pwc-archive/papers-with-abstracts huggingface dataset:
EgoMe: A New Dataset and Challenge for Following Me via Egocentric View in Real World
Authors: Heqian Qiu, Zhaofeng Shi, Lanxiao Wang, Huiyu Xiong, Xiang Li, Hongliang Li
Date: 2025-01-31 00:00:00
Tasks: Imitation Learning

In human imitation learning, the imitator typically take the egocentric view as a benchmark, naturally transferring behaviors observed from an exocentric view to their owns, which provides inspiration for researching how robots can more effectively imitate human behavior. However, current research primarily focuses on the basic alignment issues of ego-exo data from different cameras, rather than collecting data from the imitator's perspective, which is inconsistent with the high-level cognitive process. To advance this research, we introduce a novel large-scale egocentric dataset, called EgoMe, which towards following the process of human imitation learning via the imitator's egocentric view in the real world. Our dataset includes 7902 paired exo-ego videos (totaling15804 videos) spanning diverse daily behaviors in various real-world scenarios. For each video pair, one video captures an exocentric view of the imitator observing the demonstrator's actions, while the other captures an egocentric view of the imitator subsequently following those actions. Notably, EgoMe uniquely incorporates exo-ego eye gaze, other multi-modal sensor IMU data and different-level annotations for assisting in establishing correlations between observing and imitating process. We further provide a suit of challenging benchmarks for fully leveraging this data resource and promoting the robot imitation learning research. Extensive analysis demonstrates significant advantages over existing datasets. Our EgoMe dataset and benchmarks are available at https://huggingface.co/datasets/HeqianQiu/EgoMe.
Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment
Authors: Song-Lin Lv, Yu-Yang Chen, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
Date: 2025-01-31 00:00:00

Vision-language models (VLMs), such as CLIP, have demonstrated exceptional generalization capabilities and can quickly adapt to downstream tasks through prompt fine-tuning. Unfortunately, in classification tasks involving non-training classes, known as open-vocabulary setting, fine-tuned VLMs often overfit to train classes, resulting in a misalignment between confidence scores and actual accuracy on unseen classes, which significantly undermines their reliability in real-world deployments. Existing confidence calibration methods typically require training parameters or analyzing features from the training dataset, restricting their ability to generalize unseen classes without corresponding train data. Moreover, VLM-specific calibration methods rely solely on text features from train classes as calibration indicators, which inherently limits their ability to calibrate train classes. To address these challenges, we propose an effective multimodal calibration method Contrast-Aware Calibration (CAC). Building on the original CLIP's zero-shot adaptability and the conclusion from empirical analysis that poor intra-class and inter-class discriminative ability on unseen classes is the root cause, we calculate calibration weights based on the contrastive difference between the original and fine-tuned CLIP. This method not only adapts to calibrating unseen classes but also overcomes the limitations of previous VLM calibration methods that could not calibrate train classes. In experiments involving 11 datasets with 5 fine-tuning methods, CAC consistently achieved the best calibration effect on both train and unseen classes without sacrificing accuracy and inference speed.
Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models
Authors: Ruiyu Wang, Yu Yuan, Shizhao Sun, Jiang Bian
Date: 2025-01-31 00:00:00

Creating Computer-Aided Design (CAD) models requires significant expertise and effort. Text-to-CAD, which converts textual descriptions into CAD parametric sequences, is crucial in streamlining this process. Recent studies have utilized ground-truth parametric sequences, known as sequential signals, as supervision to achieve this goal. However, CAD models are inherently multimodal, comprising parametric sequences and corresponding rendered visual objects. Besides,the rendering process from parametric sequences to visual objects is many-to-one. Therefore, both sequential and visual signals are critical for effective training. In this work, we introduce CADFusion, a framework that uses Large Language Models (LLMs) as the backbone and alternates between two training stages: the sequential learning (SL) stage and the visual feedback (VF) stage. In the SL stage, we train LLMs using ground-truth parametric sequences, enabling the generation of logically coherent parametric sequences. In the VF stage, we reward parametric sequences that render into visually preferred objects and penalize those that do not, allowing LLMs to learn how rendered visual objects are perceived and evaluated. These two stages alternate throughout the training, ensuring balanced learning and preserving benefits of both signals. Experiments demonstrate that CADFusion significantly improves performance, both qualitatively and quantitatively.
The Role of Graph-based MIL and Interventional Training in the Generalization of WSI Classifiers
Authors: Rita Pereira, M. Rita Verdelho, Catarina Barata, Carlos Santiago
Date: 2025-01-31 00:00:00
Tasks: graph construction, Multiple Instance Learning

Whole Slide Imaging (WSI), which involves high-resolution digital scans of pathology slides, has become the gold standard for cancer diagnosis, but its gigapixel resolution and the scarcity of annotated datasets present challenges for deep learning models. Multiple Instance Learning (MIL), a widely-used weakly supervised approach, bypasses the need for patch-level annotations. However, conventional MIL methods overlook the spatial relationships between patches, which are crucial for tasks such as cancer grading and diagnosis. To address this, graph-based approaches have gained prominence by incorporating spatial information through node connections. Despite their potential, both MIL and graph-based models are vulnerable to learning spurious associations, like color variations in WSIs, affecting their robustness. In this dissertation, we conduct an extensive comparison of multiple graph construction techniques, MIL models, graph-MIL approaches, and interventional training, introducing a new framework, Graph-based Multiple Instance Learning with Interventional Training (GMIL-IT), for WSI classification. We evaluate their impact on model generalization through domain shift analysis and demonstrate that graph-based models alone achieve the generalization initially anticipated from interventional training. Our code is available here: github.com/ritamartinspereira/GMIL-IT
Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)
Authors: Maja Pavlovic
Date: 2025-01-31 00:00:00

To be considered reliable, a model must be calibrated so that its confidence in each decision closely reflects its true outcome. In this blogpost we'll take a look at the most commonly used definition for calibration and then dive into a frequently used evaluation measure for model calibration. We'll then cover some of the drawbacks of this measure and how these surfaced the need for additional notions of calibration, which require their own new evaluation measures. This post is not intended to be an in-depth dissection of all works on calibration, nor does it focus on how to calibrate models. Instead, it is meant to provide a gentle introduction to the different notions and their evaluation measures as well as to re-highlight some issues with a measure that is still widely used to evaluate calibration.
Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing
Authors: Genc Hoxha, Olivér Angyal, Begüm Demir
Date: 2025-01-31 00:00:00
Tasks: Retrieval, Time Series

The development of image time series retrieval (ITSR) methods is a growing research interest in remote sensing (RS). Given a user-defined image time series (i.e., the query time series), the ITSR methods search and retrieve from large archives the image time series that have similar content to the query time series. The existing ITSR methods in RS are designed for unimodal retrieval problems, limiting their usability and versatility. To overcome this issue, as a first time in RS we introduce the task of cross-modal text-ITSR. In particular, we present a self-supervised cross-modal text-image time series retrieval (text-ITSR) method that enables the retrieval of image time series using text sentences as queries, and vice versa. In detail, we focus our attention on text-ITSR in pairs of images (i.e., bitemporal images). The proposed text-ITSR method consists of two key components: 1) modality-specific encoders to model the semantic content of bitemporal images and text sentences with discriminative features; and 2) modality-specific projection heads to align textual and image representations in a shared embedding space. To effectively model the temporal information within the bitemporal images, we introduce two fusion strategies: i) global feature fusion (GFF) strategy that combines global image features through simple yet effective operators; and ii) transformer-based feature fusion (TFF) strategy that leverages transformers for fine-grained temporal integration. Extensive experiments conducted on two benchmark RS archives demonstrate the effectiveness of the proposed method in accurately retrieving semantically relevant bitemporal images (or text sentences) to a query text sentence (or bitemporal image). The code of this work is publicly available at https://git.tu-berlin.de/rsim/cross-modal-text-tsir.
Beyond Token Compression: A Training-Free Reduction Framework for Efficient Visual Processing in MLLMs
Authors: Hongliang Li, Jiaxin Zhang, Wenhui Liao, Dezhi Peng, Kai Ding, Lianwen Jin
Date: 2025-01-31 00:00:00
Tasks: Decoder

Multimodal Large Language Models (MLLMs) are typically based on decoder-only or cross-attention architectures. While decoder-only MLLMs outperform their cross-attention counterparts, they require significantly higher computational resources due to extensive self-attention and FFN operations on visual tokens. This raises the question: can we eliminate these expensive operations while maintaining the performance? To this end, we present a novel analysis framework to investigate the necessity of these costly operations in decoder-only MLLMs. Our framework introduces two key innovations: (1) Hollow Attention, which limits visual token interactions to local attention while maintaining visual-text associations, and (2) Probe-Activated Dynamic FFN, which selectively activates FFN parameters for visual tokens. Both methods do not require fine-tuning, which significantly enhances analysis efficiency. To assess the impact of applying these reductions across different proportions of layers, we developed a greedy search method that significantly narrows the search space. Experiments on state-of-the-art MLLMs reveal that applying our reductions to approximately half of the layers not only maintains but sometimes improves model performance, indicating significant computational redundancy in current architectures. Additionally, our method is orthogonal to existing token compression techniques, allowing for further combination to achieve greater computational reduction. Our findings may provide valuable insights for the design of more efficient future MLLMs. Our code will be publicly available at https://github.com/L-Hugh/Beyond-Token-Compression.
SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging
Authors: Javier Montalvo, Pablo Carballeira, Álvaro García-Martín
Date: 2025-01-31 00:00:00
Tasks: Autonomous Driving, LIDAR Semantic Segmentation, Segmentation, Semantic Segmentation, Transfer Learning

Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI have been manually collected and labeled, the introduction of simulation tools such as CARLA, has enabled the creation of synthetic datasets on demand. In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution. Using this tool, we have generated SynthmanticLiDAR, a synthetic dataset for semantic segmentation on LiDAR imaging, designed to be similar to SemanticKITTI, and we evaluate its contribution to the training process of different semantic segmentation algorithms by using a naive transfer learning approach. Our results show that incorporating SynthmanticLiDAR into the training process improves the overall performance of tested algorithms, proving the usefulness of our dataset, and therefore, our adapted CARLA simulator. The dataset and simulator are available in https://github.com/vpulab/SynthmanticLiDAR.
XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
Authors: Bo Lan, Pei Li, Jiaxi Yin, Yunpeng Song, Ge Wang, Han Ding, Jinsong Han, Fei Wang
Date: 2025-01-31 00:00:00
Tasks: Action Localization, Action Recognition, Pose Estimation, Synthetic Data Generation, Temporal Action Localization

Human Action Recognition (HAR) plays a crucial role in applications such as health monitoring, smart home automation, and human-computer interaction. While HAR has been extensively studied, action summarization, which involves identifying and summarizing continuous actions, remains an emerging task. This paper introduces the novel XRF V2 dataset, designed for indoor daily activity Temporal Action Localization (TAL) and action summarization. XRF V2 integrates multimodal data from Wi-Fi signals, IMU sensors (smartphones, smartwatches, headphones, and smart glasses), and synchronized video recordings, offering a diverse collection of indoor activities from 16 volunteers across three distinct environments. To tackle TAL and action summarization, we propose the XRFMamba neural network, which excels at capturing long-term dependencies in untrimmed sensory sequences and outperforms state-of-the-art methods, such as ActionFormer and WiFiTAD. We envision XRF V2 as a valuable resource for advancing research in human action localization, action forecasting, pose estimation, multimodal foundation models pre-training, synthetic data generation, and more.
Virtual airways heatmaps to optimize point of entry location in lung biopsy planning systems
Authors: Debora Gil, Pere Lloret, Marta Diez-Ferrer, Carles Sanchez
Date: 2025-01-31 00:00:00
Tasks: Anatomy

Purpose: We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the actual orientation during the operation. Additionally, the study examines the impact of the characteristics of the lesion. Methods: The quality of the biopsy is given by a heatmap projected onto the skeleton of a patient-specific model of airways. The skeleton provides a 3D representation of airways structure, while the heatmap intensity represents the potential amount of tissue that it could be extracted from each POE. This amount of tissue is determined by the intersection of the lesion with a cone that represents the uncertainty area in the introduction of biopsy instruments. The cone, lesion, and skeleton are modelled as graphical objects that define a 3D scene of the intervention. Results: We have simulated different settings of the intervention scene from a single anatomy extracted from a CT scan and two lesions with regular and irregular shapes. The different scenarios are simulated by systematic rotation of each lesion placed at different distances from airways. Analysis of the heatmaps for the different settings show a strong impact of lesion orientation for irregular shape and the distance for both shapes. Conclusion: The proposed heatmaps help to visually assess the optimal POE and identify whether multiple optimal POEs exist in different zones of the bronchi. They also allow us to model the maximum allowable error in navigation systems and study which variables have the greatest influence on the success of the operation. Additionally, they help determine at what point this influence could potentially jeopardize the operation.
Visual Autoregressive Modeling for Image Super-Resolution
Authors: Yunpeng Qu, Kun Yuan, Jinhua Hao, Kai Zhao, Qizhi Xie, Ming Sun, Chao Zhou
Date: 2025-01-31 00:00:00
Tasks: Image Super-Resolution, Quantization, Super-Resolution

Image Super-Resolution (ISR) has seen significant progress with the introduction of remarkable generative models. However, challenges such as the trade-off issues between fidelity and realism, as well as computational complexity, have also posed limitations on their application. Building upon the tremendous success of autoregressive models in the language domain, we propose \textbf{VARSR}, a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction. To effectively integrate and preserve semantic information in low-resolution images, we propose using prefix tokens to incorporate the condition. Scale-aligned Rotary Positional Encodings are introduced to capture spatial structures and the diffusion refiner is utilized for modeling quantization residual loss to achieve pixel-level fidelity. Image-based Classifier-free Guidance is proposed to guide the generation of more realistic images. Furthermore, we collect large-scale data and design a training process to obtain robust generative priors. Quantitative and qualitative results show that VARSR is capable of generating high-fidelity and high-realism images with more efficiency than diffusion-based methods. Our codes will be released at https://github.com/qyp2000/VARSR.
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images
Authors: Zhengrui Guo, Qichen Sun, Jiabo Ma, Lishuang Feng, Jinzhuo Wang, Hao Chen
Date: 2025-01-31 00:00:00
Tasks: Survival Analysis

Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images. While transformer architectures excel at modeling long-range correlations through self-attention, their quadratic computational complexity makes them impractical for computational pathology applications. Existing solutions like local-global or linear self-attention reduce computational costs but compromise the strong modeling capabilities of full self-attention. In this work, we propose Querent, i.e., the query-aware long contextual dynamic modeling framework, which maintains the expressive power of full self-attention while achieving practical efficiency. Our method adaptively predicts which surrounding regions are most relevant for each patch, enabling focused yet unrestricted attention computation only with potentially important contexts. By using efficient region-wise metadata computation and importance estimation, our approach dramatically reduces computational overhead while preserving global perception to model fine-grained patch correlations. Through comprehensive experiments on biomarker prediction, gene mutation prediction, cancer subtyping, and survival analysis across over 10 WSI datasets, our method demonstrates superior performance compared to the state-of-the-art approaches. Code will be made available at https://github.com/dddavid4real/Querent.
OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation
Authors: YuChen Lin, Chenguo Lin, Jianjin Xu, Yadong Mu
Date: 2025-01-31 00:00:00

Recently, significant advancements have been made in the reconstruction and generation of 3D assets, including static cases and those with physical interactions. To recover the physical properties of 3D assets, existing methods typically assume that all materials belong to a specific predefined category (e.g., elasticity). However, such assumptions ignore the complex composition of multiple heterogeneous objects in real scenarios and tend to render less physically plausible animation given a wider range of objects. We propose OmniPhysGS for synthesizing a physics-based 3D dynamic scene composed of more general objects. A key design of OmniPhysGS is treating each 3D asset as a collection of constitutive 3D Gaussians. For each Gaussian, its physical material is represented by an ensemble of 12 physical domain-expert sub-models (rubber, metal, honey, water, etc.), which greatly enhances the flexibility of the proposed model. In the implementation, we define a scene by user-specified prompts and supervise the estimation of material weighting factors via a pretrained video diffusion model. Comprehensive experiments demonstrate that OmniPhysGS achieves more general and realistic physical dynamics across a broader spectrum of materials, including elastic, viscoelastic, plastic, and fluid substances, as well as interactions between different materials. Our method surpasses existing methods by approximately 3% to 16% in metrics of visual quality and text alignment.
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
Authors: Shenghao Fu, Qize Yang, Qijie Mo, Junkai Yan, Xihan Wei, Jingke Meng, Xiaohua Xie, Wei-Shi Zheng
Date: 2025-01-31 00:00:00
Proceeding: CVPR 2025 1
Tasks: Caption Generation, Language Modeling, Language Modelling, Large Language Model

Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset is available at https://github.com/iSEE-Laboratory/LLMDet.