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共 20951 条记录，以下是111-120 订阅

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Eclipse: Disambiguating Illumination and Materials using Unintended Shadows

Eclipse: Disambiguating Illumination and Materials using Uni...

引用

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

作者： Verbin, Dor Mildenhall, Ben Hedman, Peter Barron, Jonathan T. Zickler, Todd Srinivasan, Pratul P. Google Res Mountain View CA 94043 USA Harvard Univ Cambridge MA USA

ISBN: (纸本)9798350353013;9798350353006

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light, and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination-even from diffuse objects-by exploiting unintended shadows, like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines, but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatiallyvarying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.

关键词： computer Graphics Inverse Rendering Non-line-of-sight Imaging

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A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives

A Backpack Full of Skills: Egocentric Video Understanding wi...

引用

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

作者： Peirone, Simone Alberto Pistilli, Francesca Alliegro, Antonio Averta, Giuseppe Politecnico Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9798350353006

Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning novel skills. To accomplish this, we look for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead, to support multiple down-stream tasks and enable cooperation when learning novel skills. We then propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights, as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods. Project webpage: ***/EgoPack.

关键词： Egocentric vision Video Understanding

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Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning

Context-based and Diversity-driven Specificity in Compositio...

引用

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

作者： Li, Yun Liu, Zhe Chen, Hang Yao, Lina CSIROs Data61 Clayton Vic Australia Bytedance Ltd Beijing Peoples R China Snap Inc Santa Monica CA USA

ISBN: (纸本)9798350353006

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize 'Sliced-Strawberry' over a generic 'Red-Strawberry', despite the former being more informative. They also suffer from ballooning search space when shifting from Close-World (CW) to Open-World (OW) CZSL. To address the issues, we introduce the Context-based and Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context. This novel approach allows for more accurate predictions by emphasizing specific attribute-object pairs and improves composition filtering in OW-CZSL. We conduct experiments in both CW and OW scenarios, and our model achieves state-of-the-art results across three datasets.

关键词： compositional zero-shot learning transfer learning vision language model

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Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box vision-Language Models for Selective Visual Question Answering

Consistency and Uncertainty: Identifying Unreliable Response...

引用

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

作者： Khan, Zaid Fu, Yun Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The goal of selective prediction is to allow an a model to abstain when it may not be able to deliver a reliable prediction, which is important in safety-critical contexts. Existing approaches to selective prediction typically require access to the internals of a model, require retraining a model or study only unimodal models. However, the most powerful models (e.g. GPT-4) are typically only available as black boxes with inaccessible internals, are not retrainable by end-users, and are frequently used for multimodal tasks. We study the possibility of selective prediction for vision-language models in a realistic, black-box setting. We propose using the principle of neighborhood consistency to identify unreliable responses from a black-box vision-language model in question answering tasks. We hypothesize that given only a visual question and model response, the consistency of the model's responses over the neighborhood of a visual question will indicate reliability. It is impossible to directly sample neighbors in feature space in a black-box setting. Instead, we show that it is possible to use a smaller proxy model to approximately sample from the neighborhood. We find that neighborhood consistency can be used to identify model responses to visual questions that are likely unreliable, even in adversarial settings or settings that are out-of-distribution to the proxy model.

关键词： predictive uncertainty selective prediction trustworthy ml vision-language visual question answering

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Prompt Learning via Meta-Regularization

Prompt Learning via Meta-Regularization

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

作者： Park, Jinyoung Ko, Juyeon Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350353006

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of down-stream tasks. However, most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further, ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition, we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://***/mlvlab/ProMetaR.

关键词： Meta-learning Prompt tuning vision-Language Model

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Emu Edit: Precise Image Editing via recognition and Generation Tasks

Emu Edit: Precise Image Editing via Recognition and Generati...

引用

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

作者： Sheynin, Shelly Polyak, Adam Singer, Uriel Kirstain, Yuval Zohar, Amit Ashual, Oron Parikh, Devi Taigman, Yaniv Meta GenAI Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Instruction-based image editing holds immense potential for a variety of applications, as it enables users to perform any editing operation using a natural language instruction. However, current models in this domain often struggle with accurately executing user instructions. We present Emu Edit, a multi-task image editing model which sets state-of-the-art results in instruction-based image editing. To develop Emu Edit we train it to multi-task across an unprecedented range of tasks, such as region-based editing, free-form editing, and computer vision tasks, all of which are formulated as generative tasks. Additionally, to enhance Emu Edit's multi-task learning abilities, we provide it with learned task embeddings which guide the generation process towards the correct edit type. Both these elements are essential for Emu Edit's outstanding performance. Furthermore, we show that Emu Edit can generalize to new tasks, such as image inpainting, super-resolution, and compositions of editing tasks, with just a few labeled examples. This capability offers a significant advantage in scenarios where high-quality samples are scarce. Lastly, to facilitate a more rigorous and informed assessment of instructable image editing models, we release a new challenging and versatile benchmark that includes seven different image editing tasks. (1)

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ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization

ICON: Incremental CONfidence for Joint Pose and Radiance Fie...

引用

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

作者： Wang, Weiyao Gleize, Pierre Tang, Hao Chen, Xingyu Liang, Kevin J. Feiszli, Matt Meta FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However, NeRF training requires accurate camera pose for each input view, typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint, but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON), an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further, ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF, and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON, without prior pose initialization, achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.

关键词： Three dimensional computer graphics

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A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech recognition

A Study of Dropout-Induced Modality Bias on Robustness to Mi...

引用

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

作者： Dai, Yusheng Chen, Hang Du, Jun Wang, Ruoyu Chen, Shihao Wang, Haotian Lee, Chin-Hui Univ Sci & Technol China Hefei Peoples R China Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350353006

Advanced Audio-Visual Speech recognition (AVSR) systems have been observed to be sensitive to missing video frames, performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this study, we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next, we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings, we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality, maintaining performance and robustness simultaneously. Finally, to address an entirely missing modality, we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://***/dalision/ModalBiasAVSR.

关键词： Audio-Visual Processing Audio-Visual Speech recognition Multimodality

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Optimal Transport Aggregation for Visual Place recognition

Optimal Transport Aggregation for Visual Place Recognition

引用

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

作者： Izquierdo, Sergio Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9798350353006

The task of Visual Place recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone, in order to form a global descriptor for each image. In this context, we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors), which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD, we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a 'dustbin' cluster, designed to selectively discard features deemed non-informative, enhancing the overall descriptor quality. Additionally, we leverage and fine-tune DINOv2 as a backbone, which provides enhanced description power for the local features, and dramatically reduces the required training time. As a result, our single-stage method not only surpasses single-stage baselines in public VPR datasets, but also surpasses two-stage methods that add a re-ranking with significantly higher cost. Code and models are available at https://***/serizba/salad.

关键词： Image Retrieval Metric Learning Visual Place recognition

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Active Open-Vocabulary recognition: Let Intelligent Moving Mitigate CLIP Limitations

Active Open-Vocabulary Recognition: Let Intelligent Moving M...

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

作者： Fan, Lei Zhou, Jianxiong Xing, Xiaoying Wu, Ying Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798350353006

Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements. Given the evolving environment and the multitude of object classes, it is impractical to include all possible classes during the training stage. In this paper, we aim at advancing active open-vocabulary recognition, empowering embodied agents to actively perceive and classify arbitrary objects. However, directly adopting recent open-vocabulary classification models, like Contrastive Language Image Pretraining (CLIP), poses its unique challenges. Specifically, we observe that CLIP's performance is heavily affected by the viewpoint and occlusions, compromising its reliability in unconstrained embodied perception scenarios. Further, the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues, we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features, without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset, the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition, without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method.

关键词： Active vision Embodied perception

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