mtybilly/Instruct2DS huggingface dataset:
Multimodal Prompt Perceiver: Empower Adaptiveness Generalizability and Fidelity for All-in-One Image Restoration
Authors: Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He Yuang Ai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite substantial progress all-in-one image restoration (IR) grapples with persistent challenges in handling intricate real-world degradations. This paper introduces MPerceiver: a novel multimodal prompt learning approach that harnesses Stable Diffusion (SD) priors to enhance adaptiveness generalizability and fidelity for all-in-one image restoration. Specifically we develop a dual-branch module to master two types of SD prompts: textual for holistic representation and visual for multiscale detail representation. Both prompts are dynamically adjusted by degradation predictions from the CLIP image encoder enabling adaptive responses to diverse unknown degradations. Moreover a plug-in detail refinement module improves restoration fidelity via direct encoder-to-decoder information transformation. To assess our method MPerceiver is trained on 9 tasks for all-in-one IR and outperforms state-of-the-art task-specific methods across many tasks. Post multitask pre-training MPerceiver attains a generalized representation in low-level vision exhibiting remarkable zero-shot and few-shot capabilities in unseen tasks. Extensive experiments on 16 IR tasks underscore the superiority of MPerceiver in terms of adaptiveness generalizability and fidelity.
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More
Authors: Xu Zheng, Yuanhuiyi Lyu, Lin Wang Jiazhou Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Event cameras have recently been shown beneficial for practical vision tasks such as action recognition thanks to their high temporal resolution power efficiency and reduced privacy concerns. However current research is hindered by 1) the difficulty in processing events because of their prolonged duration and dynamic actions with complex and ambiguous semantics and 2) the redundant action depiction of the event frame representation with fixed stacks. We find language naturally conveys abundant semantic information rendering it stunningly superior in reducing semantic uncertainty. In light of this we propose ExACT a novel approach that for the first time tackles event-based action recognition from a cross-modal conceptualizing perspective. Our ExACT brings two technical contributions. Firstly we propose an adaptive fine-grained event (AFE) representation to adaptively filter out the repeated events for the stationary objects while preserving dynamic ones. This subtly enhances the performance of ExACT without extra computational cost. Then we propose a conceptual reasoning-based uncertainty estimation module which simulates the recognition process to enrich the semantic representation. In particular conceptual reasoning builds the temporal relation based on the action semantics and uncertainty estimation tackles the semantic uncertainty of actions based on the distributional representation. Experiments show that our ExACT achieves superior recognition accuracy of 94.83%(+2.23%) 90.10%(+37.47%) and 67.24% on PAF HARDVS and our SeAct datasets respectively.
Color Shift Estimation-and-Correction for Image Enhancement
Authors: Ke Xu, Gerhard Petrus Hancke, Rynson W.H. Lau Yiyu Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. We observe that over- and over-exposed regions display opposite color tone distribution shifts which may not be easily normalized in joint modeling as they usually do not have "normal-exposed" regions/pixels as reference. In this paper we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches.
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Authors: Jinhyun Jang, Jin Kim, Kwonyoung Kim, Kwanghoon Sohn Hyeongjun Kwon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual scenes are naturally organized in a hierarchy where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements leading to a comprehensive scene understanding. In this paper we propose a Visual Hierarchy Mapper (Hi-Mapper) a novel approach for enhancing the structured understanding of the pre-trained Deep Neural Networks (DNNs). Hi-Mapper investigates the hierarchical organization of the visual scene by 1) pre-defining a hierarchy tree through the encapsulation of probability densities; and 2) learning the hierarchical relations in hyperbolic space with a novel hierarchical contrastive loss. The pre-defined hierarchy tree recursively interacts with the visual features of the pre-trained DNNs through hierarchy decomposition and encoding procedures thereby effectively identifying the visual hierarchy and enhancing the recognition of an entire scene. Extensive experiments demonstrate that Hi-Mapper significantly enhances the representation capability of DNNs leading to an improved performance on various tasks including image classification and dense prediction tasks.
ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks
Authors: Yunhe Wang, Jianyuan Guo, Enhua Wu Kai Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The large-scale visual pretraining has significantly improve the performance of large vision models. However we observe the low FLOPs pitfall that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper we introduce a novel design principle termed ParameterNet aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example ParameterNet-600M can achieve higher accuracy than the widely-used Swin Transformer (81.6% vs. 80.9%) and has much lower FLOPs (0.6G vs. 4.5G). The code will be released at https://parameternet.github.io/.
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Authors: Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler Bingxin Ke
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity from relatively modest CNNs to large Transformer architectures. Still monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout since their knowledge of the visual world is restricted by the data seen during training and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better more generalizable depth estimation. We introduce Marigold a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.
Identifying Important Group of Pixels using Interactions
Authors: Kazuhiko Kawamoto, Hiroshi Kera Kosuke Sumiyasu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: To better understand the behavior of image classifiers it is useful to visualize the contribution of individual pixels to the model prediction. In this study we propose a method MoXI(Model eXplanation by Interactions) that efficiently and accurately identifies a group of pixels with high prediction confidence. The proposed method employs game-theoretic concepts Shapley values and interactions taking into account the effects of individual pixels and the cooperative influence of pixels on model confidence. Theoretical analysis and experiments demonstrate that our method better identifies the pixels that are highly contributing to the model outputs than widely-used by Grad-CAM Attention rollout and Shapley value. While prior studies have suffered from the exponential computational cost in the computation of Shapley value and interactions we show that this can be reduced to quadratic cost for our task. The code is available at https://github.com/KosukeSumiyasu/MoXI.
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network
Authors: Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, Yingna Wu Wenqiao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently 3D anomaly detection a crucial problem involving fine-grained geometry discrimination is getting more attention. However the lack of abundant real 3D anomaly data limits the scalability of current models. To enable scalable anomaly data collection we propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3D models for 3D anomaly detection. Specifically we construct a synthetic dataset i.e. Anomaly-ShapeNet based on ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories which provides a rich and varied collection of data enabling efficient training and enhancing adaptability to industrial scenarios. Meanwhile to enable scalable representation learning for 3D anomaly localization we propose a self-supervised method i.e. Iterative Mask Reconstruction Network (IMRNet). During training we propose a geometry-aware sample module to preserve potentially anomalous local regions during point cloud down-sampling. Then we randomly mask out point patches and sent the visible patches to a transformer for reconstruction-based self-supervision. During testing the point cloud repeatedly goes through the Mask Reconstruction Network with each iteration's output becoming the next input. By merging and contrasting the final reconstructed point cloud with the initial input our method successfully locates anomalies. Experiments show that IMRNet outperforms previous state-of-the-art methods achieving 66.1% in I-AUC on our Anomaly-ShapeNet dataset and 72.5% in I-AUC on Real3D-AD dataset. Our benchmark will be released at https://github.com/Chopper233/Anomaly-ShapeNet.
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Authors: Xieyuanli Chen, Jiawei Huang, Jingyi Xu, Zhen Luo, Jintao Xu, Weihao Gu, Rui Ai, Hesheng Wang Junyi Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Understanding how the surrounding environment changes is crucial for performing downstream tasks safely and reliably in autonomous driving applications. Recent occupancy estimation techniques using only camera images as input can provide dense occupancy representations of large-scale scenes based on the current observation. However they are mostly limited to representing the current 3D space and do not consider the future state of surrounding objects along the time axis. To extend camera-only occupancy estimation into spatiotemporal prediction we propose Cam4DOcc a new benchmark for camera-only 4D occupancy forecasting evaluating the surrounding scene changes in a near future. We build our benchmark based on multiple publicly available datasets including nuScenes nuScenes-Occupancy and Lyft-Level5 which provides sequential occupancy states of general movable and static objects as well as their 3D backward centripetal flow. To establish this benchmark for future research with comprehensive comparisons we introduce four baseline types from diverse camera-based perception and prediction implementations including a static-world occupancy model voxelization of point cloud prediction 2D-3D instance-based prediction and our proposed novel end-to-end 4D occupancy forecasting network. Furthermore the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios. The dataset and our implementation of all four baselines in the proposed Cam4DOcc benchmark are released as open source at https://github.com/haomo-ai/Cam4DOcc.
DIOD: Self-Distillation Meets Object Discovery
Authors: Hejer Ammar, Julien Denize, Florian Chabot, Quoc-Cuong Pham Sandra Kara
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Instance segmentation demands substantial labeling resources. This has prompted increased interest to explore the object discovery task as an unsupervised alternative. In particular promising results were achieved in localizing instances using motion supervision only. However the motion signal introduces complexities due to its inherent noise and sparsity which constrains the effectiveness of current methodologies. In the present paper we propose DIOD (self DIstillation meets Object Discovery) the first method that places the motion-guided object discovery within a framework of continuous improvement through knowledge distillation providing solutions to existing limitations (i) DIOD robustly eliminates the noise present in the exploited motion maps providing accurate motion-supervision (ii) DIOD leverages the discovered objects within an iterative pseudo-labeling framework enriching the initial motion-supervision with static objects which results in a cost-efficient increase in performance. Through experiments on synthetic and real-world datasets we demonstrate the benefits of bridging the gap between object discovery and distillation by significantly improving the state-of-the-art. This enhancement is also sustained across other demanding metrics so far reserved for supervised tasks.
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
Authors: Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, Shenlong Wang Jing Wen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce GoMAvatar a novel approach for real-time memory-efficient high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap PeopleSnapshot and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
Neural Redshift: Random Networks are not Random Functions
Authors: Armand Mihai Nicolicioiu, Valentin Hartmann, Ehsan Abbasnejad Damien Teney
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Our understanding of the generalization capabilities of neural networks NNs is still incomplete. Prevailing explanations are based on implicit biases of gradient descent GD but they cannot account for the capabilities of models from gradientfree methods nor the simplicity bias recently observed in untrained networks This paper seeks other sources of generalization in NNs. To understand the inductive biases provided by architectures independently from GD we examine untrained randomweight networks Even simple MLPs show strong inductive biases uniform sampling in weight space yields a very biased distribution of functions in terms of complexity But unlike common wisdom NNs do not have an inherent simplicity bias This property depends on components such as ReLUs residual connections and layer normalizations Alternative architectures can be built with a bias for any level of complexity. Transformers also inherit all these properties from their building blocks. We provide a fresh explanation for the success of deep learning independent from gradientbased training It points at promising avenues for controlling the solutions implemented by trained models.
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
Authors: Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin, Xihui Liu, Ziwei Liu Xian Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation sampling (SDS) which suffers from inadequate fine details or excessive training time. In this paper we propose an efficient yet effective framework HumanGaussian that generates high-quality 3D humans with fine-grained geometry and realistic appearance. Our key insight is that 3D Gaussian Splatting is an efficient renderer with periodic Gaussian shrinkage or growing where such adaptive density control can be naturally guided by intrinsic human structures. Specifically 1) we first propose a Structure-Aware SDS that simultaneously optimizes human appearance and geometry. The multi-modal score function from both RGB and depth space is leveraged to distill the Gaussian densification and pruning process. 2) Moreover we devise an Annealed Negative Prompt Guidance by decomposing SDS into a noisier generative score and a cleaner classifier score which well addresses the over-saturation issue. The floating artifacts are further eliminated based on Gaussian size in a prune-only phase to enhance generation smoothness. Extensive experiments demonstrate the superior efficiency and competitive quality of our framework rendering vivid 3D humans under diverse scenarios.
DIEM: Decomposition-Integration Enhancing Multimodal Insights
Authors: Guoming Wang, Junhao Guo, Juncheng Li, Wenqiao Zhang, Rongxing Lu, Siliang Tang Xinyi Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In image question answering due to the abundant and sometimes redundant information precisely matching and integrating the information from both text and images is a challenge. In this paper we propose the Decomposition-Integration Enhancing Multimodal Insight (DIEM) which initially decomposes the given question and image into multiple subquestions and several sub-images aiming to isolate specific elements for more focused analysis. We then integrate these sub-elements by matching each subquestion with its relevant sub-images while also retaining the original image to construct a comprehensive answer to the original question without losing sight of the overall context. This strategy mirrors the human cognitive process of simplifying complex problems into smaller components for individual analysis followed by an integration of these insights. We implement DIEM on the LLaVA-v1.5 model and evaluate its performance on ScienceQA and MM-Vet. Experimental results indicate that our method boosts accuracy in most question classes of the ScienceQA (+2.03% in average) especially in the image modality (+3.40%). On MM-Vet our method achieves an improvement in MM-Vet scores increasing from 31.1 to 32.4. These findings highlight DIEM's effectiveness in harmonizing the complexities of multimodal data demonstrating its ability to enhance accuracy and depth in image question answering through its decomposition-integration process.
CosmicMan: A Text-to-Image Foundation Model for Humans
Authors: Jianglin Fu, Kaiyuan Liu, Wentao Wang, Kwan-Yee Lin, Wayne Wu Shikai Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present CosmicMan a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the dilemma of inferior quality and text-image misalignment for humans CosmicMan enables generating photo-realistic human images with meticulous appearance reasonable structure and precise text-image alignment with detailed dense descriptions. At the heart of CosmicMan's success are the new reflections and perspectives on data and models: (1) We found that data quality and a scalable data production flow are essential for the final results from trained models. Hence we propose a new data production paradigm Annotate Anyone which serves as a perpetual data flywheel to produce high-quality data with accurate yet cost-effective annotations over time. Based on this we constructed a large-scale dataset CosmicMan-HQ 1.0 with 6 Million high-quality real-world human images in a mean resolution of 1488x1255 and attached with precise text annotations deriving from 115 Million attributes in diverse granularities. (2) We argue that a text-to-image foundation model specialized for humans must be pragmatic - easy to integrate into down-streaming tasks while effective in producing high-quality human images. Hence we propose to model the relationship between dense text descriptions and image pixels in a decomposed manner and present Decomposed-Attention-Refocusing (Daring) training framework. It seamlessly decomposes the cross-attention features in existing text-to-image diffusion model and enforces attention refocusing without adding extra modules. Through Daring we show that explicitly discretizing continuous text space into several basic groups that align with human body structure is the key to tackling the misalignment problem in a breeze. Project page: https://cosmicman-cvpr2024.github.io/.
LLMs are Good Sign Language Translators
Authors: Lin Geng Foo, Yixuan He, Hossein Rahmani, Jun Liu Jia Gong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language. Inspired by the strong translation capabilities of large language models (LLMs) that are trained on extensive multilingual text corpora we aim to harness off-the-shelf LLMs to handle SLT. In this paper we regularize the sign videos to embody linguistic characteristics of spoken language and propose a novel SignLLM framework to transform sign videos into a language-like representation for improved readability by off-the-shelf LLMs. SignLLM comprises two key modules: (1) The Vector-Quantized Visual Sign module converts sign videos into a sequence of discrete character-level sign tokens and (2) the Codebook Reconstruction and Alignment module converts these character-level tokens into word-level sign representations using an optimal transport formulation. A sign-text alignment loss further bridges the gap between sign and text tokens enhancing semantic compatibility. We achieve state-of-the-art gloss-free results on two widely-used SLT benchmarks.
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
Authors: Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Jenq-Neng Hwang, Xiaozhong Xu, Shan Liu Ziyu Shan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference which have achieved tremendous improvements due to the utilization of deep neural networks. However learning-based NR-PCQA methods suffer from the scarcity of labeled data and usually perform suboptimally in terms of generalization. To solve the problem we propose a novel contrastive pre-training framework tailored for PCQA (CoPA) which enables the pre-trained model to learn quality-aware representations from unlabeled data. To obtain anchors in the representation space we project point clouds with different distortions into images and randomly mix their local patches to form mixed images with multiple distortions. Utilizing the generated anchors we constrain the pre-training process via a quality-aware contrastive loss following the philosophy that perceptual quality is closely related to both content and distortion. Furthermore in the model fine-tuning stage we propose a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives. Extensive experiments show that our method outperforms the state-of-the-art PCQA methods on popular benchmarks. Further investigations demonstrate that CoPA can also benefit existing learning-based PCQA models.
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
Authors: Sunghoon Im, Jaedeok Kim, Kyong Hwan Jin Woo Kyoung Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a practical approach to JPEG image decoding utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compression rate inevitably resulting in quality degradation while encoding an image. We have designed a continuous cosine spectrum estimator to address the quality degradation issue that restores the distorted spectrum. By leveraging local DCT formulations our network has the privilege to exploit dequantization and upsampling simultaneously. Our proposed model enables decoding compressed images directly across different quality factors using a single pre-trained model without relying on a conventional JPEG decoder. As a result our proposed network achieves state-of-the-art performance in flexible color image JPEG artifact removal tasks. Our source code is available at https://github.com/WooKyoungHan/JDEC
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Authors: Zheqi Lv, Hao Zhou, Jia-Wei Liu, Juncheng Li, Mengze Li, Yunfei Li, Dongping Zhang, Yueting Zhuang, Siliang Tang Wenqiao Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to extend ADA from a single source domain to multiple source domains termed Multi-source Active Domain Adaptation (MADA). Not surprisingly we find that most traditional ADA methods cannot work directly in such a setting mainly due to the excessive domain gap introduced by all the source domains. Considering this we propose a Detective framework that comprehensively considers the domain shift between multi-source domains and target domains to detect the informative target samples. Specifically the Detective leverages a dynamic Domain Adaptation (DA) model that learns how to adapt the model's parameters to fit the union of multi-source domains. This enables an approximate single-source domain modeling by the dynamic model. We then comprehensively measure both domain uncertainty and predictive uncertainty in the target domain to detect informative target samples using evidential deep learning thereby mitigating uncertainty miscalibration. Experiments demonstrate that our solution outperforms existing methods by a considerable margin on three domain adaptation benchmarks.
Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification
Authors: Jiahuan Zhou, Xun Wang, Manyu Zhu, Yuxin Peng Zhenyu Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Lifelong Person Re-identification (L-ReID) aims to learn from sequentially collected data to match a person across different scenes. Once an L-ReID model is updated using new data all historical images in the gallery are required to be re-calculated to obtain new features for testing known as "re-indexing". However it is infeasible when raw images in the gallery are unavailable due to data privacy concerns resulting in incompatible retrieval between the query and the gallery features calculated by different models which causes significant performance degradation. In this paper we focus on a new task called Re-indexing Free Lifelong Person Re-identification (RFL-ReID) which requires achieving effective L-ReID without re-indexing raw images in the gallery. To this end we propose a Continual Compatible Representation (C2R) method which facilitates the query feature calculated by the continuously updated model to effectively retrieve the gallery feature calculated by the old model in a compatible manner. Specifically we design a Continual Compatible Transfer (CCT) network to continuously transfer and consolidate the old gallery feature into the new feature space. Besides a Balanced Compatible Distillation module is introduced to achieve compatibility by aligning the transferred feature space with the new feature space. Finally a Balanced Anti-forgetting Distillation module is proposed to eliminate the accumulated forgetting of old knowledge during the continual compatible transfer. Extensive experiments on several benchmark L-ReID datasets demonstrate the effectiveness of our method against state-of-the-art methods for both RFL-ReID and L-ReID tasks. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/C2R_CVPR2024.
Revisiting Spatial-Frequency Information Integration from a Hierarchical Perspective for Panchromatic and Multi-Spectral Image Fusion
Authors: Jie Huang, Naishan Zheng, Man Zhou, Keyu Yan, Danfeng Hong, Feng Zhao Jiangtong Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Pan-sharpening is a super-resolution problem that essentially relies on spectra fusion of panchromatic (PAN) images and low-resolution multi-spectral (LRMS) images. The previous methods have validated the effectiveness of information fusion in the Fourier space of the whole image. However they haven't fully explored the Fourier relationships at different hierarchies between PAN and LRMS images. To this end we propose a Hierarchical Frequency Integration Network (HFIN) to facilitate hierarchical Fourier information integration for pan-sharpening. Specifically our network consists of two designs: information stratification and information integration. For information stratification we hierarchically decompose PAN and LRMS information into spatial global Fourier and local Fourier information and fuse them independently. For information integration the above hierarchical fused information is processed to further enhance their relationships and undergo comprehensive integration. Our method extend a new space for exploring the relationships of PAN and LRMS images enhancing the integration of spatial-frequency information. Extensive experiments robustly validate the effectiveness of the proposed network showcasing its superior performance compared to other state-of-the-art methods and generalization in real-world scenes and other fusion tasks as a general image fusion framework. Code is available at https://github.com/JosephTiTan/HFIN.
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
Authors: Jiacheng Deng, Tianzhu Zhang Jiahao Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D instance segmentation (3DIS) is a crucial task but point-level annotations are tedious in fully supervised settings. Thus using bounding boxes (bboxes) as annotations has shown great potential. The current mainstream approach is a two-step process involving the generation of pseudo-labels from box annotations and the training of a 3DIS network with the pseudo-labels. However due to the presence of intersections among bboxes not every point has a determined instance label especially in overlapping areas. To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet) which devises a novel pseudo-labeler called Simulation-assisted Transformer. The labeler consists of two main components. The first is Simulation-assisted Mean Teacher which introduces Mean Teacher for the first time in this task and constructs simulated samples to assist the labeler in acquiring prior knowledge about overlapping areas. To better model local-global structure we also propose Local-Global Aware Attention as the decoder for teacher and student labelers. Extensive experiments conducted on the ScanNetV2 and S3DIS datasets verify the superiority of our designs.
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number
Authors: Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, Zheng Zhang Ke Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Object-centric learning (OCL) extracts the representation of objects with slots offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention which utilizes attention mechanisms to iteratively refine slot representations. However a major drawback of most object-centric models including slot attention is their reliance on predefining the number of slots. This not only necessitates prior knowledge of the dataset but also overlooks the inherent variability in the number of objects present in each instance. To overcome this fundamental limitation we present a novel complexity-aware object auto-encoder framework. Within this framework we introduce an adaptive slot attention (AdaSlot) mechanism that dynamically determines the optimal number of slots based on the content of the data. This is achieved by proposing a discrete slot sampling module that is responsible for selecting an appropriate number of slots from a candidate list. Furthermore we introduce a masked slot decoder that suppresses unselected slots during the decoding process. Our framework tested extensively on object discovery tasks with various datasets shows performance matching or exceeding top fixed-slot models. Moreover our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity offering the potential for further exploration in slot attention research. Project will be available at https://kfan21.github.io/AdaSlot/
CORES: Convolutional Response-based Score for Out-of-distribution Detection
Authors: Chao Hou, Weilong Peng, Runnan Chen, Peican Zhu, Wenping Wang, Zhihong Tian Keke Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover through backtracking from the most prominent predictions CORES effectively pinpoints sample-relevant kernels across different layers. These kernels which exhibit a strong correlation to input samples are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.
Task-Driven Wavelets using Constrained Empirical Risk Minimization
Authors: Ray Sheombarsing, Jan-Jakob Sonke, Jonas Teuwen Eric Marcus
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions. This flexibility however makes the strict enforcement of constraints on DNNs a difficult problem. In contexts where it is critical to limit the function space to which certain network components belong such as wavelets employed in Multi-Resolution Analysis (MRA) naive constraints via additional terms in the loss function are inadequate. To address this we introduce a Convolutional Neural Network (CNN) wherein the convolutional filters are strictly constrained to be wavelets. This allows the filters to update to task-optimized wavelets during the training procedure. Our primary contribution lies in the rigorous formulation of these filters via a constrained empirical risk minimization framework thereby providing an exact mechanism to enforce these structural constraints. While our work is grounded in theory we investigate our approach empirically through applications in medical imaging particularly in the task of contour prediction around various organs achieving superior performance compared to baseline methods.
HOI-M^3: Capture Multiple Humans and Objects Interaction within Contextual Environment
Authors: Jingyan Zhang, Zining Song, Zhanhe Shi, Chengfeng Zhao, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang Juze Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Humans naturally interact with both others and the surrounding multiple objects engaging in various social activities. However recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects due to fundamental data scarcity. In this paper we introduce HOI-M^3 a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M^3 dataset we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M^3 dataset corresponding codes and pre-trained models will be disseminated to the community for future research.
Interactive3D: Create What You Want by Interactive 3D Generation
Authors: Lihe Ding, Zhanpeng Huang, Zibin Wang, Tianfan Xue, Dan Xu Shaocong Dong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D object generation has undergone significant advancements yielding high-quality results. However fall short in achieving precise user control often yielding results that do not align with user expectations thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current generative models due to limited interaction capabilities. Existing methods mainly offer two approaches: (i) interpreting textual instructions with constrained controllability or (ii) reconstructing 3D objects from 2D images. Both of them limit customization to the confines of the 2D reference and potentially introduce undesirable artifacts during the 3D lifting process restricting the scope for direct and versatile 3D modifications. In this work we introduce Interactive3D an innovative framework for interactive 3D generation that grants users precise control over the generative process through extensive 3D interaction capabilities. Interactive3D is constructed in two cascading stages utilizing distinct 3D representations. The first stage employs Gaussian Splatting for direct user interaction allowing modifications and guidance of the generative direction at any intermediate step through (i) Adding and Removing components (ii) Deformable and Rigid Dragging (iii) Geometric Transformations and (iv) Semantic Editing. Subsequently the Gaussian splats are transformed into InstantNGP. We introduce a novel (v) Interactive Hash Refinement module to further add details and extract the geometry in the second stage. Our experiments demonstrate that proposed Interactive3D markedly improves the controllability and quality of 3D generation. Our project webpage is available at https://interactive-3d.github.io/.
DeiT-LT: Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
Authors: Pradipto Mondal, Mayank Mishra, Ashish Ramayee Asokan, R. Venkatesh Babu Harsh Rangwani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT we divide the input image into patch tokens and process them through a stack of self-attention blocks. However unlike Convolutional Neural Network (CNN) ViT's simple architecture has no informative inductive bias (e.g. locality etc.). Due to this ViT requires a large amount of data for pre-training. Various data-efficient approaches (DeiT) have been proposed to train ViT on balanced datasets effectively. However limited literature discusses the use of ViT for datasets with long-tailed imbalances. In this work we introduce DeiT-LT to tackle the problem of training ViTs from scratch on long-tailed datasets. In DeiT-LT we introduce an efficient and effective way of distillation from CNN via distillation \texttt DIST  token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes. This leads to the learning of local CNN-like features in early ViT blocks improving generalization for tail classes. Further to mitigate overfitting we propose distilling from a flat CNN teacher which leads to learning low-rank generalizable features for DIST tokens across all ViT blocks. With the proposed DeiT-LT scheme the distillation DIST token becomes an expert on the tail classes and the classifier CLS token becomes an expert on the head classes. The experts help to effectively learn features corresponding to both the majority and minority classes using a distinct set of tokens within the same ViT architecture. We show the effectiveness of DeiT-LT for training ViT from scratch on datasets ranging from small-scale CIFAR-10 LT to large-scale iNaturalist-2018. Project Page: https://rangwani-harsh.github.io/DeiT-LT.
Accurate Spatial Gene Expression Prediction by Integrating Multi-Resolution Features
Authors: Ji Hun Ha, Kyeong Chan Im, Joo Sang Lee Youngmin Chung
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However the high costs and methodological limitations of ST necessitate a more robust predictive model. In response this paper introduces TRIPLEX a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features capturing cellular morphology at individual spots the local context around these spots and the global tissue organization. By integrating these features through an effective fusion strategy TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study conducted on three public ST datasets and supplemented with Visium data from 10X Genomics demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE) Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.