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共 21179 条记录，以下是251-260 订阅

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ViT-CoMer: vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions

ViT-CoMer: Vision Transformer with Convolutional Multi-scale...

引用

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

作者： Xia, Chunlong Wang, Xinliang Lv, Feng Hao, Xin Shi, Yifeng Baidu Inc Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Although vision Transformer (ViT) has achieved significant success in computer vision, it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems, which introduce additional pre-training costs. Therefore, we present a plain, pre-training-free, and feature-enhanced ViT back-bone with Convolutional Multi-scale feature interaction, named ViT-CoMer, which facilitates bidirectional interaction between CNN and transformer. Compared to the state-of-the-art, ViT-CoMer has the following advantages: (1) We inject spatial pyramid multi-receptive field convolutional features into the ViT architecture, which effectively alleviates the problems of limited local information interaction and single-feature representation in ViT. (2) We propose a simple and efficient CNN-Transformer bidirectional fusion interaction module that performs multi-scale fusion across hierarchical features, which is beneficial for handling dense prediction tasks. (3) We evaluate the performance of ViT-CoMer across various dense prediction tasks, different frameworks, and multiple advanced pre-training. Notably, our ViT-CoMer-L achieves 64.3% AP on COCO val2017 without extra training data, and 62.1% mIoU on ADE20K val, both of which are comparable to state-of-the-art methods. We hope ViT-CoMer can serve as a new backbone for dense prediction tasks to facilitate future research. The code will be released at https://***/Traffic-X/ViT-CoMer.

关键词： DensePrediction visionFoundationBackbone visionTransformer

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Grounding Everything: Emerging Localization Properties in vision-Language Transformers

Grounding Everything: Emerging Localization Properties in Vi...

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

作者： Bousselham, Walid Petersen, Felix Ferrari, Vittorio Kuehne, Hilde Univ Bonn Bonn Germany Goethe Univ Frankfurt Frankfurt Germany Stanford Univ Stanford CA 94305 USA Synthesia Io London England MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350353013;9798350353006

vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models seem to fall behind when it comes to zero-shot localization of referential expressions and objects in images. As a result, they need to be fine-tuned for this task. In this paper, we show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. To leverage those capabilities, we propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery [17] to a self-self attention path. We show that the concept of self-self attention corresponds to clustering, thus enforcing groups of tokens arising from the same object to be similar while preserving the alignment with the language space. To further guide the group formation, we propose a set of regularizations that allows the model to finally generalize across datasets and backbones. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation. GEM not only outperforms other training-free open-vocabulary localization methods, but also achieves state-of-the-art results on the recently proposed OpenImagesV7 large-scale segmentation benchmark. (1)

关键词： CLIP open-vocabulary zero-shot segmentation vision-language model

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Collaborating Foundation Models for Domain Generalized Semantic Segmentation

Collaborating Foundation Models for Domain Generalized Seman...

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

作者： Benigmim, Yasser Roy, Subhankar Essid, Slim Kalogeiton, Vicky Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Palaiseau France Inst Polytech Paris CNRS Ecole Polytech LIX Palaiseau France Univ Aberdeen Aberdeen Scotland

ISBN: (纸本)9798350353013;9798350353006

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation, (ii) Diffusion Model to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU, respectively. Our code is available at https://***/yasserben/CLOUDS

关键词： computer vision Deep Learning Domain Adaptation Domain Generalization Foundation Models Semantic Segmentation

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Discovering and Mitigating Visual Biases through Keyword Explanation

Discovering and Mitigating Visual Biases through Keyword Exp...

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

作者： Kim, Younghyun Mo, Sangwoo Kim, Minkyu Lee, Kyungmin Lee, Jaeho Shin, Jinwoo Korea Adv Inst Sci & Technol Daejeon South Korea Univ Michigan Ann Arbor MI 48109 USA KRAFTON Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350353006

Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often identified indirectly through visualization or sample statistics, which necessitates additional human supervision for interpretation. To tackle this issue, we propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords. Specifically, we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases, such as gender bias in CelebA, background bias in Waterbirds, and distribution shifts in ImageNet-R/C. Additionally, B2T uncovers novel biases in larger datasets, such as Dollar Street and ImageNet. For example, we discovered a contextual bias between "bee" and "flower" in ImageNet. We also highlight various applications of B2T keywords, including debiased training, CLIP prompting, and model comparison.(1)

关键词： bias and fairness explainable AI vision-language model

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ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images

ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-vie...

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

作者： Jeong, Jinseo Koo, Junseo Zhang, Qimeng Kim, Gunhee Seoul Natl Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene. In this work, we confront this limitation using LDR multi-view images captured with emissive sources turned on and off. Two key issues must be addressed: 1) ambiguity arising from the limited dynamic range along with unknown lighting details, and 2) the expensive computational cost in volume rendering to backtrace the paths leading to final object colors. We present a novel approach, ESR-NeRF, leveraging neural networks as learnable functions to represent ray-traced fields. By training networks to satisfy light transport segments, we regulate outgoing radiances, progressively identifying emissive sources while being aware of reflection areas. The results on scenes encompassing emissive sources with various properties demonstrate the superiority of ESR-NeRF in qualitative and quantitative ways. Our approach also extends its applicability to the scenes devoid of emissive sources, achieving lower CD metrics on the DTU dataset.

关键词： Inverse-rendering NeRF Scene-editing vision + graphics

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Generating Enhanced Negatives for Training Language-Based Object Detectors

Generating Enhanced Negatives for Training Language-Based Ob...

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

作者： Zhao, Shiyu Zhao, Long Kumar, Vijay B. G. Suh, Yumin Metaxas, Dimitris N. Chandraker, Manmohan Schulter, Samuel Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego La Jolla CA USA

ISBN: (纸本)9798350353006

The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful, but requires good positive and negative samples. However, the free-form nature and the open vocabulary of object descriptions make the space of negatives extremely large. Prior works randomly sample negatives or use rule-based techniques to build them. In contrast, we propose to leverage the vast knowledge built into modern generative models to automatically build negatives that are more relevant to the original data. Specifically, we use large-Language-models to generate negative text descriptions, and text-to-image diffusion models to also generate corresponding negative images. Our experimental analysis confirms the relevance of the generated negative data, and its use in language-based detectors improves performance on two complex benchmarks. Code is available at https://***/xiaofeng94/Gen-Enhanced-Negs.

关键词： large language model negative example mining open-vocabulary object detection vision and language

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LowRankOcc: Tensor Decomposition and Low-Rank Recovery for vision-based 3D Semantic Occupancy Prediction

LowRankOcc: Tensor Decomposition and Low-Rank Recovery for V...

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

作者： Zhao, Linqing Xu, Xiuwei Wang, Ziwei Zhang, Yunpeng Zhang, Borui Zheng, Wenzhao Du, Dalong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids, but the sparsity of non-empty voxels introduces considerable spatial redundancy, leading to potential overfitting risks. In contrast, our approach leverages the intrinsic low-rank property of 3D occupancy data, factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically, we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding recovery" framework, we encode 3D contexts with only 1/2D convolutions and poolings, and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.

关键词： 3D semantic occupancy tensor decomposition

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Continual Segmentation with Disentangled Objectness Learning and Class recognition

Continual Segmentation with Disentangled Objectness Learning...

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

作者： Gong, Yizheng Yu, Siyue Wang, Xiaoyang Xiao, Jimin Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England Metavisioncn Istanbul Turkiye

ISBN: (纸本)9798350353013;9798350353006

Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://***/jordangong/CoMasTRe.

关键词： continual learning image segmentation Transformer

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TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding

TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object...

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

作者： Liu, Yun Yang, Haolin Si, Xu Liu, Ling Li, Zipeng Zhang, Yuxiang Liu, Yebin Yi, Li Tsinghua Univ Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However, existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this, we construct TACO, an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views, precise hand-object 3D meshes, and action labels. To rapidly expand the data scale, we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO, we benchmark three generalizable hand-object-interaction tasks: compositional action recognition, generalizable hand-object motion forecasting, and cooperative grasp synthesis. Extensive experiments reveal new insights, challenges, and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://***.

关键词： Action recognition Dataset Hand-object Interaction Motion Capture Motion Synthesis

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Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions

Improved Zero-Shot Classification by Adapting VLMs with Text...

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

作者： Saha, Oindrila Van Horn, Grant Maji, Subhransu Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798350353006

The zero-shot performance of existing vision-language models (VLMs) such as CLIP [29] is limited by the availability of large-scale, aligned image and text datasets in specific domains. In this work, we leverage two complementary sources of information-descriptions of categories generated by large language models (LLMs) and abundant, fine- grained image classification datasets-to improve the zero-shot classification performance of VLMs across finegrained domains. On the technical side, we develop methods to train VLMs with this "bag-level" image-text supervision. We find that simply using these attributes at test-time does not improve performance, but our training strategy, for example, on the iNaturalist [41] dataset, leads to an average improvement of 4-5% in zero-shot classification accuracy for novel categories of birds [42] and flowers [23]. Similar improvements are observed in domains where a subset of the categories was used to fine-tune the model. By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories. We systematically evaluate their ability to improve zero-shot cat-egorization in natural domains. Our findings suggest that geographic priors can be just as effective and are complementary to visual appearance. Our method also outperforms prior work on prompt-based tuning of VLMs. We release the benchmark, consisting of 14 datasets at https: //***/cvl-umass/AdaptCLIPZS, which will contribute to future research in zero-shot recognition.

关键词： Fine-grained Classification Large Language Model Multimodal Model vision Language Model Zero Shot Learning

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