pwc-archive/papers-with-abstracts huggingface dataset:
IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond
Authors: Peng Hu, Chunming He, Lei Xu, Jingduo Tian, Sina Farsiu, Yulun Zhang, Pei Liu, Xiu Li
Date: 2025-03-12 00:00:00
Tasks: Blind Face Restoration, Image Quality Assessment, No-Reference Image Quality Assessment, NR-IQA

Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs. Conventional approaches predominantly rely on learning feature representations from ground-truth (GT) data; however, inherent imperfections in GT datasets constrain restoration performance to the mean quality level of the training data, rather than attaining maximally attainable visual quality. To overcome this limitation, we propose a novel framework that incorporates an Image Quality Prior (IQP) derived from No-Reference Image Quality Assessment (NR-IQA) models to guide the restoration process toward optimal HQ reconstructions. Our methodology synergizes this IQP with a learned codebook prior through two critical innovations: (1) During codebook learning, we devise a dual-branch codebook architecture that disentangles feature extraction into universal structural components and HQ-specific attributes, ensuring comprehensive representation of both common and high-quality facial characteristics. (2) In the codebook lookup stage, we implement a quality-conditioned Transformer-based framework. NR-IQA-derived quality scores act as dynamic conditioning signals to steer restoration toward the highest feasible quality standard. This score-conditioned paradigm enables plug-and-play enhancement of existing BFR architectures without modifying the original structure. We also formulate a discrete representation-based quality optimization strategy that circumvents over-optimization artifacts prevalent in continuous latent space approaches. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques across multiple benchmarks. Besides, our quality-conditioned framework demonstrates consistent performance improvements when integrated with prior BFR models. The code will be released.
Revealing the Implicit Noise-based Imprint of Generative Models
Authors: Xinghan Li, Jingjing Chen, Yue Yu, Xue Song, Haijun Shan, Yu-Gang Jiang
Date: 2025-03-12 00:00:00

With the rapid advancement of vision generation models, the potential security risks stemming from synthetic visual content have garnered increasing attention, posing significant challenges for AI-generated image detection. Existing methods suffer from inadequate generalization capabilities, resulting in unsatisfactory performance on emerging generative models. To address this issue, this paper presents a novel framework that leverages noise-based model-specific imprint for the detection task. Specifically, we propose a novel noise-based imprint simulator to capture intrinsic patterns imprinted in images generated by different models. By aggregating imprints from various generative models, imprints of future models can be extrapolated to expand training data, thereby enhancing generalization and robustness. Furthermore, we design a new pipeline that pioneers the use of noise patterns, derived from a noise-based imprint extractor, alongside other visual features for AI-generated image detection, resulting in a significant improvement in performance. Our approach achieves state-of-the-art performance across three public benchmarks including GenImage, Synthbuster and Chameleon.
DAVE: Diagnostic benchmark for Audio Visual Evaluation
Authors: Gorjan Radevski, Teodora Popordanoska, Matthew B. Blaschko, Tinne Tuytelaars
Date: 2025-03-12 00:00:00
Tasks: Diagnostic

Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-modal learning, existing benchmarks often suffer from strong visual bias -- where answers can be inferred from visual data alone -- and provide only aggregate scores that conflate multiple sources of error. This makes it difficult to determine whether models struggle with visual understanding, audio interpretation, or audio-visual alignment. In this work, we introduce DAVE (Diagnostic Audio Visual Evaluation), a novel benchmark dataset designed to systematically evaluate audio-visual models across controlled challenges. DAVE alleviates existing limitations by (i) ensuring both modalities are necessary to answer correctly and (ii) decoupling evaluation into atomic subcategories. Our detailed analysis of state-of-the-art models reveals specific failure modes and provides targeted insights for improvement. By offering this standardized diagnostic framework, we aim to facilitate more robust development of audio-visual models. The dataset is released: https://github.com/gorjanradevski/dave
SDD-4DGS: Static-Dynamic Aware Decoupling in Gaussian Splatting for 4D Scene Reconstruction
Authors: Dai Sun, Huhao Guan, Kun Zhang, Xike Xie, S. Kevin Zhou
Date: 2025-03-12 00:00:00
Tasks: 4D reconstruction

Dynamic and static components in scenes often exhibit distinct properties, yet most 4D reconstruction methods treat them indiscriminately, leading to suboptimal performance in both cases. This work introduces SDD-4DGS, the first framework for static-dynamic decoupled 4D scene reconstruction based on Gaussian Splatting. Our approach is built upon a novel probabilistic dynamic perception coefficient that is naturally integrated into the Gaussian reconstruction pipeline, enabling adaptive separation of static and dynamic components. With carefully designed implementation strategies to realize this theoretical framework, our method effectively facilitates explicit learning of motion patterns for dynamic elements while maintaining geometric stability for static structures. Extensive experiments on five benchmark datasets demonstrate that SDD-4DGS consistently outperforms state-of-the-art methods in reconstruction fidelity, with enhanced detail restoration for static structures and precise modeling of dynamic motions. The code will be released.
Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness
Authors: Yu Feng, Dingxin Zhang, Runkai Zhao, Yong Xia, Heng Huang, Weidong Cai
Date: 2025-03-12 00:00:00
Tasks: Backdoor Attack

Backdoor attacks pose a severe threat to deep neural networks (DNN) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously. Existing 3D point cloud backdoor attacks primarily rely on sample-wise global modifications, resulting in suboptimal stealthiness. To address this limitation, we propose Stealthy Patch-Wise Backdoor Attack (SPBA), which employs the first patch-wise trigger for 3D point clouds and restricts perturbations to local regions, significantly enhancing stealthiness. Specifically, SPBA decomposes point clouds into local patches and evaluates their geometric complexity using a curvature-based patch imperceptibility score, ensuring that the trigger remains less perceptible to the human eye by strategically applying it across multiple geometrically complex patches with lower visual sensitivity. By leveraging the Graph Fourier Transform (GFT), SPBA optimizes a patch-wise spectral trigger that perturbs the spectral features of selected patches, enhancing attack effectiveness while preserving the global geometric structure of the point cloud. Extensive experiments on ModelNet40 and ShapeNetPart demonstrate that SPBA consistently achieves an attack success rate (ASR) exceeding 96.5% across different models while achieving state-of-the-art imperceptibility compared to existing backdoor attack methods.
GASPACHO: Gaussian Splatting for Controllable Humans and Objects
Authors: Aymen Mir, Arthur Moreau, Helisa Dhamo, Zhensong Zhang, Eduardo Pérez-Pellitero
Date: 2025-03-12 00:00:00
Tasks: Human-Object Interaction Detection, Object

We present GASPACHO: a method for generating photorealistic controllable renderings of human-object interactions. Given a set of multi-view RGB images of human-object interactions, our method reconstructs animatable templates of the human and object as separate sets of Gaussians simultaneously. Different from existing work, which focuses on human reconstruction and ignores objects as background, our method explicitly reconstructs both humans and objects, thereby allowing for controllable renderings of novel human object interactions in different poses from novel-camera viewpoints. During reconstruction, we constrain the Gaussians that generate rendered images to be a linear function of a set of canonical Gaussians. By simply changing the parameters of the linear deformation functions after training, our method can generate renderings of novel human-object interaction in novel poses from novel camera viewpoints. We learn the 3D Gaussian properties of the canonical Gaussians on the underlying 2D manifold of the canonical human and object templates. This in turns requires a canonical object template with a fixed UV unwrapping. To define such an object template, we use a feature based representation to track the object across the multi-view sequence. We further propose an occlusion aware photometric loss that allows for reconstructions under significant occlusions. Several experiments on two human-object datasets - BEHAVE and DNA-Rendering - demonstrate that our method allows for high-quality reconstruction of human and object templates under significant occlusion and the synthesis of controllable renderings of novel human-object interactions in novel human poses from novel camera views.
Unified Dense Prediction of Video Diffusion
Authors: Lehan Yang, Lu Qi, Xiangtai Li, Sheng Li, Varun Jampani, Ming-Hsuan Yang
Date: 2025-03-12 00:00:00
Proceeding: CVPR 2025 1
Tasks: Prediction, Video Generation

We present a unified network for simultaneously generating videos and their corresponding entity segmentation and depth maps from text prompts. We utilize colormap to represent entity masks and depth maps, tightly integrating dense prediction with RGB video generation. Introducing dense prediction information improves video generation's consistency and motion smoothness without increasing computational costs. Incorporating learnable task embeddings brings multiple dense prediction tasks into a single model, enhancing flexibility and further boosting performance. We further propose a large-scale dense prediction video dataset~\datasetname, addressing the issue that existing datasets do not concurrently contain captions, videos, segmentation, or depth maps. Comprehensive experiments demonstrate the high efficiency of our method, surpassing the state-of-the-art in terms of video quality, consistency, and motion smoothness.
Fully-Synthetic Training for Visual Quality Inspection in Automotive Production
Authors: Christoph Huber, Dino Knoll, Michael Guthe
Date: 2025-03-12 00:00:00
Tasks: Defect Detection, object-detection, Object Detection

Visual Quality Inspection plays a crucial role in modern manufacturing environments as it ensures customer safety and satisfaction. The introduction of Computer Vision (CV) has revolutionized visual quality inspection by improving the accuracy and efficiency of defect detection. However, traditional CV models heavily rely on extensive datasets for training, which can be costly, time-consuming, and error-prone. To overcome these challenges, synthetic images have emerged as a promising alternative. They offer a cost-effective solution with automatically generated labels. In this paper, we propose a pipeline for generating synthetic images using domain randomization. We evaluate our approach in three real inspection scenarios and demonstrate that an object detection model trained solely on synthetic data can outperform models trained on real images.
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation
Authors: Lianyuan Yu, Xiuzhen Guo, Ji Shi, Hongxiao Wang, Hongwei Li
Date: 2025-03-12 00:00:00
Tasks: Image Segmentation, Mamba, Medical Image Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Semi-supervised learning enhances medical image segmentation by leveraging unlabeled data, reducing reliance on extensive labeled datasets. On the one hand, the distribution discrepancy between limited labeled data and abundant unlabeled data can hinder model generalization. Most existing methods rely on local similarity matching, which may introduce bias. In contrast, Mamba effectively models global context with linear complexity, learning more comprehensive data representations. On the other hand, medical images usually exhibit consistent anatomical structures defined by geometric features. Most existing methods fail to fully utilize global geometric priors, such as volumes, moments etc. In this work, we introduce a global information interaction and geometric priors focus framework (GIGP). Firstly, we present a Global Information Interaction Mamba module to reduce distribution discrepancy between labeled and unlabeled data. Secondly, we propose a Geometric Moment Attention Mechanism to extract richer global geometric features. Finally, we propose Global Geometric Perturbation Consistency to simulate organ dynamics and geometric variations, enhancing the ability of the model to learn generalized features. The superior performance on the NIH Pancreas and Left Atrium datasets demonstrates the effectiveness of our approach.
Post-interactive Multimodal Trajectory Prediction for Autonomous Driving
Authors: Ziyi Huang, Yang Li, Dushuai Li, Yao Mu, Hongmao Qin, Nan Zheng
Date: 2025-03-12 00:00:00
Tasks: Autonomous Driving, Prediction, Trajectory Prediction

Modeling the interactions among agents for trajectory prediction of autonomous driving has been challenging due to the inherent uncertainty in agents' behavior. The interactions involved in the predicted trajectories of agents, also called post-interactions, have rarely been considered in trajectory prediction models. To this end, we propose a coarse-to-fine Transformer for multimodal trajectory prediction, i.e., Pioformer, which explicitly extracts the post-interaction features to enhance the prediction accuracy. Specifically, we first build a Coarse Trajectory Network to generate coarse trajectories based on the observed trajectories and lane segments, in which the low-order interaction features are extracted with the graph neural networks. Next, we build a hypergraph neural network-based Trajectory Proposal Network to generate trajectory proposals, where the high-order interaction features are learned by the hypergraphs. Finally, the trajectory proposals are sent to the Proposal Refinement Network for further refinement. The observed trajectories and trajectory proposals are concatenated together as the inputs of the Proposal Refinement Network, in which the post-interaction features are learned by combining the previous interaction features and trajectory consistency features. Moreover, we propose a three-stage training scheme to facilitate the learning process. Extensive experiments on the Argoverse 1 dataset demonstrate the superiority of our method. Compared with the baseline HiVT-64, our model has reduced the prediction errors by 4.4%, 8.4%, 14.4%, 5.7% regarding metrics minADE6, minFDE6, MR6, and brier-minFDE6, respectively.
Revisiting Medical Image Retrieval via Knowledge Consolidation
Authors: Yang Nan, Huichi Zhou, Xiaodan Xing, Giorgos Papanastasiou, Lei Zhu, Zhifan Gao, Alejandro F Fangi, Guang Yang
Date: 2025-03-12 00:00:00
Tasks: Image Retrieval, Medical Image Retrieval, Retrieval

As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical component of clinical data management, playing a vital role in decision-making and safeguarding patient information. Existing methods usually learn hash functions using bottleneck features, which fail to produce representative hash codes from blended embeddings. Although contrastive hashing has shown superior performance, current approaches often treat image retrieval as a classification task, using category labels to create positive/negative pairs. Moreover, many methods fail to address the out-of-distribution (OOD) issue when models encounter external OOD queries or adversarial attacks. In this work, we propose a novel method to consolidate knowledge of hierarchical features and optimisation functions. We formulate the knowledge consolidation by introducing Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH). DaRF adaptively integrates shallow and deep representations into blended features, and SCH incorporates image fingerprints to enhance the adaptability of positive/negative pairings. These blended features further facilitate OOD detection and content-based recommendation, contributing to a secure AI-driven healthcare environment. Moreover, we present a content-guided ranking to improve the robustness and reproducibility of retrieval results. Our comprehensive assessments demonstrate that the proposed method could effectively recognise OOD samples and significantly outperform existing approaches in medical image retrieval (p<0.05). In particular, our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.
Pig behavior dataset and Spatial-temporal perception and enhancement networks based on the attention mechanism for pig behavior recognition
Authors: Fangzheng Qi, Zhenjie Hou, En Lin, Xing Li, iuzhen Liang, Xinwen Zhou
Date: 2025-03-12 00:00:00

The recognition of pig behavior plays a crucial role in smart farming and welfare assurance for pigs. Currently, in the field of pig behavior recognition, the lack of publicly available behavioral datasets not only limits the development of innovative algorithms but also hampers model robustness and algorithm optimization.This paper proposes a dataset containing 13 pig behaviors that significantly impact welfare.Based on this dataset, this paper proposes a spatial-temporal perception and enhancement networks based on the attention mechanism to model the spatiotemporal features of pig behaviors and their associated interaction areas in video data. The network is composed of a spatiotemporal perception network and a spatiotemporal feature enhancement network. The spatiotemporal perception network is responsible for establishing connections between the pigs and the key regions of their behaviors in the video data. The spatiotemporal feature enhancement network further strengthens the important spatial features of individual pigs and captures the long-term dependencies of the spatiotemporal features of individual behaviors by remodeling these connections, thereby enhancing the model's perception of spatiotemporal changes in pig behaviors. Experimental results demonstrate that on the dataset established in this paper, our proposed model achieves a MAP score of 75.92%, which is an 8.17% improvement over the best-performing traditional model. This study not only improces the accuracy and generalizability of individual pig behavior recognition but also provides new technological tools for modern smart farming. The dataset and related code will be made publicly available alongside this paper.
VideoScan: Enabling Efficient Streaming Video Understanding via Frame-level Semantic Carriers
Authors: Ruanjun Li, Yuedong Tan, Yuanming Shi, Jiawei Shao
Date: 2025-03-12 00:00:00
Tasks: GPU, Streaming video understanding, Video Understanding

This paper introduces VideoScan, an efficient vision-language model (VLM) inference framework designed for real-time video interaction that effectively comprehends and retains streamed video inputs while delivering rapid and accurate responses. A longstanding challenge in video understanding--particularly for long-term or real-time applications--stems from the substantial computational overhead caused by the extensive length of visual tokens. To address this, VideoScan employs a single semantic carrier token to represent each frame, progressively reducing computational and memory overhead during its two-phase inference process: prefilling and decoding. The embedding of the semantic carrier token is derived from an optimized aggregation of frame-level visual features, ensuring compact yet semantically rich representations. Critically, the corresponding key-value pairs are trained to retain contextual semantics from prior frames, enabling efficient memory management without sacrificing temporal coherence. During inference, the visual tokens of each frame are processed only once during the prefilling phase and subsequently discarded in the decoding stage, eliminating redundant computations. This design ensures efficient VLM inference even under stringent real-time constraints. Comprehensive experiments on diverse offline and online benchmarks demonstrate that LLaVA-Video, supported by our method, achieves up to $\sim 5\times$ and $1.29\times$ speedups compared to its original version and previous efficient streaming video understanding approaches, respectively. Crucially, these improvements are attained while maintaining competitive performance and ensuring stable GPU memory consumption (consistently $\sim 18$GB, independent of video duration).
Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models
Authors: Xiaozhen Qiao, Peng Huang, Jiakang Yuan, Xianda Guo, Bowen Ye, Zhe Sun, Xuelong Li
Date: 2025-03-12 00:00:00
Tasks: Test-time Adaptation

Test-time adaptation (TTA) is crucial in maintaining Vision-Language Models (VLMs) performance when facing real-world distribution shifts, particularly when the source data or target labels are inaccessible. Existing TTA methods rely on CLIP's output probability distribution for feature evaluation, which can introduce biases under domain shifts. This misalignment may cause features to be misclassified due to text priors or incorrect textual associations. To address these limitations, we propose Bidirectional Prototype-Reward co-Evolution (BPRE), a novel TTA framework for VLMs that integrates feature quality assessment with prototype evolution through a synergistic feedback loop. BPRE first employs a Multi-Dimensional Quality-Aware Reward Module to evaluate feature quality and guide prototype refinement precisely. The continuous refinement of prototype quality through Prototype-Reward Interactive Evolution will subsequently enhance the computation of more robust Multi-Dimensional Quality-Aware Reward Scores. Through the bidirectional interaction, the precision of rewards and the evolution of prototypes mutually reinforce each other, forming a self-evolving cycle. Extensive experiments are conducted across 15 diverse recognition datasets encompassing natural distribution shifts and cross-dataset generalization scenarios. Results demonstrate that BPRE consistently achieves superior average performance compared to state-of-the-art methods across different model architectures, such as ResNet-50 and ViT-B/16. By emphasizing comprehensive feature evaluation and bidirectional knowledge refinement, BPRE advances VLM generalization capabilities, offering a new perspective on TTA.