pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Support Information Mining for Category-Agnostic Pose Estimation
Authors: Pengfei Ren, Yuanyuan Gao, Haifeng Sun, Qi Qi, Jingyu Wang, Jianxin Liao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Category-Agnostic Pose Estimation, Pose Estimation

    Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations. Existing methods achieve the localization of arbitrary keypoints through similarity matching between support keypoint features and query image features. However these methods primarily focus on mining information from the query images neglecting the fact that support samples with keypoint annotations contain rich category-specific fine-grained semantic information and prior structural information. In this paper we propose a Support-based Dynamic Perception Network (SDPNet) for the robust and accurate CAPE. On the one hand SDPNet models complex dependencies between support keypoints constructing category-specific prior structure to guide the interaction of query keypoints. On the other hand SDPNet extracts fine-grained semantic information from support samples dynamically modulating the refinement process of query. Our method outperforms existing methods on MP-100 dataset by a large margin.
DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency
Authors: Heng Guo, Jieji Ren, Feishi Wang, Boxin Shi, Mingjun Ren, Yasuyuki Matsushita
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Translucency. Unlike qualitative and semantic labels such as diffuse and specular that have been used in previous datasets our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://photometricstereo.github.io/diligentrt.html.
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy
Authors: DaeJun Kang, Dongsuk Kum, Sanmin Kim
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Driving, Autonomous Vehicles, Continual Learning, motion prediction, Representation Learning

    Embodied AI such as autonomous vehicles suffers from insufficient long-tailed data because it must be obtained from the physical world. In fact data must be continuously obtained in a series of small batches and the model must also be continuously trained to achieve generalizability and scalability by improving the biased data distribution. This paper addresses the training cost and catastrophic forgetting problems when continuously updating models to adapt to incoming small batches from various environments for real-world motion prediction in autonomous driving. To this end we propose a novel continual motion prediction (CMP) learning framework based on sparse meta-representation learning and an optimal memory buffer retention strategy. In meta-representation learning a model explicitly learns a sparse representation of each driving environment from road geometry to vehicle states by training to reduce catastrophic forgetting based on an augmented modulation network with sparsity regularization. Also in the adaptation phase We develop an Optimal Memory Buffer Retention strategy that smartly preserves diverse samples by focusing on representation similarity. This approach handles the nuanced task distribution shifts characteristic of motion prediction datasets ensuring our model stays responsive to evolving input variations without requiring extensive resources. The experiment results demonstrate that the proposed method shows superior adaptation performance to the conventional continual learning approach which is developed using a synthetic dataset for the continual learning problem.
FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking
Authors: Seokju Cho, Jiahui Huang, Seungryong Kim, Joon-Young Lee
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Optical Flow Estimation

    In the domain of video tracking existing methods often grapple with a trade-off between spatial density and temporal range. Current approaches in dense optical flow estimators excel in providing spatially dense tracking but are limited to short temporal spans. Conversely recent advancements in long-range trackers offer extended temporal coverage but at the cost of spatial sparsity. This paper introduces FlowTrack a novel framework designed to bridge this gap. FlowTrack combines the strengths of both paradigms by 1) chaining confident flow predictions to maximize efficiency and 2) automatically switching to an error compensation module in instances of flow prediction inaccuracies. This dual strategy not only offers efficient dense tracking over extended temporal spans but also ensures robustness against error accumulations and occlusions common pitfalls of naive flow chaining. Furthermore we demonstrate that chained flow itself can serve as an effective guide for an error compensation module even for occluded points. Our framework achieves state-of-the-art accuracy for long-range tracking on the DAVIS dataset and renders 50% speed-up when performing dense tracking.
JointSQ: Joint Sparsification-Quantization for Distributed Learning
Authors: Weiying Xie, Haowei Li, Jitao Ma, Yunsong Li, Jie Lei, Donglai Liu, Leyuan Fang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Quantization

    Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.
Class Tokens Infusion for Weakly Supervised Semantic Segmentation
Authors: Sung-Hoon Yoon, Hoyong Kwon, Hyeonseong Kim, Kuk-Jin Yoon
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

    Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT) the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token Infusion (CTI). By infusing the class tokens from images we guide class tokens to possess class-specific distinct characteristics and global-local consistency. For this we devise two kinds of token infusion: 1) Intra-image Class Token Infusion (I-CTI) and 2) Cross-Image Class Token Infusion (C-CTI). In I-CTI we infuse the class tokens from the same but differently augmented images and thus make CAMs consistent among various deformations (view color). In C-CTI by infusing the class tokens from the other images and imposing the resulting CAMs to be similar it learns class-specific distinct characteristics. Besides the CTI we bring the background (BG) concept into ViT with the BG token to reduce the false positive activation of CAMs. We demonstrate the effectiveness of our method on PASCAL VOC 2012 and MS COCO 2014 datasets achieving state-of-the-art results in weakly supervised semantic segmentation. The code is available at https://github.com/yoon307/CTI
Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors
Authors: Ziqin Zhou, Hai-Ming Xu, Yangyang Shu, Lingqiao Liu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Few-Shot Semantic Segmentation, Segmentation, Semantic Segmentation, Zero-Shot Semantic Segmentation

    The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper we explore to harnesses this emergent feature to tackle few-shot semantic segmentation a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery we find that the concept of "relationship descriptors" initially conceived for enhancing the CLIP model for zero-shot semantic segmentation offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation extending its application across multiple layers to enhance performance. Building upon this adaptation we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets including PASCAL-5^ i  and COCO-20^ i  we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://github.com/ZiqinZhou66/FewSegwithRD.git.
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification
Authors: Yuanmin Huang, Mi Zhang, Daizong Ding, Erling Jiang, Zhaoxiang Wang, Min Yang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Adversarial Robustness, Classification, Point Cloud Classification, Robust classification

    Deep neural networks have demonstrated remarkable performance in point cloud classification. However previous works show they are vulnerable to adversarial perturbations that can manipulate their predictions. Given the distinctive modality of point clouds various attack strategies have emerged posing challenges for existing defenses to achieve effective generalization. In this study we for the first time introduce causal modeling to enhance the robustness of point cloud classification models. Our insight is from the observation that adversarial examples closely resemble benign point clouds from the human perspective. In our causal modeling we incorporate two critical variables the structural information (standing for the key feature leading to the classification) and the hidden confounders (standing for the noise interfering with the classification). The resulting overall framework CausalPC consists of three sub-modules to identify the causal effect for robust classification. The framework is model-agnostic and adaptable for integration with various point cloud classifiers. Our approach significantly improves the adversarial robustness of three mainstream point cloud classification models on two benchmark datasets. For instance the classification accuracy for DGCNN on ModelNet40 increases from 29.2% to 72.0% with CausalPC whereas the best-performing baseline achieves only 42.4%.
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition
Authors: Jianqiang Wan, Sibo Song, Wenwen Yu, Yuliang Liu, Wenqing Cheng, Fei Huang, Xiang Bai, Cong Yao, Zhibo Yang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decoder, document understanding, Key Information Extraction, Table Recognition, Text Generation, Text Spotting

    Recently visually-situated text parsing (VsTP) has experienced notable advancements driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However due to the diversified targets and heterogeneous schemas previous works usually design task-specific architectures and objectives for individual tasks which inadvertently leads to modal isolation and complex workflow. In this paper we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically we devise a universal model called OmniParser which can simultaneously handle three typical visually-situated text parsing tasks: text spotting key information extraction and table recognition. In OmniParser all tasks share the unified encoder-decoder architecture the unified objective: point-conditioned text generation and the unified input & output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks despite its unified concise design. The code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery.
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning
Authors: Yutian Luo, Shiqi Zhao, Haoran Wu, Zhiwu Lu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

    Traditional online class incremental learning assumes class sets in different tasks are disjoint. However recent works have shifted towards a more realistic scenario where tasks have shared classes creating blurred task boundaries. Under this setting although existing approaches could be directly applied challenges like data imbalance and varying class-wise data volumes complicate the critical coreset selection used for replay. To tackle these challenges we introduce DECO (Dual-Enhanced Coreset Selection with Class-wise Collaboration) an approach that starts by establishing a class-wise balanced memory to address data imbalances followed by a tailored class-wise gradient-based similarity scoring system for refined coreset selection strategies with reasonable score guidance to all classes. DECO is distinguished by two main strategies: (1) Collaborative Diverse Score Guidance that mitigates biased knowledge in less-exposed classes through guidance from well-established classes simultaneously consolidating the knowledge in the established classes to enhance overall stability. (2) Adaptive Similarity Score Constraint that relaxes constraints between class types boosting learning plasticity for less-exposed classes and assisting well-established classes in defining clearer boundaries thereby improving overall plasticity. Overall DECO helps effectively identify critical coreset samples improving learning stability and plasticity across all classes. Extensive experiments are conducted on four benchmark datasets to demonstrate the effectiveness and superiority of DECO over other competitors under this online blurry class incremental learning setting.
Video Frame Interpolation via Direct Synthesis with the Event-based Reference
Authors: YuHan Liu, Yongjian Deng, Hao Chen, Zhen Yang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Motion Estimation, Video Frame Interpolation

    Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However this reliance engenders a heavy dependency on the quality and consistency of keyframes rendering these methods susceptible to challenges in extreme real-world scenarios such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data we prioritize the reliability of the event-based reference leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides we implement a bi-directional event-guided alignment from keyframes to the reference using the introduced E-PCD module. Finally a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real-world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.
OpenEQA: Embodied Question Answering in the Era of Foundation Models
Authors: Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul McVay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Alexander Sax, Aravind Rajeswaran
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Embodied Question Answering, Question Answering

    We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory exemplified by agents on smart glasses or by actively exploring the environment as in the case of mobile robots. We accompany our formulation with OpenEQA -- the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol we evaluate several state-of-the-art foundation models including GPT-4V and find that they significantly lag behind human-level performance. Consequently OpenEQA stands out as a straightforward measurable and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI conversational agents and world models.
Capturing Closely Interacted Two-Person Motions with Reaction Priors
Authors: Qi Fang, Yinghui Fan, Yanjun Li, Junting Dong, Dingwei Wu, Weidong Zhang, Kang Chen
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 2k, Decoder

    In this paper we focus on capturing closely interacted two-person motions from monocular videos an important yet understudied topic. Unlike less-interacted motions closely interacted motions contain frequently occurring inter-human occlusions which pose significant challenges to existing capturing algorithms. To address this problem our key observation is that close physical interactions between two subjects typically happen under very specific situations (e.g. handshake hug etc.) and such situational contexts contain strong prior semantics to help infer the poses of occluded joints. In this spirit we introduce reaction priors which are invertible neural networks that bi-directionally model the pose probability distributions of one person given the pose of the other. The learned reaction priors are then incorporated into a query-based pose estimator which is a decoder-only Transformer with self-attentions on both intra-joint and inter-joint relationships. We demonstrate that our design achieves considerably higher performance than previous methods on multiple benchmarks. What's more as existing datasets lack sufficient cases of close human-human interactions we also build a new dataset called Dual-Human to better evaluate different methods. Dual-Human contains around 2k sequences of closely interacted two-person motions each with synthetic multi-view renderings contact annotations and text descriptions. We believe that this new public dataset can significantly promote further research in this area.
Mind Artist: Creating Artistic Snapshots with Human Thought
Authors: Jiaxuan Chen, Yu Qi, Yueming Wang, Gang Pan
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Graph Matching, Image Reconstruction, Representation Learning

    We introduce Mind Artist (MindArt) a novel and efficient neural decoding architecture to snap artistic photographs from our mind in a controllable manner. Recently progress has been made in image reconstruction with non-invasive brain recordings but it's still difficult to generate realistic images with high semantic fidelity due to the scarcity of data annotations. Unlike previous methods this work casts the neural decoding into optimal transport (OT) and representation decoupling problems. Specifically under discrete OT theory we design a graph matching-guided neural representation learning framework to seek the underlying correspondences between conceptual semantics and neural signals which yields a natural and meaningful self-supervisory task. Moreover the proposed MindArt structured with multiple stand-alone modal branches enables the seamless incorporation of semantic representation into any visual style information thus leaving it to have multi-modal reconstruction and training-free semantic editing capabilities. By doing so the reconstructed images of MindArt have phenomenal realism both in terms of semantics and appearance. We compare our MindArt with leading alternatives and achieve SOTA performance in different decoding tasks. Importantly our approach can directly generate a series of stylized "mind snapshots" w/o extra optimizations which may open up more potential applications. Code is available at https://github.com/JxuanC/MindArt.