GenAI4ELab/papercli-papers huggingface dataset:
Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning
Authors: Jiahan Li; Jiuyang Dong; Shenjin Huang; Xi Li; Junjun Jiang; Xiaopeng Fan; Yongbing Zhang
Venue: CVPR 2024 (main)

Abstract: Recently virtual staining technology has greatly promoted the advancement of histopathology. Despite the practical successes achieved the outstanding performance of most virtual staining methods relies on hard-to-obtain paired images in training. In this paper we propose a method for virtual immunohistochemistry (IHC) staining named confusion-GAN which does not require paired images and can achieve comparable performance to supervised algorithms. Specifically we propose a multi-branch discriminator which judges if the features of generated images can be embedded into the feature pool of target domain images to improve the visual quality of generated images. Meanwhile we also propose a novel patch-level pathology information extractor which is assisted by multiple instance learning to ensure pathological consistency during virtual staining. Extensive experiments were conducted on three types of IHC images including a high-resolution hepatocellular carcinoma immunohistochemical dataset proposed by us. The results demonstrated that our proposed confusion-GAN can generate highly realistic images that are capable of deceiving even experienced pathologists. Furthermore compared to using H&E images directly the downstream diagnosis achieved higher accuracy when using images generated by confusion-GAN. Our dataset and codes will be available at https://github.com/jiahanli2022/confusion-GAN.
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Authors: Yunxiao Shi; Manish Kumar Singh; Hong Cai; Fatih Porikli
Venue: CVPR 2024 (main)

Abstract: In this paper we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. Specifically we first enhance a baseline convolutional depth completion model by applying attention to 2D features in the bottleneck and skip connections. This effectively improves the performance of this simple network and sets it on par with the latest complex transformer-based models. Leveraging the initial depths and features from this network we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it allowing the model to explicitly learn and exploit 3D geometric features. In addition we propose normalization techniques to process the point cloud which improves learning and leads to better accuracy than directly using point transformers off the shelf. Furthermore we incorporate global attention on downsampled point cloud features which enables long-range context while still being computationally feasible. We evaluate our method DeCoTR on established depth completion benchmarks including NYU Depth V2 and KITTI showcasing that it sets new state-of-the-art performance. We further conduct zero-shot evaluations on ScanNet and DDAD benchmarks and demonstrate that DeCoTR has superior generalizability compared to existing approaches.
Utility-Fairness Trade-Offs and How to Find Them
Authors: Sepehr Dehdashtian; Bashir Sadeghi; Vishnu Naresh Boddeti
Venue: CVPR 2024 (main)

Abstract: When building classification systems with demographic fairness considerations there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fairness w.r.t. a known demographic attribute. These objectives often compete so optimizing both can lead to a trade-off between utility and fairness. While existing works acknowledge the trade-offs and study their limits two questions remain unanswered: 1) What are the optimal tradeoffs between utility and fairness? and 2) How can we numerically quantify these trade-offs from data for a desired prediction task and demographic attribute of interest? This paper addresses these questions. We introduce two utility-fairness trade-offs: the Data-Space and Label-Space Trade-off. The trade-offs reveal three regions within the utility-fairness plane delineating what is fully and partially possible and impossible. We propose U-FaTE a method to numerically quantify the trade-offs for a given prediction task and group fairness definition from data samples. Based on the trade-offs we introduce a new scheme for evaluating representations. An extensive evaluation of fair representation learning methods and representations from over 1000 pre-trained models revealed that most current approaches are far from the estimated and achievable fairness-utility trade-offs across multiple datasets and prediction tasks.
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
Authors: Yeonguk Yu; Sungho Shin; Seunghyeok Back; Mihwan Ko; Sangjun Noh; Kyoobin Lee
Venue: CVPR 2024 (main)

Abstract: Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-training with pseudo-labels since ground-truth cannot be obtained from test data. Although the quality of pseudo labels is important for stable and accurate long-term adaptation it has not been previously addressed. In this work we propose DPLOT a simple yet effective TTA framework that consists of two components: (1) domain-specific block selection and (2) pseudo-label generation using paired-view images. Specifically we select blocks that involve domain-specific feature extraction and train these blocks by entropy minimization. After blocks are adjusted for current test domain we generate pseudo-labels by averaging given test images and corresponding flipped counterparts. By simply using flip augmentation we prevent a decrease in the quality of the pseudo-labels which can be caused by the domain gap resulting from strong augmentation. Our experimental results demonstrate that DPLOT outperforms previous TTA methods in CIFAR10-C CIFAR100-C and ImageNet-C benchmarks reducing error by up to 5.4% 9.1% and 2.9% respectively. Also we provide an extensive analysis to demonstrate effectiveness of our framework. Code is available at https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-TTA.
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
Authors: Yuqi Zhang; Guanying Chen; Jiaxing Chen; Shuguang Cui
Venue: CVPR 2024 (main)

Abstract: We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly objects in urban aerial images exhibit substantial variations in size including buildings cars and roads which pose a significant challenge for accurate 2D segmentation. Secondly the 2D labels generated by existing segmentation methods suffer from the multi-view inconsistency problem especially in the case of aerial images where each image captures only a small portion of the entire scene. To overcome these limitations we first introduce a scale-adaptive semantic label fusion strategy that enhances the segmentation of objects of varying sizes by combining labels predicted from different altitudes harnessing the novel-view synthesis capabilities of NeRF. We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels. Furthermore we exploit multi-view reconstructed depth priors to improve the geometric quality of the reconstructed radiance field resulting in enhanced segmentation results. Experiments on multiple real-world urban-scale datasets demonstrate that our approach outperforms existing methods highlighting its effectiveness. The source code is available at https://github.com/zyqz97/Aerial_lifting.
SAOR: Single-View Articulated Object Reconstruction
Authors: Mehmet Aygun; Oisin Mac Aodha
Venue: CVPR 2024 (main)

Abstract: We introduce SAOR a novel approach for estimating the 3D shape texture and viewpoint of an articulated object from a single image captured in the wild. Unlike prior approaches that rely on pre-defined category-specific 3D templates or tailored 3D skeletons SAOR learns to articulate shapes from single-view image collections with a skeleton-free part-based model without requiring any 3D object shape priors. To prevent ill-posed solutions we propose a cross-instance consistency loss that exploits disentangled object shape deformation and articulation. This is helped by a new silhouette-based sampling mechanism to enhance viewpoint diversity during training. Our method only requires estimated object silhouettes and relative depth maps from off-the-shelf pre-trained networks during training. At inference time given a single-view image it efficiently outputs an explicit mesh representation. We obtain improved qualitative and quantitative results on challenging quadruped animals compared to relevant existing work.
A Theory of Joint Light and Heat Transport for Lambertian Scenes
Authors: Mani Ramanagopal; Sriram Narayanan; Aswin C. Sankaranarayanan; Srinivasa G. Narasimhan
Venue: CVPR 2024 (main)

Abstract: We present a novel theory that establishes the relationship between light transport in visible and thermal infrared and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.
iKUN: Speak to Trackers without Retraining
Authors: Yunhao Du; Cheng Lei; Zhicheng Zhao; Fei Su
Venue: CVPR 2024 (main)

Abstract: Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions. Previous works realize it by simply integrating an extra textual module into the multi-object tracker. However they typically need to retrain the entire framework and have difficulties in optimization. In this work we propose an insertable Knowledge Unification Network termed iKUN to enable communication with off-the-shelf trackers in a plug-and-play manner. Concretely a knowledge unification module (KUM) is designed to adaptively extract visual features based on textual guidance. Meanwhile to improve the localization accuracy we present a neural version of Kalman filter (NKF) to dynamically adjust process noise and observation noise based on the current motion status. Moreover to address the problem of open-set long-tail distribution of textual descriptions a test-time similarity calibration method is proposed to refine the confidence score with pseudo frequency. Extensive experiments on Refer-KITTI dataset verify the effectiveness of our framework. Finally to speed up the development of RMOT we also contribute a more challenging dataset Refer-Dance by extending public DanceTrack dataset with motion and dressing descriptions. The codes and dataset are available at https://github.com/dyhBUPT/iKUN.
RankMatch: Exploring the Better Consistency Regularization for Semi-supervised Semantic Segmentation
Authors: Huayu Mai; Rui Sun; Tianzhu Zhang; Feng Wu
Venue: CVPR 2024 (main)

Abstract: The key lie in semi-supervised semantic segmentation is how to fully exploit substantial unlabeled data to improve the model's generalization performance by resorting to constructing effective supervision signals. Most methods tend to directly apply contrastive learning to seek additional supervision to complement independent regular pixel-wise consistency regularization. However these methods tend not to be preferred ascribed to their complicated designs heavy memory footprints and susceptibility to confirmation bias. In this paper we analyze the bottlenecks exist in contrastive learning-based methods and offer a fresh perspective on inter-pixel correlations to construct more safe and effective supervision signals which is in line with the nature of semantic segmentation. To this end we develop a coherent RankMatch network including the construction of representative agents to model inter-pixel correlation beyond regular individual pixel-wise consistency and further unlock the potential of agents by modeling inter-agent relationships in pursuit of rank-aware correlation consistency. Extensive experimental results on multiple benchmarks including mitochondria segmentation demonstrate that RankMatch performs favorably against state-of-the-art methods. Particularly in the low-data regimes RankMatch achieves significant improvements.
Facial Identity Anonymization via Intrinsic and Extrinsic Attention Distraction
Authors: Zhenzhong Kuang; Xiaochen Yang; Yingjie Shen; Chao Hu; Jun Yu
Venue: CVPR 2024 (main)

Abstract: The unprecedented capture and application of face images raise increasing concerns on anonymization to fight against privacy disclosure. Most existing methods may suffer from the problem of excessive change of the identity-independent information or insufficient identity protection. In this paper we present a new face anonymization approach by distracting the intrinsic and extrinsic identity attentions. On the one hand we anonymize the identity information in the feature space by distracting the intrinsic identity attention. On the other we anonymize the visual clues (i.e. appearance and geometry structure) by distracting the extrinsic identity attention. Our approach allows for flexible and intuitive manipulation of face appearance and geometry structure to produce diverse results and it can also be used to instruct users to perform personalized anonymization. We conduct extensive experiments on multiple datasets and demonstrate that our approach outperforms state-of-the-art methods.
3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation
Authors: Songchun Zhang; Yibo Zhang; Quan Zheng; Rui Ma; Wei Hua; Hujun Bao; Weiwei Xu; Changqing Zou
Venue: CVPR 2024 (main)

Abstract: Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However these methods heavily rely on the outputs of existing models leading to error accumulation in geometry and appearance that prevent the models from being used in various scenarios (e.g. outdoor and unreal scenarios). To address this limitation we generatively refine the newly generated local views by querying and aggregating global 3D information and then progressively generate the 3D scene. Specifically we employ a tri-plane features-based NeRF as a unified representation of the 3D scene to constrain global 3D consistency and propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior from 2D diffusion model as well as the global 3D information of the current scene. Our extensive experiments demonstrate that in comparison to previous methods our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
VMINer: Versatile Multi-view Inverse Rendering with Near- and Far-field Light Sources
Authors: Fan Fei; Jiajun Tang; Ping Tan; Boxin Shi
Venue: CVPR 2024 (main)

Abstract: This paper introduces a versatile multi-view inverse rendering framework with near- and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering our framework adopts a lightweight yet inclusive lighting model for different near- and far-field lights thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering software for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.
RoHM: Robust Human Motion Reconstruction via Diffusion
Authors: Siwei Zhang; Bharat Lal Bhatnagar; Yuanlu Xu; Alexander Winkler; Petr Kadlecek; Siyu Tang; Federica Bogo
Venue: CVPR 2024 (main)

Abstract: We propose RoHM an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. RoHM is a novel diffusion-based motion model that conditioned on noisy and occluded input data reconstructs complete plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models one for global trajectory and one for local motion. To capture the correlations between the two we then introduce a novel conditioning module combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively while being faster at test time. The code is available at https://sanweiliti.github.io/ROHM/ROHM.html.
Do You Remember? Dense Video Captioning with Cross-Modal Memory Retrieval
Authors: Minkuk Kim; Hyeon Bae Kim; Jinyoung Moon; Jinwoo Choi; Seong Tae Kim
Venue: CVPR 2024 (main)

Abstract: There has been significant attention to the research on dense video captioning which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model without extensive pretraining from a large video dataset. Our code is available at https://github.com/ailab-kyunghee/CM2_DVC.
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation
Authors: Yuanchen Wu; Xichen Ye; Kequan Yang; Jide Li; Xiaoqiang Li
Venue: CVPR 2024 (main)

Abstract: Recently One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of Class Activation Map (CAM) we observe that one-stage pipelines often encounter confirmation bias caused by incorrect CAM pseudo-labels impairing their final segmentation performance. Although recent works discard many unreliable pseudo-labels to implicitly alleviate this issue they fail to exploit sufficient supervision for their models. To this end we propose a dual student framework with trustworthy progressive learning (DuPL). Specifically we propose a dual student network with a discrepancy loss to yield diverse CAMs for each sub-net. The two sub-nets generate supervision for each other mitigating the confirmation bias caused by learning their own incorrect pseudo-labels. In this process we progressively introduce more trustworthy pseudo-labels to be involved in the supervision through dynamic threshold adjustment with an adaptive noise filtering strategy. Moreover we believe that every pixel even discarded from supervision due to its unreliability is important for WSSS. Thus we develop consistency regularization on these discarded regions providing supervision of every pixel. Experiment results demonstrate the superiority of the proposed DuPL over the recent state-of-the-art alternatives on PASCAL VOC 2012 and MS COCO datasets. Code is available at https://github.com/Wu0409/DuPL.
Learning with Structural Labels for Learning with Noisy Labels
Authors: Noo-ri Kim; Jin-Seop Lee; Jee-Hyong Lee
Venue: CVPR 2024 (main)

Abstract: Deep Neural Networks (DNNs) have demonstrated remarkable performance across diverse domains and tasks with large-scale datasets. To reduce labeling costs for large-scale datasets semi-automated and crowdsourcing labeling methods are developed but their labels are inevitably noisy. Learning with Noisy Labels (LNL) approaches aim to train DNNs despite the presence of noisy labels. These approaches utilize the memorization effect to select correct labels and refine noisy ones which are then used for subsequent training. However these methods encounter a significant decrease in the model's generalization performance due to the inevitably existing noise labels. To overcome this limitation we propose a new approach to enhance learning with noisy labels by incorporating additional distribution information--structural labels. In order to leverage additional distribution information for generalization we employ a reverse k-NN which helps the model in achieving a better feature manifold and mitigating overfitting to noisy labels. The proposed method shows outperformed performance in multiple benchmark datasets with IDN and real-world noisy datasets.