pwc-archive/papers-with-abstracts huggingface dataset:
State Space Model Meets Transformer: A New Paradigm for 3D Object Detection
Authors: Chuxin Wang, Wenfei Yang, Xiang Liu, Tianzhu Zhang
Date: 2025-03-18 00:00:00
Proceeding: International Conference on Learning Representations 2025 1
Tasks: 3D Object Detection, Decoder, object-detection, Object Detection, State Space Models

DETR-based methods, which use multi-layer transformer decoders to refine object queries iteratively, have shown promising performance in 3D indoor object detection. However, the scene point features in the transformer decoder remain fixed, leading to minimal contributions from later decoder layers, thereby limiting performance improvement. Recently, State Space Models (SSM) have shown efficient context modeling ability with linear complexity through iterative interactions between system states and inputs. Inspired by SSMs, we propose a new 3D object DEtection paradigm with an interactive STate space model (DEST). In the interactive SSM, we design a novel state-dependent SSM parameterization method that enables system states to effectively serve as queries in 3D indoor detection tasks. In addition, we introduce four key designs tailored to the characteristics of point cloud and SSM: The serialization and bidirectional scanning strategies enable bidirectional feature interaction among scene points within the SSM. The inter-state attention mechanism models the relationships between state points, while the gated feed-forward network enhances inter-channel correlations. To the best of our knowledge, this is the first method to model queries as system states and scene points as system inputs, which can simultaneously update scene point features and query features with linear complexity. Extensive experiments on two challenging datasets demonstrate the effectiveness of our DEST-based method. Our method improves the GroupFree baseline in terms of AP50 on ScanNet V2 (+5.3) and SUN RGB-D (+3.2) datasets. Based on the VDETR baseline, Our method sets a new SOTA on the ScanNetV2 and SUN RGB-D datasets.
Deeply Supervised Flow-Based Generative Models
Authors: Inkyu Shin, Chenglin Yang, Liang-Chieh Chen
Date: 2025-03-18 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Flow based generative models have charted an impressive path across multiple visual generation tasks by adhering to a simple principle: learning velocity representations of a linear interpolant. However, we observe that training velocity solely from the final layer output underutilizes the rich inter layer representations, potentially impeding model convergence. To address this limitation, we introduce DeepFlow, a novel framework that enhances velocity representation through inter layer communication. DeepFlow partitions transformer layers into balanced branches with deep supervision and inserts a lightweight Velocity Refiner with Acceleration (VeRA) block between adjacent branches, which aligns the intermediate velocity features within transformer blocks. Powered by the improved deep supervision via the internal velocity alignment, DeepFlow converges 8 times faster on ImageNet with equivalent performance and further reduces FID by 2.6 while halving training time compared to previous flow based models without a classifier free guidance. DeepFlow also outperforms baselines in text to image generation tasks, as evidenced by evaluations on MSCOCO and zero shot GenEval.
Utilization of Neighbor Information for Image Classification with Different Levels of Supervision
Authors: Gihan Jayatilaka, Abhinav Shrivastava, Matthew Gwilliam
Date: 2025-03-18 00:00:00
Tasks: Clustering, image-classification, Image Classification, Image Clustering

We propose to bridge the gap between semi-supervised and unsupervised image recognition with a flexible method that performs well for both generalized category discovery (GCD) and image clustering. Despite the overlap in motivation between these tasks, the methods themselves are restricted to a single task -- GCD methods are reliant on the labeled portion of the data, and deep image clustering methods have no built-in way to leverage the labels efficiently. We connect the two regimes with an innovative approach that Utilizes Neighbor Information for Classification (UNIC) both in the unsupervised (clustering) and semisupervised (GCD) setting. State-of-the-art clustering methods already rely heavily on nearest neighbors. We improve on their results substantially in two parts, first with a sampling and cleaning strategy where we identify accurate positive and negative neighbors, and secondly by finetuning the backbone with clustering losses computed by sampling both types of neighbors. We then adapt this pipeline to GCD by utilizing the labelled images as ground truth neighbors. Our method yields state-of-the-art results for both clustering (+3% ImageNet-100, Imagenet200) and GCD (+0.8% ImageNet-100, +5% CUB, +2% SCars, +4% Aircraft).
Advances in 4D Generation: A Survey
Authors: Qiaowei Miao, Kehan Li, JinSheng Quan, Zhiyuan Min, Shaojie Ma, Yichao Xu, Yi Yang, Yawei Luo
Date: 2025-03-18 00:00:00
Tasks: Autonomous Driving, Computational Efficiency, Scene Generation, Survey

Generative artificial intelligence has witnessed remarkable advancements across multiple domains in recent years. Building on the successes of 2D and 3D content generation, 4D generation, which incorporates the temporal dimension into generative tasks, has emerged as a burgeoning yet rapidly evolving research area. This paper presents a comprehensive survey of this emerging field, systematically examining its theoretical foundations, key methodologies, and practical applications, with the aim of providing readers with a holistic understanding of the current state and future potential of 4D generation. We begin by introducing the core concepts of 4D data representations, encompassing both structured and unstructured formats, and their implications for generative tasks. Building upon this foundation, we delve into the enabling technologies that drive 4D generation, including advancements in spatiotemporal modeling, neural representations, and generative frameworks. We further review recent studies that employ diverse control mechanisms and representation strategies for generating 4D outputs, categorizing these approaches and summarizing their research trajectories. In addition, we explore the wide-ranging applications of 4D generation techniques, spanning dynamic object modeling, scene generation, digital human synthesis, 4D content editing, and autonomous driving. Finally, we analyze the key challenges inherent to 4D generation, such as data availability, computational efficiency, and spatiotemporal consistency, and propose promising directions for future research. Our code is publicly available at: \href{https://github.com/MiaoQiaowei/Awesome-4D}{https://github.com/MiaoQiaowei/Awesome-4D}.
The Power of Context: How Multimodality Improves Image Super-Resolution
Authors: Kangfu Mei, Hossein Talebi, Mojtaba Ardakani, Vishal M. Patel, Peyman Milanfar, Mauricio Delbracio
Date: 2025-03-18 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Super-Resolution, Super-Resolution

Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inputs. Existing methods often rely on limited image priors, leading to suboptimal results. We propose a novel approach that leverages the rich contextual information available in multiple modalities -- including depth, segmentation, edges, and text prompts -- to learn a powerful generative prior for SISR within a diffusion model framework. We introduce a flexible network architecture that effectively fuses multimodal information, accommodating an arbitrary number of input modalities without requiring significant modifications to the diffusion process. Crucially, we mitigate hallucinations, often introduced by text prompts, by using spatial information from other modalities to guide regional text-based conditioning. Each modality's guidance strength can also be controlled independently, allowing steering outputs toward different directions, such as increasing bokeh through depth or adjusting object prominence via segmentation. Extensive experiments demonstrate that our model surpasses state-of-the-art generative SISR methods, achieving superior visual quality and fidelity. See project page at https://mmsr.kfmei.com/.
Aligning Multimodal LLM with Human Preference: A Survey
Authors: Tao Yu, Yi-Fan Zhang, Chaoyou Fu, Junkang Wu, Jinda Lu, Kun Wang, Xingyu Lu, Yunhang Shen, Guibin Zhang, Dingjie Song, Yibo Yan, Tianlong Xu, Qingsong Wen, Zhang Zhang, Yan Huang, Liang Wang, Tieniu Tan
Date: 2025-03-18 00:00:00
Tasks: Survey

Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs), built upon LLMs, have demonstrated impressive potential in tackling complex tasks involving visual, auditory, and textual data. However, critical issues related to truthfulness, safety, o1-like reasoning, and alignment with human preference remain insufficiently addressed. This gap has spurred the emergence of various alignment algorithms, each targeting different application scenarios and optimization goals. Recent studies have shown that alignment algorithms are a powerful approach to resolving the aforementioned challenges. In this paper, we aim to provide a comprehensive and systematic review of alignment algorithms for MLLMs. Specifically, we explore four key aspects: (1) the application scenarios covered by alignment algorithms, including general image understanding, multi-image, video, and audio, and extended multimodal applications; (2) the core factors in constructing alignment datasets, including data sources, model responses, and preference annotations; (3) the benchmarks used to evaluate alignment algorithms; and (4) a discussion of potential future directions for the development of alignment algorithms. This work seeks to help researchers organize current advancements in the field and inspire better alignment methods. The project page of this paper is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Alignment.
MusicInfuser: Making Video Diffusion Listen and Dance
Authors: Susung Hong, Ira Kemelmacher-Shlizerman, Brian Curless, Steven M. Seitz
Date: 2025-03-18 00:00:00
Tasks: Video Generation

We introduce MusicInfuser, an approach for generating high-quality dance videos that are synchronized to a specified music track. Rather than attempting to design and train a new multimodal audio-video model, we show how existing video diffusion models can be adapted to align with musical inputs by introducing lightweight music-video cross-attention and a low-rank adapter. Unlike prior work requiring motion capture data, our approach fine-tunes only on dance videos. MusicInfuser achieves high-quality music-driven video generation while preserving the flexibility and generative capabilities of the underlying models. We introduce an evaluation framework using Video-LLMs to assess multiple dimensions of dance generation quality. The project page and code are available at https://susunghong.github.io/MusicInfuser.
A Convex formulation for linear discriminant analysis
Authors: Sai Vijay Kumar Surineela, Prathyusha Kanakamalla, Harigovind Harikumar, Tomojit Ghosh
Date: 2025-03-17 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction

We present a supervised dimensionality reduction technique called Convex Linear Discriminant Analysis (ConvexLDA). The proposed model optimizes a multi-objective cost function by balancing two complementary terms. The first term pulls the samples of a class towards its centroid by minimizing a sample's distance from its class-centroid in low dimensional space. The second term pushes the classes far apart by maximizing their hyperellipsoid scattering volume via the logarithm of the determinant (\textit{log det}) of the outer product matrix formed by the low-dimensional class-centroids. Using the negative of the \textit{log det}, we pose the final cost as a minimization problem, which balances the two terms using a hyper-parameter $\lambda$. We demonstrate that the cost function is convex. Unlike Fisher LDA, the proposed method doesn't require to compute the inverse of a matrix, hence avoiding any ill-conditioned problem where data dimension is very high, e.g. RNA-seq data. ConvexLDA doesn't require pair-wise distance calculation, making it faster and more easily scalable. Moreover, the convex nature of the cost function ensures global optimality, enhancing the reliability of the learned embedding. Our experimental evaluation demonstrates that ConvexLDA outperforms several popular linear discriminant analysis (LDA)-based methods on a range of high-dimensional biological data, image data sets, etc.
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
Authors: Pingting Hao, Kunpeng Liu, Wanfu Gao
Date: 2025-03-18 00:00:00
Tasks: feature selection, Multi-Label Learning

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets.
Towards Harmless Multimodal Assistants with Blind Preference Optimization
Authors: Yongqi Li, Lu Yang, Jian Wang, Runyang You, Wenjie Li, Liqiang Nie
Date: 2025-03-18 00:00:00

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction. Given the extensive applications of MLLMs, the associated safety issues have become increasingly critical. Due to the effectiveness of preference optimization in aligning MLLMs with human preferences, there is an urgent need for safety-related preference data for MLLMs. To address this, we construct the MMSafe-PO preference dataset towards harmless multimodal assistants, featuring multimodal instructions, the conversational format, and ranked paired responses from human feedback. We also identify two insightful observations: modality co-defense and modality cheating, which illustrate that MLLMs possess a certain level of inherent defense while still presenting unique safety challenges. Based on these observations, we propose the Blind Preference Optimization (BPO) approach. Comprehensive experiments on three benchmarks show that BPO effectively enhances the safety capabilities of MLLMs. Notably, BPO significantly improves the safety rate of the base MLLM by 45.0%, outperforming the DPO approach. Additionally, applying BPO to the MMSafe-PO dataset greatly reduces the base MLLM's unsafe rate on other safety benchmarks (14.5% on MM-SafetyBench and 82.9% on HarmEval, demonstrating the effectiveness and robustness of both the dataset and the approach. We release code and data at https://lu-yang666.github.io/MMsafe-PO-Web/.
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization
Authors: Jamie Wynn, Zawar Qureshi, Jakub Powierza, Jamie Watson, Mohamed Sayed
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Novel View Synthesis

Exploring real-world spaces using novel-view synthesis is fun, and reimagining those worlds in a different style adds another layer of excitement. Stylized worlds can also be used for downstream tasks where there is limited training data and a need to expand a model's training distribution. Most current novel-view synthesis stylization techniques lack the ability to convincingly change geometry. This is because any geometry change requires increased style strength which is often capped for stylization stability and consistency. In this work, we propose a new autoregressive 3D Gaussian Splatting stylization method. As part of this method, we contribute a new RGBD diffusion model that allows for strength control over appearance and shape stylization. To ensure consistency across stylized frames, we use a combination of novel depth-guided cross attention, feature injection, and a Warp ControlNet conditioned on composite frames for guiding the stylization of new frames. We validate our method via extensive qualitative results, quantitative experiments, and a user study. Code online.
High-Quality 3D Head Reconstruction from Any Single Portrait Image
Authors: Jianfu Zhang, Yujie Gao, Jiahui Zhan, Wentao Wang, Yiyi Zhang, Haohua Zhao, Liqing Zhang
Date: 2025-03-11 00:00:00
Tasks: Novel View Synthesis

In this work, we introduce a novel high-fidelity 3D head reconstruction method from a single portrait image, regardless of perspective, expression, or accessories. Despite significant efforts in adapting 2D generative models for novel view synthesis and 3D optimization, most methods struggle to produce high-quality 3D portraits. The lack of crucial information, such as identity, expression, hair, and accessories, limits these approaches in generating realistic 3D head models. To address these challenges, we construct a new high-quality dataset containing 227 sequences of digital human portraits captured from 96 different perspectives, totalling 21,792 frames, featuring diverse expressions and accessories. To further improve performance, we integrate identity and expression information into the multi-view diffusion process to enhance facial consistency across views. Specifically, we apply identity- and expression-aware guidance and supervision to extract accurate facial representations, which guide the model and enforce objective functions to ensure high identity and expression consistency during generation. Finally, we generate an orbital video around the portrait consisting of 96 multi-view frames, which can be used for 3D portrait model reconstruction. Our method demonstrates robust performance across challenging scenarios, including side-face angles and complex accessories
Design of an Expression Recognition Solution Employing the Global Channel-Spatial Attention Mechanism
Authors: Jun Yu, Yang Zheng, Lei Wang, Yongqi Wang, Shengfan Xu
Date: 2025-03-15 00:00:00
Tasks: Facial Expression Recognition

Facial expression recognition is a challenging classification task with broad application prospects in the field of human - computer interaction. This paper aims to introduce the methods of our upcoming 8th Affective Behavior Analysis in the Wild (ABAW) competition to be held at CVPR2025. To address issues such as low recognition accuracy caused by subtle expression changes and multi - scales in facial expression recognition in videos, we propose global channel - spatial attention and median - enhanced spatial - channel attention to strengthen feature processing for speech and images respectively. Secondly, to fully utilize the complementarity between the speech and facial expression modalities, a speech - and - facial - expression key - frame alignment technique is adopted to calculate the weights of speech and facial expressions. These weights are input into the feature fusion layer for multi - scale dilated fusion, which effectively improves the recognition rate of facial expression recognition. In the facial expression recognition task of the 6th ABAW competition, our method achieved excellent results on the official validation set, which fully demonstrates the effectiveness and competitiveness of the proposed method.
3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction
Authors: Peizhen Zheng, Longfei Wei, Dongjing Jiang, Jianfei Zhang
Date: 2025-03-15 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, Computational Efficiency

The accurate reconstruction of dynamic street scenes is critical for applications in autonomous driving, augmented reality, and virtual reality. Traditional methods relying on dense point clouds and triangular meshes struggle with moving objects, occlusions, and real-time processing constraints, limiting their effectiveness in complex urban environments. While multi-view stereo and neural radiance fields have advanced 3D reconstruction, they face challenges in computational efficiency and handling scene dynamics. This paper proposes a novel 3D Gaussian point distribution method for dynamic street scene reconstruction. Our approach introduces an adaptive transparency mechanism that eliminates moving objects while preserving high-fidelity static scene details. Additionally, iterative refinement of Gaussian point distribution enhances geometric accuracy and texture representation. We integrate directional encoding with spatial position optimization to optimize storage and rendering efficiency, reducing redundancy while maintaining scene integrity. Experimental results demonstrate that our method achieves high reconstruction quality, improved rendering performance, and adaptability in large-scale dynamic environments. These contributions establish a robust framework for real-time, high-precision 3D reconstruction, advancing the practicality of dynamic scene modeling across multiple applications.
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection
Authors: Xin Jin, Haisheng Su, Kai Liu, Cong Ma, Wei Wu, Fei Hui, Junchi Yan
Date: 2025-03-15 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Object Detection, Mamba, object-detection, Object Detection, Representation Learning, State Space Models

Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces, which serialize the 3D voxels into the flattened 1D sequence for iterative self-attention. However, the spatial structure of 3D voxels will be inevitably destroyed during the serialization process. Besides, due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field. Inspired by the impressive performance of State Space Models (SSM) achieved in the field of 2D vision tasks, in this paper, we propose a novel Unified Mamba (UniMamba), which seamlessly integrates the merits of 3D convolution and SSM in a concise multi-head manner, aiming to perform "local and global" spatial context aggregation efficiently and simultaneously. Specifically, a UniMamba block is designed which mainly consists of spatial locality modeling, complementary Z-order serialization and local-global sequential aggregator. The spatial locality modeling module integrates 3D submanifold convolution to capture the dynamic spatial position embedding before serialization. Then the efficient Z-order curve is adopted for serialization both horizontally and vertically. Furthermore, the local-global sequential aggregator adopts the channel grouping strategy to efficiently encode both "local and global" spatial inter-dependencies using multi-head SSM. Additionally, an encoder-decoder architecture with stacked UniMamba blocks is formed to facilitate multi-scale spatial learning hierarchically. Extensive experiments are conducted on three popular datasets: nuScenes, Waymo and Argoverse 2. Particularly, our UniMamba achieves 70.2 mAP on the nuScenes dataset.