ Dept Artificial Intelligence & Data Engn Signal Proc Computat Intelligence Res Grp SP4CING Inst Informat Istanbul Turkiye

ISBN: (纸本)9798350365474

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT's performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： CNN computer vision deep learning Efficient Parameter Transformer vision Transformer ViT

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