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GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding

GSNeRF: Generalizable Semantic Neural Radiance Fields with E...

引用

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

作者： Chou, Zi-Ting Huang, Sheng-Yu Liu, I-Jieh Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353006

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Fields ( GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view image and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic GeoReasoning and Depth-Guided Visual rendering. The former is able to observe multi- view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

关键词： 3D computer vision generalizable nerf NeRF segmentation

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Streaming Dense Video Captioning

Streaming Dense Video Captioning

引用

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

作者： Zhou, Xingyi Arnab, Anurag Buch, Shyamal Yan, Shen Myers, Austin Xiong, Xuehan Nagrani, Arsha Schmid, Cordelia Google Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

An ideal model for dense video captioning - predicting captions localized temporally in a video - should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First, we propose a new memory module, based on clustering incoming tokens, which can handle arbitrarily long videos as the memory is of a fixed size. Second, we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability, and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at https://***/google-research/scenic.

关键词： captioning long video streaming video video captioning vision and language

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Semantics-aware Motion Retargeting with vision-Language Models

Semantics-aware Motion Retargeting with Vision-Language Mode...

引用

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

作者： Zhang, Haodong Chen, Zhike Xu, Haocheng Hao, Lei Wu, Xiaofei Xu, Songcen Zhang, Zhensong Wang, Yue Xiong, Rong Zhejiang Univ Hangzhou Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353013;9798350353006

Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However, most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here, we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics, we adopt a two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics. Project page can be found at https://***/view/smtnet.

关键词： Animation Motion Retargeting vision-Language Model

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ArGue: Attribute-Guided Prompt Tuning for vision-Language Models

ArGue: Attribute-Guided Prompt Tuning for Vision-Language Mo...

引用

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

作者： Tian, Xinyu Zou, Shu Yang, Zhaoyuan Zhang, Jing Australian Natl Univ Canberra ACT Australia GE Res Niskayuna NY USA

ISBN: (纸本)9798350353006

Although soft prompt tuning is effective in efficiently adapting vision-Language (V&L) models for downstream tasks, it shows limitations in dealing with distribution shifts. We address this issue with Attribute-Guided Prompt Tuning (ArGue), making three key contributions. 1) In contrast to the conventional approach of directly appending soft prompts preceding class names, we align the model with primitive visual attributes generated by Large Language Models (LLMs). We posit that a model's ability to express high confidence in these attributes signifies its capacity to discern the correct class rationales. 2) We introduce attribute sampling to eliminate disadvantageous attributes, thus only semantically meaningful attributes are preserved. 3) We propose negative prompting, explicitly enumerating class-agnostic attributes to activate spurious correlations and encourage the model to generate highly orthogonal probability distributions in relation to these negative features. In experiments, our method significantly outperforms current state-of-the-art prompt tuning methods on both novel class prediction and out-of-distribution generalization tasks. The code is available https://***/Liam-Tian/ArGue.

关键词： few-shot adaptation prompt tuning vision-language model

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JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups

JRDB-Social: A Multifaceted Robotic Dataset for Understandin...

引用

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

作者： Jahangard, Simindokht Cai, Zhixi Wen, Shiki Rezatofighi, Hamid Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350353006

Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short, necessitating a comprehensive approach that considers individual behaviour, intra-group dynamics, and social group levels for a thorough understanding. To address dataset limitations, this paper introduces JRDB-Social, an extension of JRDB [2]. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts, JRDB-Social provides annotations at three levels: individual attributes, intra-group interactions, and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models, we evaluated our benchmark to explore their capacity to decipher social human behaviour.

关键词： dataset human attributes human human interaction human social behaviour understanding interaction large language model multifaceted robotic dataset social group social robot vision language model visual question answering visual reasoning

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain vision Transformers

ALGM: Adaptive Local-then-Global Token Merging for Efficient...

引用

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

作者： Norouzi, Narges Orlova, Svetlana de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350353006

This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it merges similar tokens across the entire image. This is motivated by an analysis in which we found that, in those situations, tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations, we show that ALGM not only significantly improves the throughput by up to 100%, but can also enhance the mean IoU by up to +1.1, thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover, our approach is adaptive during inference, meaning that the same model can be used for optimal efficiency or accuracy, depending on the application. Code is available at https://***/ALGM.

关键词： Efficient vision Transformers Semantic Segmentation Token Merging

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Dense vision Transformer Compression with Few Samples

Dense Vision Transformer Compression with Few Samples

引用

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

作者： Zhang, Hanxiao Zhou, Yifan Wang, Guo-Hua 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

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

关键词： Few-shot Model Compression Pruning vision Transformer

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Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection

Gaussian Image Anomaly Detection with Greedy Eigencomponent ...

引用

ieee/cvf International conference on computer vision (ICCV)

作者： Gula, Tetiana Bertoldo, Joao P. C. PSL Univ Ctr Math Morphol Mines Paris F-77300 Fontainebleau France

ISBN: (纸本)9798350307443

This paper addresses the challenge of Anomaly detection (AD) in images by proposing a novel dimensionality reduction technique using pre-trained convolutional neural network (CNN) with EfficientNet model. We introduce two tree search methods with a greedy strategy for improved eigen-component selection. We conducted three experiments to evaluate our approach: examining components choice on test set performance when intentionally overfitting, training on one anomaly type and testing on others, and examining training with a minimal image set based on anomaly types. Unlike traditional methods that emphasize variance, our focus is on maximizing performance and understanding component behavior in diverse settings. Results show our technique outperforms both Principal Component Analysis (PCA) and Negated PCA (NPCA), suggesting a promising advancement in AD efficiency and effectiveness.

关键词： computer vision pattern recognition

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Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Repurposing Diffusion-Based Image Generators for Monocular D...

引用

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

作者： Ke, Bingxin Obukhov, Anton Huang, Shengyu Metzger, Nando Daudt, Rodrigo Caye Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland

ISBN: (纸本)9798350353006

Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://***.

关键词： ddim ddpm depth estimation diffusion generative LDM vision

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Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Adaptive Hyper-graph Aggregation for Modality-Agnostic Feder...

引用

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

作者： Qi, Fan Li, Shuai Tianjin Univ Technol Tianjin Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility, we propose the Adaptive Hyper-graph Aggregation framework, a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality, setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy, which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments, accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach, tested on three multimodal benchmark datasets, demonstrated strong performance across diverse data distributions, affirming its effectiveness in multimodal federated learning.

关键词： Knowledge engineering computer vision Adaptation models Federated learning Prototypes computer architecture Benchmark testing

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