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SinSR: Diffusion-Based Image Super-Resolution in a Single Step

SinSR: Diffusion-Based Image Super-Resolution in a Single St...

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conference on computer vision and pattern recognition (CVPR)

作者： Yufei Wang Wenhan Yang Xinyuan Chen Yaohui Wang Lanqing Guo Lap-Pui Chau Ziwei Liu Yu Qiao Alex C. Kot Bihan Wen Nanyang Technological University Shanghai Artificial Intelligence Laboratory PengCheng Laboratory The Hong Kong Polytechnic University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference steps. Recent methods utilize the degraded images in the initial state, thereby shortening the Markov chain. Nevertheless, these solutions either rely on a precise formulation of the degradation process or still necessitate a relatively lengthy generation path (e.g., 15 iterations). To enhance inference speed, we propose a simple yet effective method for achieving single-step SR generation, named SinSR. Specifically, we first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR. This allows the mapping between the input random noise and the generated high-resolution image to be obtained in a reduced and acceptable number of inference steps during training. We show that this deterministic mapping can be distilled into a student model that performs SR within only one inference step. Additionally, we propose a novel consistency-preserving loss to simultaneously leverage the ground-truth image during the distillation process, ensuring that the performance of the student model is not solely bound by the feature manifold of the teacher model, resulting in further performance improvement. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to × 10 speedup for inference. Our code will be released at https://***/wyf0912/SinSR/.

关键词： Training Manifolds Degradation computer vision Codes Computational modeling Superresolution

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GS-IR: 3D Gaussian Splatting for Inverse Rendering

GS-IR: 3D Gaussian Splatting for Inverse Rendering

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conference on computer vision and pattern recognition (CVPR)

作者： Zhihao Liang Qi Zhang Ying Feng Ying Shan Kui Jia South China University of Technology Tencent AI Lab School of Data Science The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (3DGS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results. Unlike previous works that use implicit neural representations and volume rendering (e.g. NeRF), which suffer from low expressive power and high computational complexity, we extend 3DGS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. There are two main problems when introducing 3DGS to inverse rendering: 1) 3DGS does not support producing plausible normal natively; 2) forward mapping (e.g. rasterization and splatting) cannot trace the occlusion like backward mapping (e.g. ray tracing). To address these challenges, our GS-IR proposes an efficient optimization scheme incorporating a depth-derivation-based regularization for normal estimation and a baking-based occlusion to model indirect lighting. The flexible and expressive 3DGS representation allows us to achieve fast and compact geometry reconstruction, photore-alistic novel view synthesis, and effective physically-based rendering. We demonstrate the superiority of our method over baseline methods through qualitative and quantitative evaluations of various challenging scenes. The source code is available at https://***/lzhnb/GS-IR.

关键词： Geometry Three-dimensional displays Source coding Lighting Estimation Ray tracing Rendering (computer graphics)

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DragDiffusion: Harnessing Diffusion Models for Interactive Point-Based Image Editing

DragDiffusion: Harnessing Diffusion Models for Interactive P...

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conference on computer vision and pattern recognition (CVPR)

作者： Yujun Shi Chuhui Xue Jun Hao Liew Jiachun Pan Hanshu Yan Wenqing Zhang Vincent Y. F. Tan Song Bai National University of Singapore ByteDance Inc

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DRAGGAN developed by Pan et al. (2023) [33] is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. However, due to its reliance on generative adversarial networks (GANs), its generality is limited by the capacity of pretrained GAN models. In this work, we extend this editing framework to diffusion models and propose a novel approach Dragdiffusion. By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images. Unlike other diffusion-based editing methods that provide guidance on diffusion latents of multiple time steps, our approach achieves efficient yet accurate spatial control by optimizing the latent of only one time step. This novel design is motivated by our observations that UNet features at a specific time step provides sufficient semantic and geometric information to support the drag-based editing. Moreover, we introduce two additional techniques, namely identity-preserving fine-tuning and reference-latent-control, to further preserve the identity of the original image. Lastly, we present a challenging benchmark dataset called DRAGBENCH─ the first benchmark to evaluate the performance of interactive point-based image editing methods. Experiments across a wide range of challenging cases (e.g., images with multiple objects, diverse object categories, various styles, etc.) demonstrate the versatility and generality of Dragdiffusion. Code and the Dragbench dataset: https://***/Yujun-Shi/DragDiffusion.

关键词： computer vision Accuracy Codes Semantics Benchmark testing Diffusion models Generative adversarial networks

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FSC: Few-Point Shape Completion

FSC: Few-Point Shape Completion

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conference on computer vision and pattern recognition (CVPR)

作者： Xianzu Wu Xianfeng Wu Tianyu Luan Yajing Bai Zhongyuan Lai Junsong Yuan State Key Laboratory of Precision Blasting Jianghan University Wuhan Hubei China School of Geophysics and Petroleum Resources Yangtze University Wuhan Hubei China Department of Computer Science and Engineering University at Buffalo Buffalo New York

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e.g. tens of points, are observed. Surprisingly, via entropy analysis, we find that even a few points, e.g. 64 points, could retain substantial information to help recover the 3D shape of the object. To address the challenge of shape completion with very sparse point clouds, we then propose Few-point Shape Completion (FSC) model, which contains a novel dual-branch feature extractor for handling extremely sparse inputs, coupled with an extensive branch for maximal point utilization with a saliency branch for dynamic importance assignment. This model is further bolstered by a two-stage revision network that refines both the extracted features and the decoder output, enhancing the detail and authenticity of the completed point cloud. Our experiments demonstrate the feasibility of recovering 3D shapes from a few points. The proposed Few-point Shape Completion (FSC) model outperforms previous methods on both few-point inputs and many-point inputs, and shows good gener-alizability to different object categories. Code is available at https: https://***/xianzuwu/FSC.

关键词： Point cloud compression Three-dimensional displays Shape Feature extraction Robustness Entropy Encoding

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Modeling Collaborator: Enabling Subjective vision Classification with Minimal Human Effort via LLM Tool-Use

Modeling Collaborator: Enabling Subjective Vision Classifica...

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conference on computer vision and pattern recognition (CVPR)

作者： Imad Eddine Toubal Aditya Avinash Neil Gordon Alldrin Jan Dlabal Wenlei Zhou Enming Luo Otilia Stretcu Hao Xiong Chun-Ta Lu Howard Zhou Ranjay Krishna Ariel Fuxman Tom Duerig Google Research University of Missouri University of Washington

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques, which enable rapid bootstrapping of image classifiers, users are still required to spend 30 minutes or more of monotonous, repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory, we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions, reducing the total effort required to define a concept by an order of magnitude: from labeling 2,000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points. Most importantly, our framework eliminates the need for crowd-sourced annotations. Moreover, our framework ultimately produces lightweight classification models that are deploy-able in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets, our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question answering models like PaLI-X.

关键词： Visualization Computational modeling Large language models Natural languages Wildlife Training data Manuals

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Open-World Human-Object Interaction Detection via Multi-Modal Prompts

Open-World Human-Object Interaction Detection via Multi-Moda...

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conference on computer vision and pattern recognition (CVPR)

作者： Jie Yang Bingliang Li Ailing Zeng Lei Zhang Ruimao Zhang The Chinese University of Hong Kong Shenzhen International Digital Economy Academy

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we develop MP-HOI, a powerful Multimodal Prompt-based HOI detector designed to leverage both textual descriptions for open-set generalization and visual exemplars for handling high ambiguity in descriptions, realizing HOI detection in the open world. Specifically, it integrates visual prompts into existing language-guided-only HOI detectors to handle situations where textual descriptions face difficulties in generalization and to address complex scenarios with high interaction ambiguity. To facilitate MP-HOI training, we build a large-scale HOI dataset named Magic-HOI, which gathers six existing datasets into a unified label space, forming over 186K images with 2.4K objects, 1.2K actions, and 20K HOI interactions. Furthermore, to tackle the long-tail issue within the Magic-HOI dataset, we introduce an automated pipeline for generating realistically annotated HOI images and present SynHOI, a high-quality synthetic HOI dataset containing 100K images. Leveraging these two datasets, MP-HOI optimizes the HOI task as a similarity learning process between multi-modal prompts and objects/interactions via a unified contrastive loss, to learn generalizable and transferable objects/interactions representations from large-scale data. MP-HOI could serve as a generalist HOI detector, surpassing the HOI vocabulary of existing expert models by more than 30 times. Concurrently, our results demonstrate that MP-HOI exhibits remarkable zero-shot capability in real- world scenarios and consistently achieves a new state-of-the-art performance across various benchmarks. Our project homepage is available at https://***.

关键词： Training Visualization Vocabulary computer vision Pipelines Detectors Benchmark testing

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Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields

Geometry-aware Reconstruction and Fusion-refined Rendering f...

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conference on computer vision and pattern recognition (CVPR)

作者： Tianqi Liu Xinyi Ye Min Shi Zihao Huang Zhiyu Pan Zhan Peng Zhiguo Cao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D descriptors for decoding novel views. However, existing methods show limited generalization ability in challenging conditions due to inaccurate geometry, sub-optimal descriptors, and decoding strategies. We address these issues point by point. First, we find the variance-based cost volume exhibits failure patterns as the features of pixels corresponding to the same point can be inconsistent across different views due to occlusions or reflections. We introduce an Adaptive Cost Aggregation (ACA) approach to amplify the contribution of consistent pixel pairs and suppress inconsistent ones. Un-like previous methods that solely fuse 2D features into descriptors, our approach introduces a Spatial-View Ag-gregator (SVA) to incorporate 3D context into descriptors through spatial and inter-view interaction. When decoding the descriptors, we observe the two existing decoding strategies excel in different areas, which are complementary. A Consistency-Aware Fusion (CAF) strategy is proposed to leverage the advantages of both. We incorporate the above ACA, SVA, and CAF into a coarse-to-fine framework, termed Geometry-aware Reconstruction and Fusion-refined Rendering (GeFu). GeFu attains state-of-the-art performance across multiple datasets. Code is available at https://***/TQTQliu/GeFu.

关键词： Geometry Costs Three-dimensional displays Fuses Estimation Rendering (computer graphics) Neural radiance field

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Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration

Modality-Agnostic Structural Image Representation Learning f...

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conference on computer vision and pattern recognition (CVPR)

作者： Tony C. W. Mok Zi Li Yunhao Bai Jianpeng Zhang Wei Liu Yan-Jie Zhou Ke Yan Dakai Jin Yu Shi Xiaoli Yin Le Lu Ling Zhang DAMO Academy Alibaba Group Hupan Lab Hangzhou China College of Computer Science and Technology Zhejiang University China Shengjing Hospital of China Medical University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multimodality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However, the former is sensitive to locally varying noise, while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans, causing ambiguity in determining the anatomical correspon-dence across scans with different modalities. In this paper, we propose a modality-agnostic structural representation learning method, which leverages Deep Neighbour-hood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT, abdomen MR-CT, and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.

关键词： Training Representation learning Learning systems Image registration Noise Contrastive learning Image representation

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PromptKD: Unsupervised Prompt Distillation for vision-Language Models

PromptKD: Unsupervised Prompt Distillation for Vision-Langua...

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conference on computer vision and pattern recognition (CVPR)

作者： Zheng Li Xiang Li Xinyi Fu Xin Zhang Weiqiang Wang Shuo Chen Jian Yang PCA Lab VCIP College of Computer Science Nankai University NKIARI Shenzhen Futian Tiansuan Lab Ant Group RIKEN

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pretraining, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://***/zhengli97/PromptKD.

关键词： computer vision Codes Computational modeling Prediction algorithms Data models Vectors Probability distribution

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Frequency Decoupling for Motion Magnification Via Multi-Level Isomorphic Architecture

Frequency Decoupling for Motion Magnification Via Multi-Leve...

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conference on computer vision and pattern recognition (CVPR)

作者： Fei Wang Dan Guo Kun Li Zhun Zhong Meng Wang School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observe that the low-frequency components with stable energy always possess spatial structure and less noise, making them suitable for modeling the subtle motion field. To this end, we present FD4MM, a new paradigm of Frequency Decoupling for Motion Magnification with a Multi-level Isomorphic Architecture to capture multi-level high-frequency details and a stable low-frequency structure (motion field) in video space. Since high-frequency details and subtle motions are susceptible to information degra-dation due to their inherent subtlety and unavoidable ex-ternal interference from noise, we carefully design Sparse High/Low-pass Filters to enhance the integrity of details and motion structures, and a Sparse Frequency Mixer to promote seamless recoupling. Besides, we innovatively design a contrastive regularization for this task to strengthen the model's ability to discriminate irrelevant features, re-ducing undesired motion magnification. Extensive experiments on both Real-world and Synthetic Datasets show that our FD4MM outperforms SOTA methods. Meanwhile, FD4MM reduces FLOPs by 1.63x and boosts inference speed by 1.68x than the latest method. Our code is available at https://***/Jiafei127/FD4MM.

关键词： Representation learning Filters Virtual machine monitors Shape Noise computer architecture Robustness

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