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共 11281 条记录，以下是121-130 订阅

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SpikingResformer: Bridging ResNet and vision Transformer in Spiking Neural Networks

SpikingResformer: Bridging ResNet and Vision Transformer in ...

引用

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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Intrinsic Image Diffusion for Indoor Single-view Material Estimation

Intrinsic Image Diffusion for Indoor Single-view Material Es...

引用

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

作者： Kocsis, Peter Sitzmann, Vincent Niessner, Matthias Tech Univ Munich Munich Germany MIT EECS Cambridge MA 02139 USA

ISBN: (纸本)9798350353013;9798350353006

We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue, we advocate for a probabilistic formulation, where instead of attempting to directly predict the true material properties, we employ a conditional generative model to sample from the solution space. Furthermore, we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

关键词： Appearance Decompostion computer vision Deep Learning Diffusion Graphics Lighting Estimation Material Estimation

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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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Bi-Causal: Group Activity recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

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

作者： Zhang, Youliang Liu, Wenxuan Xu, Danni Zhou, Zhuo Wang, Zheng Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Inst Artificial Intelligence Wuhan Hubei Peoples R China Hubei Key Lab Multimedia & Network Commun Engn Wuhan Hubei Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350353013;9798350353006

Current approaches in Group Activity recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Interactions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality communication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： Bidirectional causality Group activity recognition Human-object relation

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PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs

PIN: Positional Insert Unlocks Object Localisation Abilities...

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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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Resource-Efficient Transformer Pruning for Finetuning of Large Models

Resource-Efficient Transformer Pruning for Finetuning of Lar...

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

作者： Ilhan, Fatih Su, Gong Tekin, Selim Furkan Huang, Tiansheng Hu, Sihao Liu, Ling Georgia Inst Technol Atlanta GA 30332 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798350353006

With the recent advances in vision transformers and large language models (LLMs), finetuning costly large models on downstream learning tasks poses significant challenges under limited computational resources. This paper presents a REsource and ComputAtion-efficient Pruning framework (RECAP) for the finetuning of transformer-based large models. RECAP by design bridges the gap between efficiency and performance through an iterative process cycling between pruning, finetuning, and updating stages to explore different chunks of the given large-scale model. At each iteration, we first prune the model with Taylor-approximation-based importance estimation and then only update a subset of the pruned model weights based on the Fisher-information criterion. In this way, RECAP achieves two synergistic and yet conflicting goals: reducing the GPU memory footprint while maintaining model performance, unlike most existing pruning methods that require the model to be finetuned beforehand for better preservation of model performance. We perform extensive experiments with a wide range of large transformer-based architectures on various computer vision and natural language understanding tasks. Compared to recent pruning techniques, we demonstrate that RECAP offers significant improvements in GPU memory efficiency, capable of reducing the footprint by up to 65%.

关键词： efficient finetuning pruning vision transformers

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Learning by Correction: Efficient Tuning Task for Zero-Shot Generative vision-Language Reasoning

Learning by Correction: Efficient Tuning Task for Zero-Shot ...

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

作者： Li, Rongjie Wu, Yu He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improving their zero-shot reasoning typically requires second-stage instruction tuning, which relies heavily on human-labeled or large language model-generated annotation, incurring high labeling costs. To tackle this challenge, we introduce Image-Conditioned Caption Correction (ICCC), a novel pre-training task designed to enhance VLMs' zero-shot performance without the need for labeled task-aware data. The ICCC task compels VLMs to rectify mismatches between visual and language concepts, thereby enhancing instruction following and text generation conditioned on visual inputs. Leveraging language structure and a lightweight dependency parser, we construct data samples of ICCC task from image-text datasets with low labeling and computation costs. Experimental results on BLIP2 and InstructBLIP demonstrate significant improvements in zero-shot image-text generation-based VL tasks through ICCC instruction tuning.

关键词： Multimodal Reasoning vision-Language

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On the Robustness of Language Guidance for Low-Level vision Tasks: Findings from Depth Estimation

On the Robustness of Language Guidance for Low-Level Vision ...

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

作者： Chatterjee, Agneet Gokhale, Tejas Baral, Chitta Yang, Yezhou Arizona State Univ Tempe AZ 85281 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9798350353013;9798350353006

Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the language prior, particularly in terms of generalization and robustness, remains unexplored. In this paper, we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric, three-dimensional spatial relationships, incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data, these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally, to provide a foundation for future research, we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation, our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings. (1)

关键词： Low-level vision robustness vision and language

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Optimal Transport Aggregation for Visual Place recognition

Optimal Transport Aggregation for Visual Place Recognition

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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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Physical Property Understanding from Language-Embedded Feature Fields

Physical Property Understanding from Language-Embedded Featu...

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

作者： Zhai, Albert J. Shen, Yuan Chen, Emily Y. Wang, Gloria X. Wang, Xinlei Wang, Sheng Guan, Kaiyu Wang, Shenlong Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate, annotation-free, and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks, such as estimating the mass of common objects, as well as other properties like friction and hardness. Code is available at https://***/NeRF2Physics.

关键词： 3D scene understanding digital twin physical properties vision and language

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