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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是91-100 订阅

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PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs

PIN: Positional Insert Unlocks Object Localisation Abilities...

引用

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

作者： Dorkenwald, Michael Barazani, Nimrod Snoek, Cees G. M. Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798350353006

vision-Language Models (VLMs), such as Flamingo and GPT-4V, have shown immense potential by integrating large language models with vision systems. Nevertheless, these models face challenges in the fundamental computer vision task of object localisation, due to their training on multi-modal data containing mostly captions without explicit spatial grounding. While it is possible to construct custom, supervised training pipelines with bounding box annotations that integrate with VLMs, these result in specialized and hard-to-scale models. In this paper, we aim to explore the limits of caption-based VLMs and instead propose to tackle the challenge in a simpler manner by i) keeping the weights of a caption-based VLM frozen and ii) not using any supervised detection data. To this end, we introduce an input-agnostic Positional Insert (PIN), a learnable spatial prompt, containing a minimal set of parameters that are slid inside the frozen VLM, unlocking object localisation capabilities. Our PIN module is trained with a simple next-token prediction task on synthetic data without requiring the introduction of new output heads. Our experiments demonstrate strong zero-shot localisation performances on a variety of images, including Pascal VOC, COCO, LVIS, and diverse images like paintings or cartoons.

关键词： Efficient Adaption of VLMs Foundation Models vision-Language Models Visual Grounding

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Iterated Learning Improves Compositionality in Large vision-Language Models

Iterated Learning Improves Compositionality in Large Vision-...

引用

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

作者： Zheng, Chenhao Zhang, Jieyu Kembhavi, Aniruddha Krishna, Ranjay Univ Washington Seattle WA 98195 USA Univ Michigan Ann Arbor MI 48109 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9798350353006

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, recent investigations find that most-if not all-our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover, prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission-the need to teach a new generation-as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically, we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent, and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration, this training paradigm induces representations that become "easier to learn", a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7%, 4.0% respectfully in the SugarCrepe benchmark.

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Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video

Video2Game: Real-time, Interactive, Realistic and Browser-Co...

引用

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

作者： Xia, Hongchi Lin, Zhi-Hao Ma, Wei-Chiu Wang, Shenlong Univ Illinois Champaign IL 61820 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Cornell Univ Ithaca NY USA

ISBN: (纸本)9798350353013;9798350353006

Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene;(ii) a mesh module that distills the knowledge from NeRF for faster rendering;and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline, one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.

关键词： computer vision

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Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

Animate Anyone: Consistent and Controllable Image-to-Video S...

引用

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

作者： Hu, Li Alibaba Grp Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013;9798350353006

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on image animation benchmarks, achieving state-of-the-art results.

关键词： Visualization computer vision Training data Benchmark testing Animation Diffusion models Controllability

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StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-t...

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

作者： Choi, Jongwoo Seo, Kwanggyoon Ashtari, Amirsaman Noh, Junyong Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Image and video synthesis and generation vision + graphics vision applications and systems

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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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Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training

Unsupervised Video Domain Adaptation with Masked Pre-Trainin...

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

作者： Reddy, Arun Paul, William Rivera, Corban Shah, Ketul de Melo, Celso M. Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA DEVCOM US Army Res Lab Aberdeen Proving Ground MD USA

ISBN: (纸本)9798350353006

In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data, using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.

关键词： action recognition domain adaptation masked modeling vuda

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SpikingResformer: Bridging ResNet and vision Transformer in Spiking Neural Networks

SpikingResformer: Bridging ResNet and Vision Transformer in ...

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

作者： Shi, Xinyu Hao, Zecheng Yu, Zhaofei Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

The remarkable success of vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges, we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA, we propose a novel spiking vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking vision Transformer counterparts. Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps, which is the state-of-the-art result in the SNN field. Codes are available at https://***/xyshi2000/SpikingResformer

关键词： Spiking Neural Networks vision Transformer

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Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for recognition and Retrieval

Dual Pose-invariant Embeddings: Learning Category and Object...

引用

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

作者： Sarkar, Rohan Kak, Avinash Purdue Univ Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350353006

In the context of pose-invariant object recognition and retrieval, we demonstrate that it is possible to achieve significant improvements in performance if both the category-based and the object-identity-based embeddings are learned simultaneously during training. In hindsight, that sounds intuitive because learning about the categories is more fundamental than learning about the individual objects that correspond to those categories. However, to the best of what we know, no prior work in pose-invariant learning has demonstrated this effect. This paper presents an attention-based dual-encoder architecture with specially designed loss functions that optimize the inter-and intra-class distances simultaneously in two different embedding spaces, one for the category embeddings and the other for the object level embeddings. The loss functions we have proposed are pose-invariant ranking losses that are designed to minimize the intra-class distances and maximize the inter-class distances in the dual representation spaces. We demonstrate the power of our approach with three challenging multi-view datasets, ModelNet-40, ObjectPI, and FG3D. With our dual approach, for single-view object recognition, we outperform the previous best by 20.0% on ModelNet40, 2.0% on ObjectPI, and 46.5% on FG3D. On the other hand, for single-view object retrieval, we outperform the previous best by 33.7% on ModelNet40, 18.8% on ObjectPI, and 56.9% on FG3D.

关键词： Dual embedding space multi-view pose-invariant embeddings recognition and retrieval

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CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models

CONFORM: Contrast is All You Need For High-Fidelity Text-to-...

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

作者： Meral, Tuna Han Salih Simsar, Enis Tombari, Federico Yanardag, Pinar Virginia Tech Blacksburg VA USA Swiss Fed Inst Technol Zurich Switzerland TUM Munich Germany Google Menlo Pk CA USA

ISBN: (纸本)9798350353006

Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects. Existing solutions often require customly tailored functions for each of these problems, leading to sub-optimal results, especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conduct extensive experiments across a wide variety of scenarios, each involving unique combinations of objects, attributes, and scenes. These experiments effectively showcase the versatility, efficiency, and flexibility of our method in working with both latent and pixel-based diffusion models, including Stable Diffusion and Imagen. Moreover, we publicly share our source code to facilitate further research.

关键词： computer vision Contrastive learning Generative AI Semantic fidelity Stable Diffusion Text-to-image diffusion models

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