GenAI4ELab/papercli-papers huggingface dataset:
Diffusion-based Blind Text Image Super-Resolution
Authors: Yuzhe Zhang; Jiawei Zhang; Hao Li; Zhouxia Wang; Luwei Hou; Dongqing Zou; Liheng Bian
Venue: CVPR 2024 (main)

Abstract: Recovering degraded low-resolution text images is challenging especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently diffusion models have achieved great success in natural image synthesis and restoration due to their powerful data distribution modeling abilities and data generation capabilities. In this work we propose an Image Diffusion Model (IDM) to restore text images with realistic styles. For diffusion models they are not only suitable for modeling realistic image distribution but also appropriate for learning text distribution. Since text prior is important to guarantee the correctness of the restored text structure according to existing arts we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures. We further propose a Mixture of Multi-modality module (MoM) to make these two diffusion models cooperate with each other in all the diffusion steps. Extensive experiments on synthetic and real-world datasets demonstrate that our Diffusion-based Blind Text Image Super-Resolution (DiffTSR) can restore text images with more accurate text structures as well as more realistic appearances simultaneously. Code is available at https://github.com/YuzheZhang-1999/DiffTSR.
Consistent Prompting for Rehearsal-Free Continual Learning
Authors: Zhanxin Gao; Jun Cen; Xiaobin Chang
Venue: CVPR 2024 (main)

Abstract: Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt based methods are inconsistent between training and testing limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper we propose a novel prompt-based method Consistent Prompting (CPrompt) for more aligned training and testing. Specifically all existing classifiers are exposed to prompt training resulting in classifier consistency learning. In addition prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing.
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
Authors: Honghui Yang; Sha Zhang; Di Huang; Xiaoyang Wu; Haoyi Zhu; Tong He; Shixiang Tang; Hengshuang Zhao; Qibo Qiu; Binbin Lin; Xiaofei He; Wanli Ouyang
Venue: CVPR 2024 (main)

Abstract: In the context of autonomous driving the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success most methods follow the ideas originally designed for 2D images. In this paper we present UniPAD a novel self-supervised learning paradigm applying 3D volumetric differentiable rendering. UniPAD implicitly encodes 3D space facilitating the reconstruction of continuous 3D shape structures and the intricate appearance characteristics of their 2D projections. The flexibility of our method enables seamless integration into both 2D and 3D frameworks enabling a more holistic comprehension of the scenes. We manifest the feasibility and effectiveness of UniPAD by conducting extensive experiments on various 3D perception tasks. Our method significantly improves lidar- camera- and lidar-camera-based baseline by 9.1 7.7 and 6.9 NDS respectively. Notably our pre-training pipeline achieves 73.2 NDS for 3D object detection and 79.4 mIoU for 3D semantic segmentation on the nuScenes validation set achieving state-of-the-art results in comparison with previous methods.
SeD: Semantic-Aware Discriminator for Image Super-Resolution
Authors: Bingchen Li; Xin Li; Hanxin Zhu; Yeying Jin; Ruoyu Feng; Zhizheng Zhang; Zhibo Chen
Venue: CVPR 2024 (main)

Abstract: Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner. However the distribution learning is overly coarse-grained which is susceptible to virtual textures and causes counter-intuitive generation results. To mitigate this we propose the simple and effective Semantic-aware Discriminator (denoted as SeD) which encourages the SR network to learn the fine-grained distributions by introducing the semantics of images as a condition. Concretely we aim to excavate the semantics of images from a well-trained semantic extractor. Under different semantics the discriminator is able to distinguish the real-fake images individually and adaptively which guides the SR network to learn the more fine-grained semantic-aware textures. To obtain accurate and abundant semantics we take full advantage of recently popular pretrained vision models (PVMs) with extensive datasets and then incorporate its semantic features into the discriminator through a well-designed spatial cross-attention module. In this way our proposed semantic-aware discriminator empowered the SR network to produce more photo-realistic and pleasing images. Extensive experiments on two typical tasks i.e. SR and Real SR have demonstrated the effectiveness of our proposed methods. The code will be available at https://github.com/lbc12345/SeD.
SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples
Authors: Phillip Howard; Avinash Madasu; Tiep Le; Gustavo Lujan Moreno; Anahita Bhiwandiwalla; Vasudev Lal
Venue: CVPR 2024 (main)

Abstract: While vision-language models (VLMs) have achieved remarkable performance improvements recently there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g. a given occupation) while differing only in their depiction of intersectional social attributes (e.g. race & gender). Through our over-generate-then-filter methodology we produce SocialCounterfactuals a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender race and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs.
SVDTree: Semantic Voxel Diffusion for Single Image Tree Reconstruction
Authors: Yuan Li; Zhihao Liu; Bedrich Benes; Xiaopeng Zhang; Jianwei Guo
Venue: CVPR 2024 (main)

Abstract: Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic voxel diffusion process which converts an input image of a tree to a novel Semantic Voxel Structure (SVS) in 3D space. The SVS encodes the geometric appearance and semantic structural information (e.g. classifying trunks branches and leaves) which retains the intricate internal tree features. Tailored to the SVS we present SVDTree a new hybrid tree modeling approach by combining structure-oriented branch reconstruction and self-organization-based foliage reconstruction. We validate SVDTree by using images from both synthetic and real trees. The comparison results show that our approach can better preserve tree details and achieve more realistic and accurate reconstruction results than previous methods.
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Authors: Sadeep Jayasumana; Srikumar Ramalingam; Andreas Veit; Daniel Glasner; Ayan Chakrabarti; Sanjiv Kumar
Venue: CVPR 2024 (main)

Abstract: As with many machine learning problems the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images and those of images generated by the algorithm. We highlight important drawbacks of FID: Inception's poor representation of the rich and varied content generated by modern text-to-image models incorrect normality assumptions and poor sample complexity. We call for a reevaluation of FID's use as the primary quality metric for generated images. We empirically demonstrate that FID contradicts human raters it does not reflect gradual improvement of iterative text-to-image models it does not capture distortion levels and that it produces inconsistent results when varying the sample size. We also propose an alternative new metric CMMD based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis we demonstrate that FID-based evaluations of text-to-image models may be unreliable and that CMMD offers a more robust and reliable assessment of image quality.
Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
Authors: Heejoon Moon; Chunghwan Lee; Je Hyeong Hong
Venue: CVPR 2024 (main)

Abstract: The recent success in revealing scene details from sparse 3D point clouds obtained via structure-from-motion has raised significant privacy concerns in visual localization. One prominent approach for mitigating this issue is to lift 3D points to 3D lines thereby reducing the effectiveness of the scene inversion attacks but this comes at the cost of increased algorithmic complexity for camera localization due to weaker geometric constraints induced by line clouds. To overcome this limitation we propose a new lifting approach called "ray cloud" whereby each lifted 3D line intersects at one of two predefined locations depicting omnidirectional rays from two cameras. This yields two benefits i) camera localization can now be cast as relative pose estimation between the query image and the calibrated rig of two perspective cameras which can be efficiently solved using a variant of the 5-point algorithm and ii) the ray cloud introduces erroneous estimations for the density-based inversion attack degrading the quality of scene recovery. Moreover we explore possible modifications of the inversion attack to better recover scenes from the ray clouds and propose a ray sampling technique to reduce the effectiveness of the modified attack. Experimental results on two public datasets show real-time localization speed as well as enhanced privacy-preserving capability over the state-of-the-art without overly sacrificing the localization accuracy.
SuperPrimitive: Scene Reconstruction at a Primitive Level
Authors: Kirill Mazur; Gwangbin Bae; Andrew J. Davison
Venue: CVPR 2024 (main)

Abstract: Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambiguities. Most dense incremental reconstruction systems operate directly on image pixels and solve for their 3D positions using multi-view geometry cues. Such pixel-level approaches suffer from ambiguities or violations of multi-view consistency (e.g. caused by textureless or specular surfaces). We address this issue with a new image representation which we call a SuperPrimitive. SuperPrimitives are obtained by splitting images into semantically correlated local regions and enhancing them with estimated surface normal directions both of which are predicted by state-of-the-art single image neural networks. This provides a local geometry estimate per SuperPrimitive while their relative positions are adjusted based on multi-view observations. We demonstrate the versatility of our new representation by addressing three 3D reconstruction tasks: depth completion few-view structure from motion and monocular dense visual odometry. Project page: https://makezur.github.io/SuperPrimitive/
ReCoRe: Regularized Contrastive Representation Learning of World Model
Authors: Rudra P.K. Poudel; Harit Pandya; Stephan Liwicki; Roberto Cipolla
Venue: CVPR 2024 (main)

Abstract: While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments their success in everyday tasks like visual navigation has been limited particularly under significant appearance variations. This limitation arises from (i) poor sample efficiency and (ii) over-fitting to training scenarios. To address these challenges we present a world model that learns invariant features using (i) contrastive unsupervised learning and (ii) an intervention-invariant regularizer. Learning an explicit representation of the world dynamics i.e. a world model improves sample efficiency while contrastive learning implicitly enforces learning of invariant features which improves generalization. However the naive integration of contrastive loss to world models is not good enough as world-model-based RL methods independently optimize representation learning and agent policy. To overcome this issue we propose an intervention-invariant regularizer in the form of an auxiliary task such as depth prediction image denoising image segmentation etc. that explicitly enforces invariance to style interventions. Our method outperforms current state-of-the-art model-based and model-free RL methods and significantly improves on out-of-distribution point navigation tasks evaluated on the iGibson benchmark. With only visual observations we further demonstrate that our approach outperforms recent language-guided foundation models for point navigation which is essential for deployment on robots with limited computation capabilities. Finally we demonstrate that our proposed model excels at the sim-to-real transfer of its perception module on the Gibson benchmark.
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
Authors: Yushi Huang; Ruihao Gong; Jing Liu; Tianlong Chen; Xianglong Liu
Venue: CVPR 2024 (main)

Abstract: The Diffusion model a prevalent framework for image generation encounters significant challenges in terms of broad applicability due to its extended inference times and substantial memory requirements. Efficient Post-training Quantization (PTQ) is pivotal for addressing these issues in traditional models. Different from traditional models diffusion models heavily depend on the time-step t to achieve satisfactory multi-round denoising. Usually t from the finite set \ 1 \ldots T\  is encoded to a temporal feature by a few modules totally irrespective of the sampling data. However existing PTQ methods do not optimize these modules separately. They adopt inappropriate reconstruction targets and complex calibration methods resulting in a severe disturbance of the temporal feature and denoising trajectory as well as a low compression efficiency. To solve these we propose a Temporal Feature Maintenance Quantization (TFMQ) framework building upon a Temporal Information Block which is just related to the time-step t and unrelated to the sampling data. Powered by the pioneering block design we devise temporal information aware reconstruction (TIAR) and finite set calibration (FSC) to align the full-precision temporal features in a limited time. Equipped with the framework we can maintain the most temporal information and ensure the end-to-end generation quality. Extensive experiments on various datasets and diffusion models prove our state-of-the-art results. Remarkably our quantization approach for the first time achieves model performance nearly on par with the full-precision model under 4-bit weight quantization. Additionally our method incurs almost no extra computational cost and accelerates quantization time by 2.0 xon LSUN-Bedrooms 256 x256 compared to previous works. Our code is publicly available at \href https://github.com/ModelTC/TFMQ-DM  https://github.com/ModelTC/TFMQ-DM .
CNC-Net: Self-Supervised Learning for CNC Machining Operations
Authors: Mohsen Yavartanoo; Sangmin Hong; Reyhaneh Neshatavar; Kyoung Mu Lee
Venue: CVPR 2024 (main)

Abstract: CNC manufacturing is a process that employs computer numerical control (CNC) machines to govern the movements of various industrial tools and machinery encompassing equipment ranging from grinders and lathes to mills and CNC routers. However the reliance on manual CNC programming has become a bottleneck and the requirement for expert knowledge can result in significant costs. Therefore we introduce a pioneering approach named CNC-Net representing the use of deep neural networks (DNNs) to simulate CNC machines and grasp intricate operations when supplied with raw materials. CNC-Net constitutes a self-supervised framework that exclusively takes an input 3D model and subsequently generates the essential operation parameters required by the CNC machine to construct the object. Our method has the potential to transformative automation in manufacturing by offering a cost-effective alternative to the high costs of manual CNC programming while maintaining exceptional precision in 3D object production. Our experiments underscore the effectiveness of our CNC-Net in constructing the desired 3D objects through the utilization of CNC operations. Notably it excels in preserving finer local details exhibiting a marked enhancement in precision compared to the state-of-the-art 3D CAD reconstruction approaches. The codes are available at https://github.com/myavartanoo/CNC-Net_PyTorch.
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments
Authors: Duy Tho Le; Chenhui Gou; Stavya Datta; Hengcan Shi; Ian Reid; Jianfei Cai; Hamid Rezatofighi
Venue: CVPR 2024 (main)

Abstract: Autonomous robot systems have attracted increasing research attention in recent years where environment understanding is a crucial step for robot navigation human-robot interaction and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-crowded settings. Traditional benchmarks with their reliance on single sensors and limited object classes and scenarios fail to provide the comprehensive environmental understanding robots need for accurate navigation interaction and decision-making. As an extension of JRDB dataset we unveil JRDB-PanoTrack a novel open-world panoptic segmentation and tracking benchmark towards more comprehensive environmental perception. JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes as well as comprehensive 2D and 3D synchronized data modalities; (2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations with additional 3D label projections for further spatial understanding; (3) diverse object classes for closed- and open-world recognition benchmarks with OSPA-based metrics for evaluation. Extensive evaluation of leading methods shows significant challenges posed by our dataset.
CONFORM: Contrast is All You Need for High-Fidelity Text-to-Image Diffusion Models
Authors: Tuna Han Salih Meral; Enis Simsar; Federico Tombari; Pinar Yanardag
Venue: CVPR 2024 (main)

Abstract: 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. While recent studies propose various solutions they 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 conducted 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.
Self-Supervised Facial Representation Learning with Facial Region Awareness
Authors: Zheng Gao; Ioannis Patras
Venue: CVPR 2024 (main)

Abstract: Self-supervised pre-training has been proved to be effective in learning transferable representations that benefit various visual tasks. This paper asks this question: can self-supervised pre-training learn general facial representations for various facial analysis tasks? Recent efforts toward this goal are limited to treating each face image as a whole i.e. learning consistent facial representations at the image-level which overlooks the consistency of local facial representations (i.e. facial regions like eyes nose etc). In this work we make a first attempt to propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations Facial Region Awareness (FRA). Specifically we explicitly enforce the consistency of facial regions by matching the local facial representations across views which are extracted with learned heatmaps highlighting the facial regions. Inspired by the mask prediction in supervised semantic segmentation we obtain the heatmaps via cosine similarity between the per-pixel projection of feature maps and facial mask embeddings computed from learnable positional embeddings which leverage the attention mechanism to globally look up the facial image for facial regions. To learn such heatmaps we formulate the learning of facial mask embeddings as a deep clustering problem by assigning the pixel features from the feature maps to them. The transfer learning results on facial classification and regression tasks show that our FRA outperforms previous pre-trained models and more importantly using ResNet as the unified backbone for various tasks our FRA achieves comparable or even better performance compared with SOTA methods in facial analysis tasks.