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A New Path: Scaling vision-and-Language Navigation with Synthetic Instructions and Imitation Learning

A New Path: Scaling Vision-and-Language Navigation with Synt...

引用

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

作者： Kamath, Aishwarya Anderson, Peter Wang, Su Koh, Jing Yu Ku, Alexander Waters, Austin Yang, Yinfei Baldridge, Jason Parekh, Zarana NYU New York NY 10003 USA Google Res Mountain View CA 94043 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Apple Cupertino CA USA

ISBN: (纸本)9798350301298

Recent studies in vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pretraining on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360. panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky [63], a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN [27]. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale training on near-human quality synthetic instructions.

关键词： language reasoning vision

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Magic3D: High-Resolution Text-to-3D Content Creation

Magic3D: High-Resolution Text-to-3D Content Creation

引用

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

作者： Lin, Chen-Hsuan Gao, Jun Tang, Luming Takikawa, Towaki Zeng, Xiaohui Huang, Xun Kreis, Karsten Fidler, Sanja Liu, Ming-Yu Lin, Tsung-Yi NVIDIA Corp Santa Clara CA 95051 USA

ISBN: (纸本)9798350301298

DreamFusion [31] has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF) [23], achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time. In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model. Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2x faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution. User studies show 61.7% raters to prefer our approach over DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.

关键词： vision + graphics

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Task Residual for Tuning vision-Language Models

Task Residual for Tuning Vision-Language Models

引用

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

作者： Yu, Tao Lu, Zhihe Jin, Xin Chen, Zhibo Wang, Xinchao Natl Univ Singapore Singapore Singapore Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://***/geekyutao/TaskRes.

关键词： language reasoning vision

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Omni Aggregation Networks for Lightweight Image Super-Resolution

Omni Aggregation Networks for Lightweight Image Super-Resolu...

引用

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

作者： Wang, Hang Chen, Xuanhong Ni, Bingbing Liu, Yutian Liu, Jinfan Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks, this work proposes two enhanced components under a new Omni-SR architecture. First, an Omni Self-Attention (OSA) block is proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, mining the potential correlations across omni-axis (i.e., spatial and channel). Coupling with mainstream window partitioning strategies, OSA can achieve superior performance with compelling computational budgets. Second, a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e., premature saturation) in shallow models, which facilitates local propagation and meso-/global-scale interactions, rendering an omni-scale aggregation building block. Extensive experiments demonstrate that Omni-SR achieves record-high performance on lightweight super-resolution benchmarks (e.g., 26.95dB@Urban100 x4 with only 792K parameters). Our code is available at https://***/Francis0625/Omni-SR.

关键词： Low-level vision

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FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer

FlatFormer: Flattened Window Attention for Efficient Point C...

引用

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

作者： Liu, Zhijian Yang, Xinyu Tang, Haotian Yang, Shang Han, Song MIT Cambridge MA 02139 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level. However, their latency lags behind sparse convolution-based models (3x slower), hindering their usage in resource-constrained, latency-sensitive applications (such as autonomous driving). This inefficiency comes from point clouds' sparse and irregular nature, whereas transformers are designed for dense, regular workloads. This paper presents FlatFormer to close this latency gap by trading spatial proximity for better computational regularity. We first flatten the point cloud with window-based sorting and partition points into groups of equal sizes rather than windows of equal shapes. This effectively avoids expensive structuring and padding overheads. We then apply self-attention within groups to extract local features, alternate sorting axis to gather features from different directions, and shift windows to exchange features across groups. FlatFormer delivers state-of-the-art accuracy on Waymo Open Dataset with 4.6x speedup over (transformer-based) SST and 1.4x speedup over (sparse convolutional) CenterPoint. This is the first point cloud transformer that achieves real-time performance on edge GPUs and is faster than sparse convolutional methods while achieving on-par or even superior accuracy on large-scale benchmarks.

关键词： Efficient and scalable vision

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Progressive Open Space Expansion for Open-Set Model Attribution

Progressive Open Space Expansion for Open-Set Model Attribut...

引用

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

作者： Yang, Tianyun Wang, Danding Tang, Fan Zhao, Xinying Cao, Juan Tang, Sheng Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhejiang Lab Res Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing openset recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods. Github: https://***/ICTMCG/POSE

关键词： accountability ethics in vision fairness privacy Transparency

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Neural Transformation Fields for Arbitrary-Styled Font Generation

Neural Transformation Fields for Arbitrary-Styled Font Gener...

引用

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

作者： Fu, Bin He, Junjun Wang, Jianjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://***/fubinfb/NTF.

关键词： Image and video synthesis and generation

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3D Video Object Detection with Learnable Object-Centric Global Optimization

3D Video Object Detection with Learnable Object-Centric Glob...

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

作者： He, Jiawei Chen, Yuntao Wang, Naiyan Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat CRIPAC Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci HKISI Ctr Artificial Intelligence & Robot Beijing Peoples R China TuSimple San Diego CA USA

ISBN: (纸本)9798350301298

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and feature-metric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://***/jiaweihe1996/BA-Det.

关键词： detection recognition: Categorization retrieval

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Ingredient-oriented Multi-Degradation Learning for Image Restoration

Ingredient-oriented Multi-Degradation Learning for Image Res...

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

作者： Zhang, Jinghao Huang, Jie Yao, Mingde Yang, Zizheng Yu, Hu Zhou, Man Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamental ingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via an ingredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.

关键词： Low-level vision

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Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Bias Mimicking: A Simple Sampling Approach for Bias Mitigati...

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

作者： Qraitem, Maan Saenko, Kate Plummer, Bryan A. Boston Univ Boston MA 02215 USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Prior work has shown that Visual recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyper-parameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class c bias distribution, i.e. PD(B| Y = c) is mimicked across every c ' not equal c, then Y and B are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https: //***/mqraitem/Bias-Mimicking

关键词： accountability accountability ethics in vision fairness privacy Transparency

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