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Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

Conditioned and composed image retrieval combining and parti...

引用

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

作者： Baldrati, Alberto Bertini, Marco Uricchio, Tiberio Del Bimbo, Alberto Univ Firenze MICC Florence Italy Univ Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present an approach for conditioned and composed image retrieval based on CLIP features. In this extension of content-based image retrieval (CBIR) an image is combined with a text that provides information regarding user intentions, and is relevant for application domains like e-commerce. The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder. Then in a second training stage we learn a more complex combiner network that merges visual and textual features. Contrastive learning is used in both stages. The proposed approach obtains state-of-the-art performance for conditioned CBIR on the FashionIQ dataset and for composed CBIR on the more recent CIRR dataset.

关键词： Training Visualization computer vision conferences Image retrieval Solids pattern recognition

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PartDistill: 3D Shape Part Segmentation by vision-Language Model Distillation

PartDistill: 3D Shape Part Segmentation by Vision-Language M...

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

作者： Umam, Ardian Yang, Cheng-Kun Chen, Min-Hung Chuang, Jen-Hui Lin, Yen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan MediaTek Hsinchu Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353013;9798350353006

This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation. PartDistill addresses three major challenges in this task: the lack of 3D segmentation in invisible or undetected regions in the 2D projections, inconsistent 2D predictions by VLMs, and the lack of knowledge accumulation across different 3D shapes. PartDistill consists of a teacher network that uses a VLM to make 2D predictions and a student network that learns from the 2D predictions while extracting geometrical features from multiple 3D shapes to carry out 3D part segmentation. A bidirectional distillation, including forward and backward distillations, is carried out within the framework, where the former forward distills the 2D predictions to the student network, and the latter improves the quality of the 2D predictions, which subsequently enhances the final 3D segmentation. Moreover, PartDistill can exploit generative models that facilitate effortless 3D shape creation for generating knowledge sources to be distilled. Through extensive experiments, PartDistill boosts the existing methods with substantial margins on widely used ShapeNetPart and PartNetE datasets, by more than 15% and 12% higher mIoU scores, respectively. The code for this work is available at https://***/ardianumam/PartDistill.

关键词： 3d part segmentation cross-modal distillation zero-shot segmentation

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AAFormer: A Multi-Modal Transformer Network for Aerial Agricultural Images

AAFormer: A Multi-Modal Transformer Network for Aerial Agric...

引用

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

作者： Shen, Yao Wang, Lei Jin, Yue China Pacific Insurance Grp Co Ltd Shanghai Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic segmentation such as Fully Convolutional Networks can extract rich semantic features, but are difficult to exploit the long-range information. Recently, vision Transformer architectures have made outstanding performances in image segmentation tasks, but transformer-based models have not been fully explored in the field of ***, we propose a novel architecture called Agricultural Aerial Transformer (AAFormer) to solve the semantic segmentation of aerial farmland images. We adopt Mix Transformer (MiT) in the encoder stage to enhance the ability of field anomaly pattern recognition and leverage the Squeeze-and-Excitation (SE) module in the decoder stage to improve the effectiveness of key channels. The boundary maps of farmland are introduced into the decoder. Evaluated on the Agriculture-vision validation set, the mIoU of our proposed model reaches 45.44%.

关键词： Image segmentation Image recognition conferences Semantics computer architecture Transformers Feature extraction

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Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

Generalizable Whole Slide Image Classification with Fine-Gra...

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

作者： Li, Hao Chen, Ying Chen, Yifei Yu, Rongshan Yang, Wenxian Wang, Liansheng Ding, Bowen Han, Yuchen Xiamen Univ Sch Informat Xiamen Peoples R China Huawei Xiamen Peoples R China Aginome Sci Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Chest Hosp Dept Pathol Sch Med Shanghai Peoples R China

ISBN: (纸本)9798350353006

Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of models on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel "Fine-grained Visual-Semantic Interaction" (FiVE) framework for WSI classification. It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and augments generalization capabilities significantly. Furthermore, given that pathological visual patterns are redundantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demonstrates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code is available at: https://***/ls1rius/WSI FiVE.

关键词： Fine-Grained Generalizable vision-Language-Model Visual-Semantic Whole Slide Image

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KBody: Balanced monocular whole-body estimation

KBody: Balanced monocular whole-body estimation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zioulis, Nikolaos O'Brien, James F. Klothed Technologies Inc. United States Uc Berkeley United States

ISBN: (纸本)9798350302493

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Compared to other approaches, it introduces virtual joints to identify higher quality correspondences and disentangles the optimization between the pose and shape parameters to achieve a more balanced result in terms of pose and shape capturing capacity, as well as pixel alignment. © 2023 ieee.

关键词： computer vision

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Theoretically Achieving Continuous Representation of Oriented Bounding Boxes

Theoretically Achieving Continuous Representation of Oriente...

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

作者： Xiao, Zi-Kai Yang, Guo-Ye Yang, Xue Mul, Tai-Jiang Yan, Junchi Hui, Shi-Min Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab AI Shanghai Peoples R China

ISBN: (纸本)9798350353006

Considerable efforts have been devoted to Oriented Object Detection (OOD). However, one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved, which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio, and often inadvertently introduce decoding discontinuity, e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically, we propose a novel representation method called Continuous OBB (COBB), which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge, has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments, we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset, by integrating Faster-RCNN as the same baseline model, our new method outperforms the peer method Gliding Vertex by 1.13% mAP(50) (relative improvement 1.54%), and 2.46% mAP(75) (relative improvement 5.91%), without any tricks.

关键词： boundary problem computer vision object detection oriented object detection remote sensing

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PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization

PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera S...

引用

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

作者： Cai, Yanlu Zhang, Weizhong Wu, Yuan Jin, Cheng Fudan Univ Shanghai Peoples R China Haina Lab Shanghai Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact, showcasing significant potential in real applications. However, due to the limited camera setting diversity in the available datasets, the inferred camera parameters are always simply hard-coded into the model during training and not adaptable to the input in inference, making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples, i.e., 2D-3D pose pairs, under massive new camera settings. Unfortunately, to prevent over-fitting the existing camera setting, the number of synthesized samples for each new camera setting should be comparable with that for the existing one, which multiplies the scale of training and even makes it computationally prohibitive. In this paper, we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely, we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm, which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features, its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.

关键词： computer vision Multi-View Pose Estimation

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Image as a Foreign Language: BEIT Pretraining for vision and vision-Language Tasks

Image as a Foreign Language: BEIT Pretraining for Vision and...

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

作者： Wang, Wenhui Bao, Hangbo Dong, Li Bjorck, Johan Peng, Zhiliang Liu, Qiang Aggarwal, Kriti Mohammed, Owais Khan Singhal, Saksham Som, Subhojit Wei, Furu Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350301298

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEIT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We use Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEIT-3 obtains remarkable performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).

关键词： language reasoning vision

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Adversarial Counterfactual Visual Explanations

Adversarial Counterfactual Visual Explanations

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

作者： Jeanneret, Guillaume Simon, Loic Jurie, Frederic Univ Caen Normandie ENSICAEN CNRS Caen France

ISBN: (纸本)9798350301298

Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds.

关键词： Explainable computer vision

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Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld

Embodied Multi-Modal Agent trained by an LLM from a Parallel...

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

作者： Yang, Yijun Zhou, Tianyi Li, Kanxue Tao, Dapeng Li, Lusong Shen, Li He, Xiaodong Jiang, Jing Shi, Yuhui Southern Univ Sci & Technol Shenzhen Peoples R China Univ Maryland College Pk MD 20742 USA Yunnan Univ Kunming Yunnan Peoples R China JD Explore Acad Beijing Peoples R China Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9798350353006

While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals. Although vision-language models (VLMs) integrate LLM modules (1) aligned with static image features, and (2) may possess prior knowledge of world dynamics (as demonstrated in the text world), they have not been trained in an embodied visual world and thus cannot align with its dynamics. On the other hand, training an embodied agent in a noisy visual world without expert guidance is often challenging and inefficient. In this paper, we train a VLM agent living in a visual world using an LLM agent excelling in a parallel text world. Specifically, we distill LLM's reflection outcomes (improved actions by analyzing mistakes) in a text world's tasks to finetune the VLM on the same tasks of the visual world, resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dynamics. Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm, enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert. Extensive evaluations on the ALFWorld benchmark's diverse tasks highlight EMMA's superior performance to SOTA VLM-based agents, e.g., 20%-70% improvement in the success rate.

关键词： Embodied AI Imitation Learning Large Language Models vision Language Models

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