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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11889 条记录，以下是141-150 订阅

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GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation

GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation

引用

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

作者： Khanna, Mukul Ramrakhya, Ram Chhablani, Gunjan Yenamandra, Sriram Gervet, Theophile Chang, Matthew Kiraly, Zsolt Chaplot, Devendra Singh Batra, Dhruv Mottaghi, Roozbeh Georgia Inst Technol Atlanta GA 30332 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Illinois Urbana IL USA Mistral AI Paris France Univ Washington Seattle WA USA

ISBN: (纸本)9798350353006

The Embodied AI community has made significant strides in visual navigation tasks, exploring targets from 3D coordinates, objects, language descriptions, and images. However, these navigation models often handle only a single input modality as the target. With the progress achieved so far, it is time to move towards universal navigation models capable of handling various goal types, enabling more effective user interaction with robots. To facilitate this goal, we propose GOAT-Bench, a benchmark for the universal navigation task referred to as GO to AnyThing (GOAT). In this task, the agent is directed to navigate to a sequence of targets specified by the category name, language description, or image in an open-vocabulary fashion. We benchmark monolithic RL and modular methods on the GOAT task, analyzing their performance across modalities, the role of explicit and implicit scene memories, their robustness to noise in goal specifications, and the impact of memory in lifelong scenarios.

关键词： computer vision Embodied AI Visual navigation

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Instance-based Max-margin for Practical Few-shot recognition

Instance-based Max-margin for Practical Few-shot Recognition

引用

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

作者： Fu, Minghao Zhu, Ke Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications, this paper proposes a practical FSL (pFSL) setting. pFSL is based on unsupervised pre-trained models (analogous to human prior knowledge) and recognizes many novel classes simultaneously. Compared to traditional FSL, pFSL is simpler in its formulation, easier to evaluate, more challenging and more practical. To cope with the rarity of training examples, this paper proposes IbM2, an instance-based max-margin method not only for the new pFSL setting, but also works well in traditional FSL scenarios. Based on the Gaussian Annulus Theorem, IbM2 converts random noise applied to the instances into a mechanism to achieve maximum margin in the many-way pFSL (or traditional FSL) recognition task. Experiments with various self-supervised pre-training methods and diverse many- or few-way FSL tasks show that IbM2 almost always leads to improvements compared to its respective baseline methods, and in most cases the improvements are significant. With both the new pFSL setting and novel IbM2 method, this paper shows that practical few-shot learning is both viable and promising.

关键词： Few-shot Learning Transfer Learning

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Neural Refinement for Absolute Pose Regression with Feature Synthesis

Neural Refinement for Absolute Pose Regression with Feature ...

引用

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

作者： Chen, Shuai Bhalgat, Yash Li, Xinghui Bin, Jia-Wang Li, Kejie Wang, Zirui Prisacariu, Victor Adrian Univ Oxford Act Vision Lab Oxford England Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9798350353006

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of- the-art single-image APR accuracy on indoor and outdoor datasets. Code will be released at https:// ***/ActivevisionLab/NeFeS.

关键词： Feature Distillation Neural Radiance Field Pose Regression Test-time Refinement Visual Re-Localization

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LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation

LQMFormer: Language-aware Query Mask Transformer for Referri...

引用

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

作者： Shah, Nisarg A. Vibashan, V. S. Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

Referring Image Segmentation (RIS) aims to segment objects from an image based on a language description. Recent advancements have introduced transformer-based methods that leverage cross-modal dependencies, significantly enhancing performance in referring segmentation tasks. These methods are designed such that each query predicts different masks. However, RIS inherently requires a single-mask prediction, leading to a phenomenon known as Query Collapse, where all queries yield the same mask prediction. This reduces the generalization capability of the RIS model for complex or novel scenarios. To address this issue, we propose a Multi-modal Query Feature Fusion technique, characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion, a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships, and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description. Moreover, we show that including an auxiliary loss to increase the distance between mask representations of different queries further enhances performance and mitigates query collapse. Extensive experiments conducted on four benchmark datasets validate the effectiveness of our framework.

关键词： Image segmentation Multimodal Transformer vision-Language

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

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

引用

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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eTraM: Event-based Traffic Monitoring Dataset

eTraM: Event-based Traffic Monitoring Dataset

引用

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

作者： Verma, Aayush Atul Chakravarthi, Bharatesh Vaghela, Arpitsinh Wei, Hua Yang, Yezhou Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798350353006

Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields. However, their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration, we present eTraM - a first-of-its-kind, fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios in various lighting and weather conditions, providing a comprehensive overview of real-world situations. Providing 2M bounding box annotations, it covers eight distinct classes of traffic participants, ranging from vehicles to pedestrians and micro-mobility. eTraM's utility has been assessed using state-of-the-art methods for traffic participant detection, including RVT, RED, and YOLOv8. We quantitatively evaluate the ability of event-based models to generalize on nighttime and unseen scenes. Our findings substantiate the compelling potential of leveraging event cameras for traffic monitoring, opening new avenues for research and application. eTraM is available at https://***/eTraM.

关键词： DVS Dynamic vision Sensor Event-based Event-based vision Event-camera ITS Neuromorphic

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SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient vision Transformers

SlowFormer: Adversarial Attack on Compute and Energy Consump...

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

作者： Navaneet, K. L. Koohpayegani, Soroush Abbasi Sleiman, Essam Pirsiavash, Hamed Univ Calif Davis Davis CA 95616 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350353006

Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack, where the attacker optimizes for a patch that when pasted on any image, can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases, the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary in the future to lower the power usage of expensive deep models, so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack. Code is available at: https://***/UCDvision/SlowFormer

关键词： Adversarial attack computation attack efficient inference efficient vision transformers energy attack

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ShapeWalk: Compositional Shape Editing through Language-Guided Chains

ShapeWalk: Compositional Shape Editing through Language-Guid...

引用

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

作者： Slim, Habib Elhoseiny, Mohamed KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350353006

Editing 3D shapes through natural language instructions is a challenging task that requires the comprehension of both language semantics and fine-grained geometric details. To bridge this gap, we introduce ShapeWalk, a carefully designed synthetic dataset designed to advance the field of language-guided shape editing. The dataset consists of 158K unique shapes connected through 26K edit chains, with an average length of 14 chained shapes. Each consecutive pair of shapes is associated with precise language instructions describing the applied edits. We synthesize edit chains by reconstructing and interpolating shapes sampled from a realistic CAD-designed 3D dataset in the parameter space of the GeoCode shape program. We leverage rule-based methods and language models to generate accurate and realistic natural language prompts corresponding to each edit. To illustrate the practicality of our contribution, we train neural editor modules in the latent space of shape autoencoders, and demonstrate the ability of our dataset to enable a variety of language-guided shape edits. Finally, we introduce multi-step editing metrics to benchmark the capacity of our models to perform recursive shape edits. We hope that our work will enable further study of compositional language-guided shape editing, and finds application in 3D CAD design and interactive modeling.

关键词： 3D language editing 3D vision compositionality

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Understanding Video Transformers via Universal Concept Discovery

Understanding Video Transformers via Universal Concept Disco...

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

作者： Kowal, Matthew Dave, Achal Ambrus, Rares Gaidon, Adrien Derpanis, Konstantinos G. Tokmakov, Pavel York Univ Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada Toyota Res Inst Los Altos CA 94022 USA Vector Inst Toronto ON Canada

ISBN: (纸本)9798350353006

This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively, video models deal with the added temporal dimension, increasing complexity and posing challenges in identifying dynamic concepts over time. In this work, we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end, we pro-pose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model. The resulting concepts are highly interpretable, revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations, we discover that some of these mechanism are universal in video transformers. Finally, we show that VTCD can be used for fine-grained action recognition and video object segmentation.

关键词： Action recognition Interpretability Transformers Video Object Segmentation Video Understanding

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Learning to Predict Activity Progress by Self-Supervised Video Alignment

Learning to Predict Activity Progress by Self-Supervised Vid...

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

作者： Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings, redundant actions, and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycleconsistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically, to handle repeated actions, we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames, we introduce a context-dependent drop function in our framework, discouraging the alignment of droppable frames. On the other hand, to learn from videos of multiple activities jointly, we propose a multi-head crosstask network, allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction. (1)

关键词： computer vision in the wild procedural learning progress progress prediction representation learning self-supervised self-supervised representation learning unconstrained unconstrained videos Video Alignment video understanding

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