pwc-archive/papers-with-abstracts huggingface dataset:
Structure-Guided MR-to-CT Synthesis with Spatial and Semantic Alignments for Attenuation Correction of Whole-Body PET/MR Imaging
Authors: Jiaxu Zheng, Zhenrong Shen, Lichi Zhang, Qun Chen
Date: 2024-11-26 00:00:00
Tasks: Contrastive Learning

Deep-learning-based MR-to-CT synthesis can estimate the electron density of tissues, thereby facilitating PET attenuation correction in whole-body PET/MR imaging. However, whole-body MR-to-CT synthesis faces several challenges including the issue of spatial misalignment and the complexity of intensity mapping, primarily due to the variety of tissues and organs throughout the whole body. Here we propose a novel whole-body MR-to-CT synthesis framework, which consists of three novel modules to tackle these challenges: (1) Structure-Guided Synthesis module leverages structure-guided attention gates to enhance synthetic image quality by diminishing unnecessary contours of soft tissues; (2) Spatial Alignment module yields precise registration between paired MR and CT images by taking into account the impacts of tissue volumes and respiratory movements, thus providing well-aligned ground-truth CT images during training; (3) Semantic Alignment module utilizes contrastive learning to constrain organ-related semantic information, thereby ensuring the semantic authenticity of synthetic CT images.We conduct extensive experiments to demonstrate that the proposed whole-body MR-to-CT framework can produce visually plausible and semantically realistic CT images, and validate its utility in PET attenuation correction.
Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding
Authors: Mengzhao Wang, Huafeng Li, Yafei Zhang, Jinxing Li, Minghong Xie, Dapeng Tao
Date: 2024-11-26 00:00:00
Tasks: Contrastive Learning, Retrieval

Video Paragraph Grounding (VPG) aims to precisely locate the most appropriate moments within a video that are relevant to a given textual paragraph query. However, existing methods typically rely on large-scale annotated temporal labels and assume that the correspondence between videos and paragraphs is known. This is impractical in real-world applications, as constructing temporal labels requires significant labor costs, and the correspondence is often unknown. To address this issue, we propose a Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding method (DMR-JRG). In this method, retrieval and grounding tasks are mutually reinforced rather than being treated as separate issues. DMR-JRG mainly consists of two branches: a retrieval branch and a grounding branch. The retrieval branch uses inter-video contrastive learning to roughly align the global features of paragraphs and videos, reducing modality differences and constructing a coarse-grained feature space to break free from the need for correspondence between paragraphs and videos. Additionally, this coarse-grained feature space further facilitates the grounding branch in extracting fine-grained contextual representations. In the grounding branch, we achieve precise cross-modal matching and grounding by exploring the consistency between local, global, and temporal dimensions of video segments and textual paragraphs. By synergizing these dimensions, we construct a fine-grained feature space for video and textual features, greatly reducing the need for large-scale annotated temporal labels.
TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba
Authors: Xiaowen Ma, ZhenLiang Ni, Xinghao Chen
Date: 2024-11-26 00:00:00
Tasks: image-classification, Image Classification, Instance Segmentation, Mamba, object-detection, Object Detection, Semantic Segmentation

Mamba has shown great potential for computer vision due to its linear complexity in modeling the global context with respect to the input length. However, existing lightweight Mamba-based backbones cannot demonstrate performance that matches Convolution or Transformer-based methods. We observe that simply modifying the scanning path in the image domain is not conducive to fully exploiting the potential of vision Mamba. In this paper, we first perform comprehensive spectral and quantitative analyses, and verify that the Mamba block mainly models low-frequency information under Convolution-Mamba hybrid architecture. Based on the analyses, we introduce a novel Laplace mixer to decouple the features in terms of frequency and input only the low-frequency components into the Mamba block. In addition, considering the redundancy of the features and the different requirements for high-frequency details and low-frequency global information at different stages, we introduce a frequency ramp inception, i.e., gradually reduce the input dimensions of the high-frequency branches, so as to efficiently trade-off the high-frequency and low-frequency components at different layers. By integrating mobile-friendly convolution and efficient Laplace mixer, we build a series of tiny hybrid vision Mamba called TinyViM. The proposed TinyViM achieves impressive performance on several downstream tasks including image classification, semantic segmentation, object detection and instance segmentation. In particular, TinyViM outperforms Convolution, Transformer and Mamba-based models with similar scales, and the throughput is about 2-3 times higher than that of other Mamba-based models. Code is available at https://github.com/xwmaxwma/TinyViM.
Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers
Authors: Fatemeh Nourilenjan Nokabadi, Jean-Francois Lalonde, Christian Gagné
Date: 2024-11-26 00:00:00
Tasks: Object

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.
FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval
Authors: Jingyou Xie, Jiayi Kuang, Zhenzhou Lin, Jiarui Ouyang, Zishuo Zhao, Ying Shen
Date: 2024-11-26 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval, Zero-Shot Learning

Given a query from one modality, few-shot cross-modal retrieval (CMR) retrieves semantically similar instances in another modality with the target domain including classes that are disjoint from the source domain. Compared with classical few-shot CMR methods, vision-language pretraining methods like CLIP have shown great few-shot or zero-shot learning performance. However, they still suffer challenges due to (1) the feature degradation encountered in the target domain and (2) the extreme data imbalance. To tackle these issues, we propose FLEX-CLIP, a novel Feature-level Generation Network Enhanced CLIP. FLEX-CLIP includes two training stages. In multimodal feature generation, we propose a composite multimodal VAE-GAN network to capture real feature distribution patterns and generate pseudo samples based on CLIP features, addressing data imbalance. For common space projection, we develop a gate residual network to fuse CLIP features with projected features, reducing feature degradation in X-shot scenarios. Experimental results on four benchmark datasets show a 7%-15% improvement over state-of-the-art methods, with ablation studies demonstrating enhancement of CLIP features.
VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models
Authors: Lei LI, Yuancheng Wei, Zhihui Xie, Xuqing Yang, YiFan Song, Peiyi Wang, Chenxin An, Tianyu Liu, Sujian Li, Bill Yuchen Lin, Lingpeng Kong, Qi Liu
Date: 2024-11-26 00:00:00
Tasks: Hallucination

Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current assessment methods primarily rely on AI-annotated preference labels from traditional VL tasks, which can introduce biases and often fail to effectively challenge state-of-the-art models. To address these limitations, we introduce VL-RewardBench, a comprehensive benchmark spanning general multimodal queries, visual hallucination detection, and complex reasoning tasks. Through our AI-assisted annotation pipeline combining sample selection with human verification, we curate 1,250 high-quality examples specifically designed to probe model limitations. Comprehensive evaluation across 16 leading large vision-language models, demonstrates VL-RewardBench's effectiveness as a challenging testbed, where even GPT-4o achieves only 65.4% accuracy, and state-of-the-art open-source models such as Qwen2-VL-72B, struggle to surpass random-guessing. Importantly, performance on VL-RewardBench strongly correlates (Pearson's r > 0.9) with MMMU-Pro accuracy using Best-of-N sampling with VL-GenRMs. Analysis experiments uncover three critical insights for improving VL-GenRMs: (i) models predominantly fail at basic visual perception tasks rather than reasoning tasks; (ii) inference-time scaling benefits vary dramatically by model capacity; and (iii) training VL-GenRMs to learn to judge substantially boosts judgment capability (+14.7% accuracy for a 7B VL-GenRM). We believe VL-RewardBench along with the experimental insights will become a valuable resource for advancing VL-GenRMs.
Identity-Preserving Text-to-Video Generation by Frequency Decomposition
Authors: Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyuan Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, Li Yuan
Date: 2024-11-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Human-Domain Subject-to-Video, Image to Video Generation, Open-Domain Subject-to-Video, Text-to-Video Generation, Video Generation

Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This paper pushes the technical frontier of IPT2V in two directions that have not been resolved in literature: (1) A tuning-free pipeline without tedious case-by-case finetuning, and (2) A frequency-aware heuristic identity-preserving DiT-based control scheme. We propose ConsisID, a tuning-free DiT-based controllable IPT2V model to keep human identity consistent in the generated video. Inspired by prior findings in frequency analysis of diffusion transformers, it employs identity-control signals in the frequency domain, where facial features can be decomposed into low-frequency global features and high-frequency intrinsic features. First, from a low-frequency perspective, we introduce a global facial extractor, which encodes reference images and facial key points into a latent space, generating features enriched with low-frequency information. These features are then integrated into shallow layers of the network to alleviate training challenges associated with DiT. Second, from a high-frequency perspective, we design a local facial extractor to capture high-frequency details and inject them into transformer blocks, enhancing the model's ability to preserve fine-grained features. We propose a hierarchical training strategy to leverage frequency information for identity preservation, transforming a vanilla pre-trained video generation model into an IPT2V model. Extensive experiments demonstrate that our frequency-aware heuristic scheme provides an optimal control solution for DiT-based models. Thanks to this scheme, our ConsisID generates high-quality, identity-preserving videos, making strides towards more effective IPT2V. Code: https://github.com/PKU-YuanGroup/ConsisID.
Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps
Authors: Xue Xia, Randall Balestriero, Tao Zhang, Lorenz Hurni
Date: 2024-11-26 00:00:00
Tasks: Entity Alignment, Instance Segmentation, Self-Supervised Learning, Semantic Segmentation, Video Instance Segmentation

Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various historical research endeavors. However, linking these entities across diverse maps remains a persistent challenge for researchers. Traditionally, this has been addressed through a two-step process: detecting entities within individual maps and then associating them via a heuristic-based post-processing step. In this paper, we propose a novel approach that combines segmentation and association of geographic entities in historical maps using video instance segmentation (VIS). This method significantly streamlines geographic entity alignment and enhances automation. However, acquiring high-quality, video-format training data for VIS models is prohibitively expensive, especially for historical maps that often contain hundreds or thousands of geographic entities. To mitigate this challenge, we explore self-supervised learning (SSL) techniques to enhance VIS performance on historical maps. We evaluate the performance of VIS models under different pretraining configurations and introduce a novel method for generating synthetic videos from unlabeled historical map images for pretraining. Our proposed self-supervised VIS method substantially reduces the need for manual annotation. Experimental results demonstrate the superiority of the proposed self-supervised VIS approach, achieving a 24.9\% improvement in AP and a 0.23 increase in F1 score compared to the model trained from scratch.
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Authors: Mingze Sun, JunHao Chen, Junting Dong, Yurun Chen, Xinyu Jiang, Shiwei Mao, Puhua Jiang, Jingbo Wang, Bo Dai, Ruqi Huang
Date: 2024-11-26 00:00:00
Proceeding: CVPR 2025 1

Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging methods. To address this gap, we curate AnimeRig, a large-scale dataset with detailed skeleton and skinning annotations. Building upon this, we propose DRiVE, a novel framework for generating and rigging 3D human characters with intricate structures. Unlike existing methods, DRiVE utilizes a 3D Gaussian representation, facilitating efficient animation and high-quality rendering. We further introduce GSDiff, a 3D Gaussian-based diffusion module that predicts joint positions as spatial distributions, overcoming the limitations of regression-based approaches. Extensive experiments demonstrate that DRiVE achieves precise rigging results, enabling realistic dynamics for clothing and hair, and surpassing previous methods in both quality and versatility. The code and dataset will be made public for academic use upon acceptance.
Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology
Authors: Omnia Alwazzan, Amaya Gallagher-Syed, Thomas O. Millner, Sebastian Brandner, Ioannis Patras, Silvia Marino, Gregory Slabaugh
Date: 2024-11-26 00:00:00
Tasks: Diagnostic, Multiple Instance Learning, Survival Prediction, whole slide images

The integration of DNA methylation data with a Whole Slide Image (WSI) offers significant potential for enhancing the diagnostic precision of central nervous system (CNS) tumor classification in neuropathology. While existing approaches typically integrate encoded omic data with histology at either an early or late fusion stage, the potential of reintroducing omic data through dual fusion remains unexplored. In this paper, we propose the use of omic embeddings during early and late fusion to capture complementary information from local (patch-level) to global (slide-level) interactions, boosting performance through multimodal integration. In the early fusion stage, omic embeddings are projected onto WSI patches in latent-space, which generates embeddings that encapsulate per-patch molecular and morphological insights. This effectively incorporates omic information into the spatial representation of the WSI. These embeddings are then refined with a Multiple Instance Learning gated attention mechanism which attends to diagnostic patches. In the late fusion stage, we reintroduce the omic data by fusing it with slide-level omic-WSI embeddings using a Multimodal Outer Arithmetic Block (MOAB), which richly intermingles features from both modalities, capturing their correlations and complementarity. We demonstrate accurate CNS tumor subtyping across 20 fine-grained subtypes and validate our approach on benchmark datasets, achieving improved survival prediction on TCGA-BLCA and competitive performance on TCGA-BRCA compared to state-of-the-art methods. This dual fusion strategy enhances interpretability and classification performance, highlighting its potential for clinical diagnostics.
CoA: Chain-of-Action for Generative Semantic Labels
Authors: Meng Wei, Zhongnian Li, Peng Ying, Xinzheng Xu
Date: 2024-11-26 00:00:00
Tasks: Autonomous Driving, image-classification, Image Classification

Recent advances in vision-language models (VLM) have demonstrated remarkable capability in image classification. These VLMs leverage a predefined set of categories to construct text prompts for zero-shot reasoning. However, in more open-ended domains like autonomous driving, using a predefined set of labels becomes impractical, as the semantic label space is unknown and constantly evolving. Additionally, fixed embedding text prompts often tend to predict a single label (while in reality, multiple labels commonly exist per image). In this paper, we introduce CoA, an innovative Chain-of-Action (CoA) method that generates labels aligned with all contextually relevant features of an image. CoA is designed based on the observation that enriched and valuable contextual information improves generative performance during inference. Traditional vision-language models tend to output singular and redundant responses. Therefore, we employ a tailored CoA to alleviate this problem. We first break down the generative labeling task into detailed actions and construct an CoA leading to the final generative objective. Each action extracts and merges key information from the previous action and passes the enriched information as context to the next action, ultimately improving the VLM in generating comprehensive and accurate semantic labels. We assess the effectiveness of CoA through comprehensive evaluations on widely-used benchmark datasets and the results demonstrate significant improvements across key performance metrics.
NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds
Authors: Ruikai Cui, Shi Qiu, Jiawei Liu, Saeed Anwar, Nick Barnes
Date: 2024-11-26 00:00:00
Tasks: Surface Reconstruction

Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advancements address this problem by training neural signed distance functions to pull 3D location queries to their closest points on a surface, following the predicted signed distances and the analytical gradients computed by the network. In this paper, we introduce NumGrad-Pull, leveraging the representation capability of tri-plane structures to accelerate the learning of signed distance functions and enhance the fidelity of local details in surface reconstruction. To further improve the training stability of grid-based tri-planes, we propose to exploit numerical gradients, replacing conventional analytical computations. Additionally, we present a progressive plane expansion strategy to facilitate faster signed distance function convergence and design a data sampling strategy to mitigate reconstruction artifacts. Our extensive experiments across a variety of benchmarks demonstrate the effectiveness and robustness of our approach. Code is available at https://github.com/CuiRuikai/NumGrad-Pull
Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance
Authors: Jingtong Yue, Xin Lin, Zijiu Yang, Chao Ren
Date: 2024-11-26 00:00:00
Tasks: Diversity, Image Quality Assessment, No-Reference Image Quality Assessment

No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. However, performance decreases when facing real-world distortion and restored images from restoration models. The reason is that they do not consider the degradation factors of the low-quality images adequately. To address this issue, we first introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images. After that, we add the restoration network to provide the MOS score predictor with degradation information. Then, we design the Representation-based Semantic Loss (RS Loss) to assist in enhancing effective interaction between representations. Extensive experimental results demonstrate that the proposed method performs favorably against existing state-of-the-art models on both synthetic and real-world datasets.
vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Authors: Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili, Suprosanna Shit, Bjoern Menze
Date: 2024-11-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation, Zero-shot Generalization

Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging protocols, limit the generalization of existing supervised learning-based methods, requiring tedious voxel-level annotations for each dataset separately. While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem. In this work, we present vesselFM, a foundation model designed specifically for the broad task of 3D blood vessel segmentation. Unlike previous models, vesselFM can effortlessly generalize to unseen domains. To achieve zero-shot generalization, we train vesselFM on three heterogeneous data sources: a large, curated annotated dataset, data generated by a domain randomization scheme, and data sampled from a flow matching-based generative model. Extensive evaluations show that vesselFM outperforms state-of-the-art medical image segmentation foundation models across four (pre-)clinically relevant imaging modalities in zero-, one-, and few-shot scenarios, therefore providing a universal solution for 3D blood vessel segmentation.