pwc-archive/papers-with-abstracts huggingface dataset:
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Authors: Yasiru Ranasinghe, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Crowd Counting, Density Estimation

    Crowd counting is a fundamental problem in crowd analysis which is typically accomplished by estimating a crowd density map and summing over the density values. However this approach suffers from background noise accumulation and loss of density due to the use of broad Gaussian kernels to create the ground truth density maps. This issue can be overcome by narrowing the Gaussian kernel. However existing approaches perform poorly when trained with ground truth density maps with broad kernels. To deal with this limitation we propose using conditional diffusion models to predict density maps as diffusion models show high fidelity to training data during generation. With that we present CrowdDiff that generates the crowd density map as a reverse diffusion process. Furthermore as the intermediate time steps of the diffusion process are noisy we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning. In addition owing to the stochastic nature of the diffusion model we introduce producing multiple density maps to improve the counting performance contrary to the existing crowd counting pipelines. We conduct extensive experiments on publicly available datasets to validate the effectiveness of our method. CrowdDiff outperforms existing \sota crowd counting methods on several public crowd analysis benchmarks with significant improvements. CrowdDiff project is available at: https://dylran.github.io/crowddiff.github.io.
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation
Authors: Zineng Tang, ZiYi Yang, Mahmoud Khademi, Yang Liu, Chenguang Zhu, Mohit Bansal
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Generation, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

    We present CoDi-2 a Multimodal Large Language Model (MLLM) for learning in-context interleaved multimodal representations. By aligning modalities with language for both encoding and generation CoDi-2 empowers Large Language Models (LLMs) to understand modality-interleaved instructions and in-context examples and autoregressively generate grounded and coherent multimodal outputs in an any-to-any input-output modality paradigm. To train CoDi-2 we build a large-scale generation dataset encompassing in-context multimodal instructions across text vision and audio. CoDi-2 demonstrates a wide range of zero-shot and few-shot capabilities for tasks like editing exemplar learning composition reasoning etc. CoDi-2 surpasses previous domain-specific models on tasks such as subject-driven image generation vision transformation and audio editing and showcases a significant advancement for integrating diverse multimodal tasks with sequential generation.
DiffLoc: Diffusion Model for Outdoor LiDAR Localization
Authors: Wen Li, Yuyang Yang, Shangshu Yu, Guosheng Hu, Chenglu Wen, Ming Cheng, Cheng Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, model, Visual Localization

    Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving impressive results in learn-based LiDAR localization. However compared to the top-performing methods reliant on 3D-3D correspondence matching APR's accuracy still has room for improvement. We recognize APR's lack of robust features learning and iterative denoising process leads to suboptimal results. In this paper we propose DiffLoc a novel framework that formulates LiDAR localization as a conditional generation of poses. First we propose to utilize the foundation model and static-object-aware pool to learn robust features. Second we incorporate the iterative denoising process into APR via a diffusion model conditioned on the learned geometrically robust features. In addition due to the unique nature of diffusion models we propose to adapt our models to two additional applications: (1) using multiple inferences to evaluate pose uncertainty and (2) seamlessly introducing geometric constraints on denoising steps to improve prediction accuracy. Extensive experiments conducted on the Oxford Radar RobotCar and NCLT datasets demonstrate that DiffLoc outperforms better than the stateof-the-art methods. Especially on the NCLT dataset we achieve 35% and 34.7% improvement on position and orientation accuracy respectively. Our code is released at https://github.com/liw95/DiffLoc.
Robust Noisy Correspondence Learning with Equivariant Similarity Consistency
Authors: Yuchen Yang, Likai Wang, Erkun Yang, Cheng Deng
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Triplet

    The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from a significant presence of mismatched data impairing the performance of matching models. To address this problem we introduce a novel regularization approach named Equivariant Similarity Consistency (ESC) which can facilitate robust clean and noisy data separation and improve the training for cross-modal matching. Intuitively our method posits that the semantic variations caused by image changes should be proportional to those caused by text changes for any two matched samples. Accordingly we first calculate the ESC by comparing image and text semantic variations between a set of elaborated anchor points and other undivided training data. Then pairs with high ESC are filtered out as noisy correspondence pairs. We implement our method by combining the ESC with a traditional hinge-based triplet loss. Extensive experiments on three widely used datasets including Flickr30K MS-COCO and Conceptual Captions verify the effectiveness of our method.
Image Processing GNN: Breaking Rigidity in Super-Resolution
Authors: Yuchuan Tian, Hanting Chen, Chao Xu, Yunhe Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Super-Resolution, Super-Resolution

    Super-Resolution (SR) reconstructs high-resolution images from low-resolution ones. CNNs and window-attention methods are two major categories of canonical SR models. However these measures are rigid: in both operations each pixel gathers the same number of neighboring pixels hindering their effectiveness in SR tasks. Alternatively we leverage the flexibility of graphs and propose the Image Processing GNN (IPG) model to break the rigidity that dominates previous SR methods. Firstly SR is unbalanced in that most reconstruction efforts are concentrated to a small proportion of detail-rich image parts. Hence we leverage degree flexibility by assigning higher node degrees to detail-rich image nodes. Then in order to construct graphs for SR-effective aggregation we treat images as pixel node sets rather than patch nodes. Lastly we hold that both local and global information are crucial for SR performance. In the hope of gathering pixel information from both local and global scales efficiently via flexible graphs we search node connections within nearby regions to construct local graphs; and find connections within a strided sampling space of the whole image for global graphs. The flexibility of graphs boosts the SR performance of the IPG model. Experiment results on various datasets demonstrates that the proposed IPG outperforms State-of-the-Art baselines. Codes are available at https://github.com/huawei-noah/Efficient-Computing/tree/master/LowLevel/IPG.
Discriminability-Driven Channel Selection for Out-of-Distribution Detection
Authors: Yue Yuan, Rundong He, Yicong Dong, Zhongyi Han, Yilong Yin
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: channel selection, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

    Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations enhancing the distinguishability between in-distribution (ID) data and OOD data. However they assume by default that every channel is necessary for OOD detection and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight we propose \underline D iscriminability-\underline D riven \underline C hannel \underline S election (DDCS) which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover DDCS can generalize to different backbones and OOD scores.
Improving Graph Contrastive Learning via Adaptive Positive Sampling
Authors: Jiaming Zhuo, Feiyang Qin, Can Cui, Kun fu, bingxin niu, Mengzhu Wang, Yuanfang Guo, Chuan Wang, Zhen Wang, Xiaochun Cao, Liang Yang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Self-Supervised Learning

    Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to amplify feature similarities between the positive sample pairs and reduce them between the negative sample pairs. Unfortunately most existing GCLs consistently present suboptimal performances on both homophilic and heterophilic graphs. This is primarily attributed to two limitations of positive sampling that is incomplete local sampling and blind sampling. To address these limitations this paper introduces a novel GCL framework with an adaptive positive sampling module named grapH contrastivE Adaptive posiTive Samples (HEATS). Motivated by the observation that the affinity matrix corresponding to optimal positive sample sets has a block-diagonal structure with equal weights within each block a self-expressive learning objective incorporating the block and idempotent constraint is presented. This learning objective and the contrastive learning objective are iteratively optimized to improve the adaptability and robustness of HEATS. Extensive experiments on graphs and images validate the effectiveness and generality of HEATS.
CORES: Convolutional Response-based Score for Out-of-distribution Detection
Authors: Keke Tang, Chao Hou, Weilong Peng, Runnan Chen, Peican Zhu, Wenping Wang, Zhihong Tian
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Out-of-Distribution Detection

    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.
Open-Vocabulary 3D Semantic Segmentation with Foundation Models
Authors: Li Jiang, Shaoshuai Shi, Bernt Schiele
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Semantic Segmentation, Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Segmentation, Semantic Segmentation, World Knowledge

    In dynamic 3D environments the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need we introduce OV3D an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features OV3D empowers open-vocabulary recognition in the 3D domain achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets including ScanNet Matterport3D and nuScenes.
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference
Authors: Xiao Tang, Min Yang, Penghui Sun, Hui Li, Yuchao Dai, Feng Zhu, Hojae Lee
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Driving, Decoder, Image Generation, Image Reconstruction, NeRF, Optical Flow Estimation

    With photo-realistic image generation Neural Radiance Field (NeRF) is widely used for large-scale dynamic scene reconstruction as autonomous driving simulator. However large-scale scene reconstruction still suffers from extremely long training time and rendering time. Low-resolution (LR) rendering combined with upsampling can alleviate this problem but it degrades image quality. In this paper we design a lightweight reference decoder which exploits prior information from known views to improve image reconstruction quality of new views. In addition to speed up prior information search we propose an optical flow and structural similarity based prior information search method. Results on KITTI and VKITTI2 datasets show that our method significantly outperforms the baseline method in terms of training speed rendering speed and rendering quality.
CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving
Authors: Mozhgan PourKeshavarz, JunRui Zhang, Amir Rasouli
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Driving, Autonomous Vehicles, Disentanglement, Motion Planning, Prediction, Representation Learning, Trajectory Prediction

    For safe motion planning in real-world autonomous vehicles require behavior prediction models that are reliable and robust to distribution shifts. The recent studies suggest that the existing learning-based trajectory prediction models do not posses such characteristics and are susceptible to small perturbations that are not present in the training data largely due to overfitting to spurious correlations while learning. In this paper we propose a causal disentanglement representation learning approach aiming to separate invariant (causal) and variant (spurious) features for more robust learning. Our method benefits from a novel intervention mechanism in the latent space that estimates potential distribution shifts resulted from spurious correlations using uncertain feature statistics hence maintaining the realism of interventions. To facilitate learning we propose a novel invariance objective based on the variances of the distributions over uncertain statistics to induce the model to focus on invariant representations during training. We conduct extensive experiments on two large-scale autonomous driving datasets and show that besides achieving state-of-the-art performance our method can significantly improve prediction robustness to various distribution shifts in driving scenes. We further conduct ablative studies to evaluate the design choices in our proposed framework.
MaskPLAN: Masked Generative Layout Planning from Partial Input
Authors: Hang Zhang, Anton Savov, Benjamin Dillenburger
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Attribute, Design Synthesis, Layout Generation

    Layout planning spanning from architecture to interior design is a slow iterative exploration of ill-defined problems adopting a "I'll know it when I see it" approach to potential solutions. Recent advances in generative models promise automating layout generation yet often overlook the crucial role of user-guided iteration cannot generate full solutions from incomplete design ideas and do not learn for the inter-dependency of layout attributes. To address these limitations we propose MaskPLAN a novel generative model based on Graph-structured Dynamic Masked Autoencoders (GDMAE) featuring five transformers generating a blend of graph-based and image-based layout attributes. MaskPLAN lets users generate and adjust layouts with partial attribute definitions create alternatives for preferences and practice new composition-driven or functionality-driven workflows. Through cross-attribute learning and the user input as a global conditional prior we ensure that design synthesis is calibrated at every intermediate stage maintaining its feasibility and practicality. Extensive evaluations show MaskPLAN's superior performance over existing methods across multiple metrics.
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
Authors: Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu, Yanzhi Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Video Generation

    While AI-generated content has garnered significant attention achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality the complex model architecture and substantial computational demands for both training and inference create a significant gap between these models and real-world applications. This paper presents SNED a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method. Moreover we propose the supernet training sampling warm-up for fast training optimization. To showcase the flexibility of our method we conduct experiments involving both pixel-space and latent-space video diffusion models. The results demonstrate that our framework consistently produces comparable results across different model options with high efficiency. According to the experiment for the pixel-space video diffusion model we can achieve consistent video generation results simultaneously across 64 x 64 to 256 x 256 resolutions with a large range of model sizes from 640M to 1.6B number of parameters for pixel-space video diffusion models.
Point Transformer V3: Simpler Faster Stronger
Authors: Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Representation Learning

    This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning we recognize that model performance is more influenced by scale than by intricate design. Therefore we present Point Transformer V3 (PTv3) which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training PTv3 pushes these results to a higher level.
Fast Adaptation for Human Pose Estimation via Meta-Optimization
Authors: Shengxiang Hu, Huaijiang Sun, Bin Li, Dong Wei, Weiqing Li, Jianfeng Lu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Auxiliary Learning, Image Inpainting, Meta-Learning, Pose Estimation, Self-Supervised Learning, Test-time Adaptation

    Domain shift is a challenge for supervised human pose estimation where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set than on the training set. Recently test-time adaptation has proven to be an effective way to deal with domain shift in human pose estimation. Although the performance on the target domain has been improved existing methods require a large number of weight updates for convergence which is time-consuming and brings catastrophic forgetting. To solve these issues we propose a meta-auxiliary learning method to achieve fast adaptation for domain shift during inference. Specifically we take human pose estimation as the supervised primary task and propose body-specific image inpainting as a self-supervised auxiliary task. First we jointly train the primary and auxiliary tasks to get a pre-trained model on the source domain. Then meta-training correlates the performance of the two tasks to learn a good weight initialization. Finally meta-testing adapts the meta-learned model to the target data through self-supervised learning. Benefiting from the meta-learning paradigm the proposed method enables fast adaptation to the target domain while preserving the source domain knowledge. The carefully designed auxiliary task better pays attention to human-related semantics in a single image. Extensive experiments demonstrate the effectiveness of our test-time fast adaptation.