pwc-archive/papers-with-abstracts huggingface dataset:
AutoAD III: The Prequel - Back to the Pixels
Authors: Tengda Han, Max Bain, Arsha Nagrani, Gül Varol, Weidi Xie, Andrew Zisserman
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently visual language models for AD generation are limited by a lack of suitable training data and also their evaluation is hampered by using performance measures not specialized to the AD domain. In this paper we make three contributions: (i) We propose two approaches for constructing AD datasets with aligned video data and build training and evaluation datasets using these. These datasets will be publicly released; (ii) We develop a Q-former-based architecture which ingests raw video and generates AD using frozen pre-trained visual encoders and large language models; and (iii) We provide new evaluation metrics to benchmark AD quality that are well matched to human performance. Taken together we improve the state of the art on AD generation.
NC-TTT: A Noise Constrastive Approach for Test-Time Training
Authors: David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah, Ismail Ben Ayed, Christian Desrosiers
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Test-time Adaptation

    Despite their exceptional performance in vision tasks deep learning models often struggle when faced with domain shifts during testing. Test-Time Training (TTT) methods have recently gained popularity by their ability to enhance the robustness of models through the addition of an auxiliary objective that is jointly optimized with the main task. Being strictly unsupervised this auxiliary objective is used at test time to adapt the model without any access to labels. In this work we propose Noise-Contrastive Test-Time Training (NC-TTT) a novel unsupervised TTT technique based on the discrimination of noisy feature maps. By learning to classify noisy views of projected feature maps and then adapting the model accordingly on new domains classification performance can be recovered by an important margin. Experiments on several popular test-time adaptation baselines demonstrate the advantages of our method compared to recent approaches for this task. The code can be found at: https://github.com/GustavoVargasHakim/NCTTT.git
Scaled Decoupled Distillation
Authors: Shicai Wei, Chunbo Luo, Yang Luo
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Knowledge Distillation

    Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However it often suffers inferior performance compared to the feature knowledge distillation. In this paper we argue that existing logit-based methods may be sub-optimal since they only leverage the global logit output that couples multiple semantic knowledge. This may transfer ambiguous knowledge to the student and mislead its learning. To this end we propose a simple but effective method i.e. Scale Decoupled Distillation (SDD) for logit knowledge distillation. SDD decouples the global logit output into multiple local logit outputs and establishes distillation pipelines for them. This helps the student to mine and inherit fine-grained and unambiguous logit knowledge. Moreover the decoupled knowledge can be further divided into consistent and complementary logit knowledge that transfers the semantic information and sample ambiguity respectively. By increasing the weight of complementary parts SDD can guide the student to focus more on ambiguous samples improving its discrimination ability. Extensive experiments on several benchmark datasets demonstrate the effectiveness of SDD for wide teacher-student pairs especially in the fine-grained classification task. Code is available at: \href https://github.com/shicaiwei123/SDD-CVPR2024  https://github.com/shicaiwei123/SDD-CVPR2024
Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification
Authors: Bin Yang, Jun Chen, Mang Ye
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Collaborative Ranking, Contrastive Learning, Person Re-Identification

    Unsupervised visible-infrared person re-identification (US-VI-ReID) centers on learning a cross-modality retrieval model without labels reducing the reliance on expensive cross-modality manual annotation. Previous US-VI-ReID works gravitate toward learning cross-modality information with the deep features extracted from the ultimate layer. Nevertheless interfered by the multiple discrepancies solely relying on deep features is insufficient for accurately learning modality-invariant features resulting in negative optimization. The shallow feature from the shallow layers contains nuanced detail information which is critical for effective cross-modality learning but is disregarded regrettably by the existing methods. To address the above issues we design a Shallow-Deep Collaborative Learning (SDCL) framework based on the transformer with shallow-deep contrastive learning incorporating Collaborative Neighbor Learning (CNL) and Collaborative Ranking Association (CRA) module. Specifically CNL unveils the intrinsic homogeneous and heterogeneous collaboration which are harnessed for neighbor alignment enhancing the robustness in a dynamic manner. Furthermore CRA associates the cross-modality labels with the ranking association between shallow and deep features furnishing valuable supervision for cross-modality learning. Extensive experiments validate the superiority of our method even outperforming certain supervised counterparts.
Weakly-Supervised Audio-Visual Video Parsing with Prototype-based Pseudo-Labeling
Authors: Kranthi Kumar Rachavarapu, Kalyan Ramakrishnan, Rajagopalan A. N.
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Multiple Instance Learning

    In this paper we address the weakly-supervised Audio-Visual Video Parsing (AVVP) problem which aims at labeling events in a video as audible visible or both and temporally localizing and classifying them into known categories. This is challenging since we only have access to video-level (weak) event labels when training but need to predict event labels at the segment (frame) level at test time. Recent methods employ multiple-instance learning (MIL) techniques that tend to focus solely on the most discriminative segments resulting in frequent misclassifications. Our idea is to first construct several prototype features for each event class by clustering key segments identified for the event in the training data. We then assign pseudo labels to all training segments based on their feature similarities with these prototypes and re-train the model under weak and strong supervision. We facilitate this by structuring the feature space with contrastive learning using pseudo labels. Experiments show that we outperform existing methods for weakly-supervised AVVP. We also show that learning with weak and iteratively re-estimated pseudo labels can be interpreted as an expectation-maximization (EM) algorithm providing further insight for our training procedure.
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and Planning
Authors: Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, Tao Chen
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D dense captioning, Dense Captioning, Question Answering

    Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions. However developing LMMs that can comprehend reason and plan in complex and diverse 3D environments remains a challenging topic especially considering the demand for understanding permutation-invariant point cloud representations of the 3D scene. Existing works seek help from multi-view images by projecting 2D features to 3D space which inevitably leads to huge computational overhead and performance degradation. In this paper we present LL3DA a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions. The additional visual interaction enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts. Experiments show that LL3DA achieves remarkable results and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering.
ColorPCR: Color Point Cloud Registration with Multi-Stage Geometric-Color Fusion
Authors: Juncheng Mu, Lin Bie, Shaoyi Du, Yue Gao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Point Cloud Registration

    Point cloud registration is still a challenging and open problem. For example when the overlap between two point clouds is extremely low geo-only features may be not sufficient. Therefore it is important to further explore how to utilize color data in this task. Under such circumstances we propose ColorPCR for color point cloud registration with multi-stage geometric-color fusion. We design a Hierarchical Color Enhanced Feature Extraction module to extract multi-level geometric-color features and a GeoColor Superpoint Matching Module to encode transformation-invariant geo-color global context for robust patch correspondences. In this way both geometric and color data can be used thus lead to robust performance even under extremely challenging scenarios such as low overlap between two point clouds. To evaluate the performance of our method we colorize 3DMatch/3DLoMatch datasets as Color3DMatch/Color3DLoMatch and evaluations on these datasets demonstrate the effectiveness of our proposed method. Our method achieves state-of-the-art registration recall of 97.5%/88.9% on them.
Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models
Authors: Samar Fares, Karthik Nandakumar
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Sensitivity

    Poisoning (trojan/backdoor) attacks enable an adversary to train and deploy a corrupted machine learning (ML) model which typically works well and achieves good accuracy on clean input samples but behaves maliciously on poisoned samples containing specific trigger patterns. Using such poisoned ML models as the foundation to build real-world systems can compromise application safety. Hence there is a critical need for algorithms that detect whether a given target model has been poisoned. This work proposes a novel approach for detecting poisoned models called Attack To Defend (A2D) which is based on the observation that poisoned models are more sensitive to adversarial perturbations compared to benign models. We propose a metric called sensitivity to adversarial perturbations (SAP) to measure the sensitivity of a ML model to adversarial attacks at a specific perturbation bound. We then generate strong adversarial attacks against an unrelated reference model and estimate the SAP value of the target model by transferring the generated attacks. The target model is deemed to be a trojan if its SAP value exceeds a decision threshold. The A2D framework requires only black-box access to the target model and a small clean set while being computationally efficient. The A2D approach has been evaluated on four standard image datasets and its effectiveness under various types of poisoning attacks has been demonstrated
LTA-PCS: Learnable Task-Agnostic Point Cloud Sampling
Authors: Jiaheng Liu, Jianhao Li, Kaisiyuan Wang, Hongcheng Guo, Jian Yang, Junran Peng, Ke Xu, Xianglong Liu, Jinyang Guo
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Recently many approaches directly operate on point clouds for different tasks. These approaches become more computation and storage demanding when point cloud size is large. To reduce the required computation and storage one possible solution is to sample the point cloud. In this paper we propose the first Learnable Task-Agnostic Point Cloud Sampling (LTA-PCS) framework. Existing task-agnostic point cloud sampling strategy (e.g. FPS) does not consider semantic information of point clouds causing degraded performance on downstream tasks. While learning-based point cloud sampling methods consider semantic information they are task-specific and require task-oriented ground-truth annotations. So they cannot generalize well on different downstream tasks. Our LTA-PCS achieves task-agnostic point cloud sampling without requiring task-oriented labels in which both the geometric and semantic information of points is considered in sampling. Extensive experiments on multiple downstream tasks demonstrate the effectiveness of our LTA-PCS.
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular Video
Authors: Dong Wu, Zike Yan, Hongbin Zha
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Panoptic Segmentation, 3D Reconstruction, Panoptic Segmentation, Scene Understanding

    We introduce the Panoptic 3D Reconstruction task a unified and holistic scene understanding task for a monocular video. And we present PanoRecon - a novel framework to address this new task which realizes an online geometry reconstruction alone with dense semantic and instance labeling. Specifically PanoRecon incrementally performs panoptic 3D reconstruction for each video fragment consisting of multiple consecutive key frames from a volumetric feature representation using feed-forward neural networks. We adopt a depth-guided back-projection strategy to sparse and purify the volumetric feature representation. We further introduce a voxel clustering module to get object instances in each local fragment and then design a tracking and fusion algorithm for the integration of instances from different fragments to ensure temporal coherence. Such design enables our PanoRecon to yield a coherent and accurate panoptic 3D reconstruction. Experiments on ScanNetV2 demonstrate a very competitive geometry reconstruction result compared with state-of-the-art reconstruction methods as well as promising 3D panoptic segmentation result with only RGB input while being real-time. Code is available at: https://github.com/Riser6/PanoRecon.
HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Authors: HyunJun Jung, Shun-Cheng Wu, Patrick Ruhkamp, Guangyao Zhai, Hannah Schieber, Giulia Rizzoli, Pengyuan Wang, Hongcheng Zhao, Lorenzo Garattoni, Sven Meier, Daniel Roth, Nassir Navab, Benjamin Busam
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Pose Estimation

    Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches research is shifting towards category-level pose estimation for practical applications. Current category-level datasets however fall short in annotation quality and pose variety. Addressing this we introduce HouseCat6D a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P) 2) encompasses 194 diverse objects across 10 household categories including two photometrically challenging ones and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage 5) a checkerboard-free environment and 6. dense 6D parallel-jaw robotic grasp annotations. Additionally we present benchmark results for leading category-level pose estimation networks.
Ink Dot-Oriented Differentiable Optimization for Neural Image Halftoning
Authors: Hao Jiang, Bingfeng Zhou, Yadong Mu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Halftoning is a time-honored printing technique that simulates continuous tones using ink dots (halftone dots). The resurgence of deep learning has catalyzed the emergence of innovative technologies in the printing industry fostering the advancement of data-driven halftoning methods. Nevertheless current deep learning-based approaches produce halftones through image-to-image black box transformations lacking direct control over the movement of individual halftone dots. In this paper we propose an innovative halftoning method termed "neural dot-controllable halftoning". This method allows dot-level image dithering by providing direct control over the motion of each ink dot. We conceptualize halftoning as the process of sprinkling dots on a canvas. Initially a specific quantity of dots are randomly dispersed on the canvas and subsequently adjusted based on the surrounding grayscale and gradient. To establish differentiable transformations between discrete ink dot positions and halftone matrices we devise a lightweight dot encoding network to spread dense gradients to sparse dots. Dot control offers several advantages to our approach including the capability to regulate the quantity of halftone dots and enhance specific areas with artifacts in the generated halftones by adjusting the placement of the dots. Our proposed method exhibits superior performance than previous approaches in extensive quantitative and qualitative experiments.
Learning Degradation-unaware Representation with Prior-based Latent Transformations for Blind Face Restoration
Authors: Lianxin Xie, Csbingbing Zheng, Wen Xue, Le Jiang, Cheng Liu, Si Wu, Hau San Wong
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Blind Face Restoration

    Blind face restoration focuses on restoring high-fidelity details from images subjected to complex and unknown degradations while preserving identity information. In this paper we present a Prior-based Latent Transformation approach (PLTrans) which is specifically designed to learn a degradation-unaware representation thereby allowing the restoration network to effectively generalize to real-world degradation. Toward this end PLTrans learns a degradation-unaware query via a latent diffusion-based regularization module. Furthermore conditioned on the features of a degraded face image a latent dictionary that captures the priors of HQ face images is leveraged to refine the features by mapping the top-d nearest elements. The refined version will be used to build key and value for the cross-attention computation which is tailored to each degraded image and exhibits reduced sensitivity to different degradation factors. Conditioned on the resulting representation we train a decoding network that synthesizes face images with authentic details and identity preservation. Through extensive experiments we verify the effectiveness of the design elements and demonstrate the generalization ability of our proposed approach for both synthetic and unknown degradations. We finally demonstrate the applicability of PLTrans in other vision tasks.
TextNeRF: A Novel Scene-Text Image Synthesis Method based on Neural Radiance Fields
Authors: Jialei Cui, Jianwei Du, Wenzhuo LIU, Zhouhui Lian
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Generation, NeRF, Text Detection

    Acquiring large-scale well-annotated datasets is essential for training robust scene text detectors yet the process is often resource-intensive and time-consuming. While some efforts have been made to explore the synthesis of scene text images a notable gap remains between synthetic and authentic data. In this paper we introduce a novel method that utilizes Neural Radiance Fields (NeRF) to model real-world scenes and emulate the data collection process by rendering images from diverse camera perspectives enriching the variability and realism of the synthesized data. A semi-supervised learning framework is proposed to categorize semantic regions within 3D scenes ensuring consistent labeling of text regions across various viewpoints. Our method also models the pose and view-dependent appearance of text regions thereby offering precise control over camera poses and significantly improving the realism of text insertion and editing within scenes. Employing our technique on real-world scenes has led to the creation of a novel scene text image dataset. Compared to other existing benchmarks the proposed dataset is distinctive in providing not only standard annotations such as bounding boxes and transcriptions but also the information of 3D pose attributes for text regions enabling a more detailed evaluation of the robustness of text detection algorithms. Through extensive experiments we demonstrate the effectiveness of our proposed method in enhancing the performance of scene text detectors.
Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion
Authors: Naishan Zheng, Man Zhou, Jie Huang, JunMing Hou, Haoying Li, Yuan Xu, Feng Zhao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Infrared And Visible Image Fusion

    Infrared and visible image fusion aims to generate a fused image by integrating and distinguishing complementary information from multiple sources. While the cross-attention mechanism with global spatial interactions appears promising it only capture second-order spatial interactions neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean) we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies leading in superior performance over state-of-the-art alternatives as shown through comprehensive experiments across various benchmarks.
Scaling Laws for Data Filtering-- Data Curation cannot be Compute Agnostic
Authors: Sachin Goyal, Pratyush Maini, Zachary C. Lipton, aditi raghunathan, J. Zico Kolter
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: GPU

    Vision-language models (VLMs) are trained for thousands of GPU hours on carefully selected subsets of massive web scrapes. For instance the LAION public dataset retained only about 10 percent of the total crawled data. In recent times data curation has gained prominence with several works developing strategies to retain high-quality subsets of raw scraped data. However these strategies are typically developed agnostic to the available compute for training. In this paper we demonstrate that making filtering decisions independent of training compute is often suboptimal: well-curated data rapidly loses its utility when repeated eventually decreasing below the utility of unseen but lower-quality data. While past research in neural scaling laws has considered web data to be homogenous real data is not. Our work bridges this important gap in the literature by developing scaling laws that characterize the differing utility of various data subsets and accounting for how this diminishes for a data point at its nth repetition. Our key message is that data curation can not be agnostic of the total compute a model will be trained for. Even without ever jointly training on multiple data buckets our scaling laws enable us to estimate model performance under this dynamic trade-off between quality and repetition. This allows us to curate the best possible pool for achieving top performance on Datacomp at various compute budgets carving out a pareto-frontier for data curation.