pwc-archive/papers-with-abstracts huggingface dataset:
Docopilot: Improving Multimodal Models for Document-Level Understanding
Authors: Yuchen Duan, Zhe Chen, Yusong Hu, Weiyun Wang, Shenglong Ye, Botian Shi, Lewei Lu, Qibin Hou, Tong Lu, Hongsheng Li, Jifeng Dai, Wenhai Wang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: document understanding, RAG, Retrieval, Retrieval-augmented Generation

    Despite significant progress in multimodal large language models (MLLMs), their performance on complex, multi-page document comprehension remains inadequate, largely due to the lack of high-quality, document-level datasets. While current retrieval-augmented generation (RAG) methods offer partial solutions, they suffer from issues, such as fragmented retrieval contexts, multi-stage error accumulation, and extra time costs of retrieval. In this work, we present a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents. This dataset includes diverse document structures, extensive cross-page dependencies, and real question-answer pairs derived from the original documents. Building on the dataset, we develop a native multimodal model--Docopilot, which can accurately handle document-level dependencies without relying on RAG. Experiments demonstrate that Docopilot achieves superior coherence, accuracy, and efficiency in document understanding tasks and multi-turn interactions, setting a new baseline for document-level multimodal understanding. Data, code, and models are released at https://github.com/OpenGVLab/Docopilot.
CLIP-driven Coarse-to-fine Semantic Guidance for Fine-grained Open-set Semi-supervised Learning
Authors: Xiaokun Li, Yaping Huang, Qingji Guan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Fine-grained open-set semi-supervised learning (OSSL) investigates a practical scenario where unlabeled data may contain fine-grained out-of-distribution (OOD) samples. Due to the subtle visual differences among in-distribution (ID) samples, as well as between ID and OOD samples, it is extremely challenging to separate the ID and OOD samples. Due to the subtle visual differences among in-distribution (ID) and OOD samples. Recent Vision-Language Models, such as CLIP, have shown excellent generalization capabilities. However, it tends to focus on general attributes, and thus is insufficient to distinguish the fine-grained details. To tackle the issues, in this paper, we propose a novel CLIP-driven coarse-to-fine semantic-guided framework, named CFSG-CLIP, to progressively focus on the distinctive fine-grained clues.  Specifically, CFSG-CLIP comprises a coarse-guidance branch and a fine-guidance branch derived from the pre-trained CLIP model. In the coarse-guidance branch, we design a semantic filtering module to initially filter and highlight local visual features guided by cross-modality features. Then, in the fine-guidance branch, we further design a visual-semantic injection strategy, which embeds category-related visual cues into the visual encoder to further refine the local visual features. By the designed dual-guidance framework, local subtle cues are progressively discovered to distinct the subtle difference between ID and OOD samples. Extensive experiments demonstrate that CFSG-CLIP achieves competitive performance on multiple fine-grained datasets. The source code is available at https://github.com/LxxxxK/CFSG-CLIP.
Point Cloud Upsampling Using Conditional Diffusion Module with Adaptive Noise Suppression
Authors: Boqian Zhang, Shen Yang, Hao Chen, Chao Yang, Jing Jia, Guang Jiang
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: point cloud upsampling

    Point cloud upsampling can improve the quality of the initial point cloud, significantly enhancing the performance of downstream tasks such as classification and segmentation. Existing methods mostly focus on generating the geometric details of point clouds, neglecting noise suppression. To address this, we propose a novel network based on a conditional diffusion model, incorporating the Adaptive Noise Suppression (ANS) module, which we refer to as PDANS. The ANS module assigns weights to each point and determines the removal strategy based on these weights, reducing the impact of noisy points on the sampling process. The module first selects the neighborhood set for each point in the point cloud and performs a weighted sum between the point and its neighbors. It then adjusts the removal points based on the weighted sum, effectively mitigating the bias caused by outliers. We introduce the TreeTrans (TT) module to capture more correlated feature information. This module learns the interaction between high-level and low-level features, resulting in a more comprehensive and refined feature representation. Our results on several widely used benchmark datasets demonstrate that PDANS exhibits exceptional robustness in noisy point cloud processing and outperforms current state-of-the-art(SOTA) methods in terms of performance. Code is available at https://github.com/Baty2023/PDANS.
4D-Fly: Fast 4D Reconstruction from a Single Monocular Video
Authors: Diankun Wu, Fangfu Liu, Yi-Hsin Hung, Yue Qian, Xiaohang Zhan, Yueqi Duan
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 4D reconstruction

    4D reconstruction from a single monocular video is an important but challenging task due to its inherent under-constrained nature. While most existing 4D reconstruction methods focus on multi-camera settings, they always suffer from limited multi-view information in monocular videos. Recent studies have attempted to mitigate the ill-posed problem by incorporating data-driven priors as additional supervision. However, they require hours of optimization to align the splatted 2D feature maps of explicit Gaussians with various priors, which limits the range of applications. To address the time-consuming issue, we propose 4D-Fly, an efficient and effective framework for reconstructing the 4D scene from a monocular video (hundreds of frames within 6 minutes), more than 20 xfaster and even achieving higher quality than previous optimization methods. Our key insight is to unleash the explicit property of Gaussian primitives and directly apply data priors to them. Specifically, we build a streaming 4D reconstruction paradigm that includes: propagating existing Gaussian to the next timestep with an anchor-based strategy, expanding the 4D scene map with the canonical Gaussian map, and an efficient 4D scene optimization process to further improve visual quality and motion accuracy. Extensive experiments demonstrate the superiority of our 4D-Fly over state-of-the-art methods in terms of speed and quality.
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Authors: Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Active Learning

    As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and reduces training costs. Real-world scenarios necessitate the consideration of incomplete data knowledge within AL. Prior works address handling out-of-distribution (OOD) data, while another research direction has focused on category discovery. However, a combined analysis of real-world considerations combining AL with out-of-distribution data and category discovery remains unexplored. To address this gap, we propose Joint Out-of-distribution filtering and data Discovery Active learning (Joda), to uniquely address both challenges simultaneously by filtering out OOD data before selecting candidates for labeling. In contrast to previous methods, we deeply entangle the training procedure with filter and selection to construct a common feature space that aligns known and novel categories while separating OOD samples. Unlike previous works, Joda is highly efficient and completely omits auxiliary models and training access to the unlabeled pool for filtering or selection. In extensive experiments on 18 configurations and 3 metrics, Joda consistently achieves the highest accuracy with the best class discovery to OOD filtering balance compared to state-of-the-art competitor approaches.
FG^2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching
Authors: Zimin Xia, Alexandre Alahi
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Weakly-supervised Learning

    We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the two images. The pose is estimated by aligning a point plane generated from the ground image with a point plane sampled from the aerial image. To generate the ground points, we first map ground image features to a 3D point cloud. Our method then learns to select features along the height dimension to pool the 3D points to a Bird's-Eye-View (BEV) plane. This selection enables us to trace which feature in the ground image contributes to the BEV representation. Next, we sample a set of sparse matches from computed point correspondences between the two point planes and compute their relative pose using Procrustes alignment. Compared to the previous state-of-the-art, our method reduces the mean localization error by 28% on the VIGOR cross-area test set. Qualitative results show that our method learns semantically consistent matches across ground and aerial views through weakly supervised learning from the camera pose.
Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated Imagery
Authors: Sajjad Abdoli, Freeman Lewin, Gediminas Vasiliauskas, Fabian Schonholz
Date: 2025-06-06 00:00:00
Tasks: Hyperparameter Optimization, Image Generation

The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation tasks, is undergoing a paradigmatic shift in development methodologies. Traditionally dominated by a "Model Centric" approach, in which performance gains were primarily pursued through increasingly complex model architectures and hyperparameter optimization, the field is now recognizing a more nuanced "Data-Centric" approach. This emergent framework foregrounds the quality, structure, and relevance of training data as the principal driver of model performance. To operationalize this paradigm shift, we introduce the DataSeeds.AI sample dataset (the "DSD"), initially comprised of approximately 10,610 high-quality human peer-ranked photography images accompanied by extensive multi-tier annotations. The DSD is a foundational computer vision dataset designed to usher in a new standard for commercial image datasets. Representing a small fraction of DataSeed.AI's 100 million-plus image catalog, the DSD provides a scalable foundation necessary for robust commercial and multimodal AI development. Through this in-depth exploratory analysis, we document the quantitative improvements generated by the DSD on specific models against known benchmarks and make the code and the trained models used in our evaluation publicly available.
Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions
Authors: Haotian Jiang, Zeyu Bao, Shida Wang, Qianxiao Li
Date: 2025-06-06 00:00:00
Tasks: Benchmarking, State Space Models

The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data. Despite this progress, systematically characterizing the strengths and limitations of these architectures remains a fundamental challenge.In this work, we propose a synthetic benchmarking framework to evaluate how effectively different sequence models capture distinct temporal structures. The core of this approach is to generate synthetic targets, each characterized by a memory function and a parameter that determines the strength of temporal dependence. This setup allows us to produce a continuum of tasks that vary in temporal complexity, enabling fine-grained analysis of model behavior concerning specific memory properties. We focus on four representative memory functions, each corresponding to a distinct class of temporal structures.Experiments on several sequence modeling architectures confirm existing theoretical insights and reveal new findings.These results demonstrate the effectiveness of the proposed method in advancing theoretical understandingand highlight the importance of using controllable targets with clearly defined structures for evaluating sequence modeling architectures.
Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety
Authors: Ting-Wei Hsu, Hiroyasu Tsukamoto
Date: 2025-06-06 00:00:00
Tasks: Conformal Prediction, Prediction

We present a true-dynamics-agnostic, statistically rigorous framework for establishing exponential stability and safety guarantees of closed-loop, data-driven nonlinear control. Central to our approach is the novel concept of conformal robustness, which robustifies the Lyapunov and zeroing barrier certificates of data-driven dynamical systems against model prediction uncertainties using conformal prediction. It quantifies these uncertainties by leveraging rank statistics of prediction scores over system trajectories, without assuming any specific underlying structure of the prediction model or distribution of the uncertainties. With the quantified uncertainty information, we further construct the conformally robust control Lyapunov function (CR-CLF) and control barrier function (CR-CBF), data-driven counterparts of the CLF and CBF, for fully data-driven control with statistical guarantees of finite-horizon exponential stability and safety. The performance of the proposed concept is validated in numerical simulations with four benchmark nonlinear control problems.
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Authors: Yuanzhe Hu, Kinshuk Goel, Vlad Killiakov, Yaoqing Yang
Date: 2025-06-06 00:00:00
Tasks: image-classification, Image Classification, Large Language Model

Diagnosing deep neural networks (DNNs) through the eigenspectrum of weight matrices has been an active area of research in recent years. At a high level, eigenspectrum analysis of DNNs involves measuring the heavytailness of the empirical spectral densities (ESD) of weight matrices. It provides insight into how well a model is trained and can guide decisions on assigning better layer-wise training hyperparameters. In this paper, we address a challenge associated with such eigenspectrum methods: the impact of the aspect ratio of weight matrices on estimated heavytailness metrics. We demonstrate that matrices of varying sizes (and aspect ratios) introduce a non-negligible bias in estimating heavytailness metrics, leading to inaccurate model diagnosis and layer-wise hyperparameter assignment. To overcome this challenge, we propose FARMS (Fixed-Aspect-Ratio Matrix Subsampling), a method that normalizes the weight matrices by subsampling submatrices with a fixed aspect ratio. Instead of measuring the heavytailness of the original ESD, we measure the average ESD of these subsampled submatrices. We show that measuring the heavytailness of these submatrices with the fixed aspect ratio can effectively mitigate the aspect ratio bias. We validate our approach across various optimization techniques and application domains that involve eigenspectrum analysis of weights, including image classification in computer vision (CV) models, scientific machine learning (SciML) model training, and large language model (LLM) pruning. Our results show that despite its simplicity, FARMS uniformly improves the accuracy of eigenspectrum analysis while enabling more effective layer-wise hyperparameter assignment in these application domains. In one of the LLM pruning experiments, FARMS reduces the perplexity of the LLaMA-7B model by 17.3% when compared with the state-of-the-art method.
A Mixture-Based Framework for Guiding Diffusion Models
Authors: Yazid Janati, Badr Moufad, Mehdi Abou El Qassime, Alain Durmus, Eric Moulines, Jimmy Olsson
Date: 2025-02-05 00:00:00
Tasks: Denoising

Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide range of such problems, only leveraging inference-time compute and thereby eliminating the need to retrain task-specific models on the same dataset. To approximate the posterior of a Bayesian inverse problem, a diffusion model samples from a sequence of intermediate posterior distributions, each with an intractable likelihood function. This work proposes a novel mixture approximation of these intermediate distributions. Since direct gradient-based sampling of these mixtures is infeasible due to intractable terms, we propose a practical method based on Gibbs sampling. We validate our approach through extensive experiments on image inverse problems, utilizing both pixel- and latent-space diffusion priors, as well as on source separation with an audio diffusion model. The code is available at https://www.github.com/badr-moufad/mgdm
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG
Authors: Siavash Shams, Richard Antonello, Gavin Mischler, Stephan Bickel, Ashesh Mehta, Nima Mesgarani
Date: 2025-05-31 00:00:00
Tasks: EEG, Text Generation, Transfer Learning

Decoding continuous language from neural signals remains a significant challenge in the intersection of neuroscience and artificial intelligence. We introduce Neuro2Semantic, a novel framework that reconstructs the semantic content of perceived speech from intracranial EEG (iEEG) recordings. Our approach consists of two phases: first, an LSTM-based adapter aligns neural signals with pre-trained text embeddings; second, a corrector module generates continuous, natural text directly from these aligned embeddings. This flexible method overcomes the limitations of previous decoding approaches and enables unconstrained text generation. Neuro2Semantic achieves strong performance with as little as 30 minutes of neural data, outperforming a recent state-of-the-art method in low-data settings. These results highlight the potential for practical applications in brain-computer interfaces and neural decoding technologies.
Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation
Authors: Edward Fish, Richard Bowden
Date: 2025-05-30 00:00:00
Tasks: Computational Efficiency, Sign Language Translation, Translation

Recent progress in Sign Language Translation (SLT) has focussed primarily on improving the representational capacity of large language models to incorporate Sign Language features. This work explores an alternative direction: enhancing the geometric properties of skeletal representations themselves. We propose Geo-Sign, a method that leverages the properties of hyperbolic geometry to model the hierarchical structure inherent in sign language kinematics. By projecting skeletal features derived from Spatio-Temporal Graph Convolutional Networks (ST-GCNs) into the Poincar\'e ball model, we aim to create more discriminative embeddings, particularly for fine-grained motions like finger articulations. We introduce a hyperbolic projection layer, a weighted Fr\'echet mean aggregation scheme, and a geometric contrastive loss operating directly in hyperbolic space. These components are integrated into an end-to-end translation framework as a regularisation function, to enhance the representations within the language model. This work demonstrates the potential of hyperbolic geometry to improve skeletal representations for Sign Language Translation, improving on SOTA RGB methods while preserving privacy and improving computational efficiency. Code available here: https://github.com/ed-fish/geo-sign.
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
Authors: Mingjie Wei, Xuemei Xie, Yutong Zhong, Guangming Shi
Date: 2025-06-03 00:00:00
Proceeding: IEEE Transactions on Multimedia 2025 1
Tasks: 3D Human Pose Estimation, Graph Attention, Pose Estimation

Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to better learn potential long-range dependencies. It can capture the correlation across joints and groups, which complements the context of the human sub-structure. In an effective cross-scale way, it captures the pyramid-structured long-range dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies. It concatenates information from various scales into a compact sequence, and then computes the correlation between scales in parallel. Combining PGA with graph convolution modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture. It encapsulates human sub-structures into self-attention by pooling. Extensive experiments show that our approach achieves lower error and smaller model size than state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets. The code is available at https://github.com/MingjieWe/PGFormer.
Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
Authors: Sagnik Mukherjee, Lifan Yuan, Dilek Hakkani-Tur, Hao Peng
Date: 2025-05-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) yields substantial improvements in large language models (LLMs) downstream task performance and alignment with human values. Surprisingly, such large gains result from updating only a small subnetwork comprising just 5 percent to 30 percent of the parameters, with the rest effectively unchanged. We refer to this phenomenon as parameter update sparsity induced by RL. It is observed across all 7 widely used RL algorithms (e.g., PPO, GRPO, DPO) and all 10 LLMs from different families in our experiments. This sparsity is intrinsic and occurs without any explicit sparsity promoting regularizations or architectural constraints. Finetuning the subnetwork alone recovers the test accuracy, and, remarkably, produces a model nearly identical to the one obtained via full finetuning. The subnetworks from different random seeds, training data, and even RL algorithms show substantially greater overlap than expected by chance. Our analysis suggests that this sparsity is not due to updating only a subset of layers, instead, nearly all parameter matrices receive similarly sparse updates. Moreover, the updates to almost all parameter matrices are nearly full-rank, suggesting RL updates a small subset of parameters that nevertheless span almost the full subspaces that the parameter matrices can represent. We conjecture that the this update sparsity can be primarily attributed to training on data that is near the policy distribution, techniques that encourage the policy to remain close to the pretrained model, such as the KL regularization and gradient clipping, have limited impact.