GenAI4ELab/papercli-papers huggingface dataset:
Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model
Authors: Yuhan Wang; Suzhi Bi; Ying-Jun Angela Zhang; Xiaojun Yuan
Venue: CVPR 2025 (main)

Abstract: The distortion-perception (DP) tradeoff reveals a fundamental conflict between distortion metrics (e.g., MSE and PSNR) and perceptual quality. Recent research has increasingly concentrated on evaluating denoising algorithms within the DP framework. However, existing algorithms either prioritize perceptual quality by sacrificing acceptable distortion, or focus on minimizing MSE for faithful restoration. When the goal shifts or noisy measurements vary, adapting to different points on the DP plane needs retraining or even re-designing the model. Inspired by recent advances in solving inverse problems using score-based generative models, we explore the potential of flexibly and optimally traversing DP tradeoffs using a single pre-trained score-based model. Specifically, we introduce a variance-scaled reverse diffusion process and theoretically characterize the marginal distribution. We then prove that the proposed sample process is an optimal solution to the DP tradeoff for conditional Gaussian distribution. Experimental results on two-dimensional and image datasets illustrate that a single score network can effectively and flexibly traverse the DP tradeoff for general denoising problems.
IceDiff: High Resolution and High-Quality Arctic Sea Ice Forecasting with Generative Diffusion Prior
Authors: Jingyi Xu; Siwei Tu; Weidong Yang; Ben Fei; Shuhao Li; Keyi Liu; Yeqi Luo; Lipeng Ma; Lei Bai
Venue: CVPR 2025 (main)

Abstract: Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advances in artificial intelligence have made promising progress over numerical models. However, forecasting sea ice at higher resolutions is still under-explored. To bridge the gap, we propose a two-module cooperative deep learning framework, IceDiff, to forecast sea ice concentration at finer scales. IceDiff first leverages a vision transformer to generate coarse yet superior forecasting results over previous methods at a regular 25km grid. This high-quality sea ice forecasting can be utilized as reliable guidance for the next module. Subsequently, an unconditional diffusion model pre-trained on low-resolution sea ice concentration maps is utilized for sampling down-scaled sea ice forecasting via a zero-shot guided sampling strategy and a patch-based method. For the first time, IceDiff demonstrates sea ice forecasting with a 6.25km resolution. IceDiff extends the boundary of existing sea ice forecasting models and more importantly, its capability to generate high-resolution sea ice concentration data is vital for pragmatic usages and research.
DTOS: Dynamic Time Object Sensing with Large Multimodal Model
Authors: Jirui Tian; Jinrong Zhang; Shenglan Liu; Luhao Xu; Zhixiong Huang; Gao Huang
Venue: CVPR 2025 (main)

Abstract: Existing multimodal large language models (MLLMs) face significant challenges in Referring Video Object Segmentation(RVOS). We identify three critical challenges: (C1) insufficient quantitative representation of textual numerical data, (C2) repetitive and degraded response templates for spatiotemporal referencing, and (C3) loss of visual information in video sampling queries lacking textual guidance. To address these, we propose a novel framework, Dynamic Time Object Sensing (DTOS), specifically designed for RVOS. To tackle (C1) and (C2), we introduce specialized tokens to construct multi-answer response templates, enabling regression of event boundaries and target localization. This approach improves the accuracy of numerical regression while mitigating the issue of repetitive degradation. To address (C3), we propose a Text-guided Clip Sampler (TCS) that selects video clips aligned with user instructions, preventing visual information loss and ensuring consistent temporal resolution. TCS is also applicable to Moment Retrieval tasks, with enhanced multimodal input sequences preserving spatial details and maximizing temporal resolution. DTOS demonstrates exceptional capability in flexibly localizing multiple spatiotemporal targets based on user-provided textual instructions. Extensive experiments validate the effectiveness of our approach, with DTOS achieving state-of-the-art performance in J&F scores: an improvement of +4.36 on MeViS, +4.48 on Ref-DAVIS17, and +3.02 on Ref-YT-VOS. Additionally, our TCS demonstrates exceptional performance in Moment Retrieval. The code is available at https://github.com/Maulog/OPEN-DTOS-LMM
How to Merge Your Multimodal Models Over Time?
Authors: Sebastian Dziadzio; Vishaal Udandarao; Karsten Roth; Ameya Prabhu; Zeynep Akata; Samuel Albanie; Matthias Bethge
Venue: CVPR 2025 (main)

Abstract: Model merging combines expert models---each finetuned from a shared foundation model on diverse tasks and domains---into a single, more capable base model. However, existing model merging approaches assume all experts to be available simultaneously. In reality, new tasks and domains emerge continuously, prompting the need for a dynamic process of integrating these experts over time, which we call temporal model merging. The temporal dimension introduces unique challenges not addressed in prior work: At each task, should expert training start from merged previous experts or the original base model? Should all models be merged at every time step? Which merging techniques are best suited for temporal merging? Should different strategies be used for the training initialization and deployment phases? To tackle these questions, we propose a unified framework called TIME---Temporal Integration of Model Expertise---that defines temporal model merging across three axes: (1) Initialization Phase, (2) Deployment Phase, and (3) Merging Technique. Utilizing TIME, we study temporal model merging across model sizes, tasks, and compute budgets on the large-scale FoMo-in-Flux benchmark for continual multimodal pretraining. Systematic experiments across TIME and FoMo-in-Flux allow us to arrive at several crucial key insights for temporal model merging to better understand current limits and best practices for successful model merging across time.
Identifying and Mitigating Position Bias of Multi-image Vision-Language Models
Authors: Xinyu Tian; Shu Zou; Zhaoyuan Yang; Jing Zhang
Venue: CVPR 2025 (main)

Abstract: The evolution of Large Vision-Language Models (LVLMs) has progressed from single-image understanding to multi-image reasoning. Despite this advancement, our findings indicate that LVLMs struggle to robustly utilize information across multiple images, with predictions significantly affected by the alteration of image positions. To further explore this issue, we introduce Position-wise Question Answering (PQA), a meticulously designed task to quantify reasoning capabilities at each position. Our analysis reveals a pronounced position bias in LVLMs: open-source models excel in reasoning with images positioned later but underperform with those in the middle or at the beginning, while proprietary models like GPT-4o show improved comprehension for images at the beginning and end but struggle with those in the middle. Motivated by these insights, we propose SoFt Attention (SoFA), a simple, training-free approach that mitigates this bias by employing linear interpolation between inter-image causal attention and bidirectional counterparts. Experimental results demonstrate that SoFA effectively reduces position bias and significantly enhances the reasoning performance of existing LVLMs.
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Authors: Tim Lenz; Peter Neidlinger; Marta Ligero; Georg Wölflein; Marko van Treeck; Jakob N. Kather
Venue: CVPR 2025 (main)

Abstract: Representation learning of pathology whole-slide images (WSIs) has primarily relied on weak supervision with Multiple Instance Learning (MIL). This approach leads to slide representations highly tailored to a specific clinical task. Self-supervised learning (SSL) has been successfully applied to train histopathology foundation models (FMs) for patch embedding generation. However, generating patient or slide level embeddings remains challenging. Existing approaches for slide representation learning extend the principles of SSL from patch level learning to entire slides by aligning different augmentations of the slide or by utilizing multimodal data. By integrating tile embeddings from multiple FMs, we propose a new single modality SSL method in feature space that generates useful slide representations. Our contrastive pretraining strategy, called COBRA, employs multiple FMs and an architecture based on Mamba-2. COBRA exceeds performance of state-of-the-art slide encoders on four different public Clinical Protemic Tumor Analysis Consortium (CPTAC) cohorts on average by at least +4.4% AUC, despite only being pretrained on 3048 WSIs from The Cancer Genome Atlas (TCGA). Additionally, COBRA is readily compatible at inference time with previously unseen feature extractors. Code available at https://github.com/KatherLab/COBRA
Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
Authors: Zhiwei Yang; Yucong Meng; Kexue Fu; Feilong Tang; Shuo Wang; Zhijian Song
Venue: CVPR 2025 (main)

Abstract: Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP's dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP's training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO. Code is available at https://github.com/zwyang6/ExCEL.
UNIALIGN: Scaling Multimodal Alignment within One Unified Model
Authors: Bo Zhou; Liulei Li; Yujia Wang; Huafeng Liu; Yazhou Yao; Wenguan Wang
Venue: CVPR 2025 (main)

Abstract: We present UNIALIGN, a unified model to align an arbitrary number of modalities (\text e.g. , image, text, audio, 3D point cloud, etc.) through one  encoder and a single training phase. Existing solutions typically employ distinct encoders for each modality, resulting in increased parameters as the number of modalities grows. In contrast, UNIALIGN proposes a modality-aware adaptation of the powerful mixture-of-experts (MoE) schema and further integrates it with Low-Rank Adaptation (LoRA),  efficiently scaling the encoder to accommodate inputs in diverse modalities while maintaining a fixed computational overhead.Moreover, prior work often requires separate training for each extended modality. This leads to task-specific models and further hinders the communication between modalities.To address this, we propose a soft modality binding strategy that aligns all modalities using unpaired data samples across datasets. Two additional training objectives are introduced to distill knowledge from well-aligned anchor modalities and prior multimodal models, elevating UNIALIGN into a high performance multimodal foundation model.Experiments on 11 benchmarks across 6 different modalities demonstrate that UNIALIGN could achieve comparable performance to SOTA approaches, while using merely 7.8M trainable parameters and maintaining an identical model with the same weight across all tasks. Our code shall be released.
ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions
Authors: Tomáš Souček; Prajwal Gatti; Michael Wray; Ivan Laptev; Dima Damen; Josef Sivic
Venue: CVPR 2025 (main)

Abstract: The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while being grounded in a specific environment. Part of the challenge stems from the lack of large-scale training data for this problem. The contribution of this work is thus three-fold. First, we introduce an automatic approach for collecting large step-by-step visual instruction training data from instructional videos. We apply this approach to one million videos and create a large-scale, high-quality dataset of 0.6M sequences of image-text pairs. Second, we develop and train ShowHowTo, a video diffusion model capable of generating step-by-step visual instructions consistent with the provided input image. Third, we evaluate the generated image sequences across three dimensions of accuracy (step, scene, and task) and show our model achieves state-of-the-art results on all of them. Our code, dataset, and trained models are publicly available.
Exploration-Driven Generative Interactive Environments
Authors: Nedko Savov; Naser Kazemi; Mohammad Mahdi; Danda Pani Paudel; Xi Wang; Luc Van Gool
Venue: CVPR 2025 (main)

Abstract: Modern world models require costly and time-consuming collection of large video datasets with action demonstrations by people or by environment-specific agents. To simplify training, we focus on using many virtual environments for inexpensive, automatically collected interaction data. Genie, a recent multi-environment world model, demonstrates simulation abilities of many environments with shared behavior. Unfortunately, training their model requires expensive demonstrations. Therefore, we propose a training framework merely using a random agent in virtual environments. While the model trained in this manner exhibits good controls, it is limited by the random exploration possibilities. To address this limitation, we propose AutoExplore Agent - an exploration agent that entirely relies on the uncertainty of the world model, delivering diverse data from which it can learn the best. Our agent is fully independent of environment-specific rewards and thus adapts easily to new environments. With this approach, the pretrained multi-environment model can quickly adapt to new environments achieving video fidelity and controllability improvement. In order to obtain automatically large-scale interaction datasets for pretraining, we group environments with similar behavior and controls. To this end, we annotate the behavior and controls of 974 virtual environments - a dataset that we name RetroAct. For building our model, we first create an open implementation of Genie - GenieRedux and apply enhancements and adaptations in our version GenieRedux-G.  Our code and data are available at https://github.com/insait-institute/GenieRedux
Task-Agnostic Guided Feature Expansion for Class-Incremental Learning
Authors: Bowen Zheng; Da-Wei Zhou; Han-Jia Ye; De-Chuan Zhan
Venue: CVPR 2025 (main)

Abstract: The ability to learn new concepts while preserve the learned knowledge is desirable for learning systems in Class-Incremental Learning (CIL). Recently, feature expansion of the model become a prevalent solution for CIL, where the old features are fixed during the training of the new task while new features are expanded for the new tasks. However, such task-specific features learned from the new task may collide with the old features, leading to misclassification between tasks. Therefore, the expanded model is often encouraged to capture diverse features from the new task, aiming to avoid such collision. However, the existing solution is largely restricted to the samples from the current task, because of the poor accessibility to previous samples. To promote the learning and transferring of diverse features across tasks, we propose a framework called Task-Agnostic Guided Feature Expansion (TagFex). Firstly, it captures task-agnostic features continually with a separate model, providing extra task-agnostic features for subsequent tasks. Secondly, to obtain useful features from the task-agnostic model for the current task, it aggregates the task-agnostic features with the task-specific feature using a merge attention. Then the aggregated feature is transferred back into the task-specific feature for inference, helping the task-specific model capture diverse features. Extensive experiments show the effectiveness and superiority of TagFex on various CIL settings.
ShowUI: One Vision-Language-Action Model for GUI Visual Agent
Authors: Kevin Qinghong Lin; Linjie Li; Difei Gao; Zhengyuan Yang; Shiwei Wu; Zechen Bai; Stan Weixian Lei; Lijuan Wang; Mike Zheng Shou
Venue: CVPR 2025 (main)

Abstract: Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. While most agents are language-based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, highlighting the need for GUI visual agents.In this work, we develop a vision-language-action model in the digital world, namely "Our Model," which features the following innovations:1. **UI-Guided Visual Token Selection** to reduce computational costs by formulating screenshots as a UI-connected graph, adaptively identifying their redundant relationships and serving as the criteria for token selection during self-attention blocks.  2. **Interleaved Vision-Language-Action Streaming** that flexibly unifies diverse needs within GUI tasks, enabling effective management of visual-action history in navigation or pairing multi-turn query-action sequences per screenshot to enhance training efficiency.  3. **Small-Scale High-Quality GUI Instruction-Following Datasets** by careful data curation and employing a resampling strategy to address significant data type imbalances.  With the above components, our model, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding. Its UI-guided token selection further reduces 33% of redundant visual tokens during training and speeds up performance by 1.4x. Navigation experiments across web, mobile, and online environments further underscore the effectiveness and potential of our model in advancing GUI visual agents.
Let's Chorus: Partner-aware Hybrid Song-Driven 3D Head Animation
Authors: Xiumei Xie; Zikai Huang; Wenhao Xu; Peng Xiao; Xuemiao Xu; Huaidong Zhang
Venue: CVPR 2025 (main)

Abstract: Singing is a vital form of human emotional expression and social interaction, distinguished from speech by its richer emotional nuances and freer expressive style. Thus, investigating 3D facial animation driven by singing holds significant research value. Our work focuses on 3D singing facial animation driven by mixed singing audio, and to the best of our knowledge, no prior studies have explored this area. Additionally, the absence of existing 3D singing datasets poses a considerable challenge. To address this, we collect a novel audiovisual dataset, ChorusHead which features synchronized mixed vocal audio and pseudo-3D flame motions for chorus singing. In addition, We propose a partner-aware 3D chorus head generation framework driven by mixed audio inputs. The proposed framework extracts emotional features from the background music and dependence between singers and models the head movement in a latent space from the Variational Autoencoder (VAE), enabling diverse interactive head animation generation. Extensive experimental results demonstrate that our approach effectively generates 3D facial animations of interacting singers, achieving notable improvements in realism and handling background music interference with strong robustness.
Twinner: Shining Light on Digital Twins in a Few Snaps
Authors: Jesus Zarzar; Tom Monnier; Roman Shapovalov; Andrea Vedaldi; David Novotny
Venue: CVPR 2025 (main)

Abstract: We present the first large reconstruction model, Twinner, capable of recovering a scene's illumination as well as an object's geometry and material properties from only a few posed images. Twinner is based on the Large Reconstruction Model and innovates in three key ways:1) We introduce a memory-efficient voxel-grid transformer whose memory scales only quadratically with the size of the voxel grid. 2) To deal with scarcity of high-quality ground-truth PBR-shaded models, we introduce a large fully-synthetic dataset of procedurally-generated PBR-textured objects lit with varied illumination.3) To narrow the synthetic-to-real gap, we finetune the model on real life datasets by means of a differentiable physically-based shading model, eschewing the need for ground-truth illumination or material properties which are challenging to obtain in real life. We demonstrate the efficacy of our model on the real life StanfordORB benchmark where, given few input views, we achieve reconstruction quality significantly superior to existing feed-forward reconstruction networks, and comparable to significantly slower per-scene optimization methods.
Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
Authors: Jian Han; Jinlai Liu; Yi Jiang; Bin Yan; Yuqi Zhang; Zehuan Yuan; Bingyue Peng; Xiaobing Liu
Venue: CVPR 2025 (main)

Abstract: We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity refactors visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary classifier and bitwise self-correction mechanism. By theoretically expanding the tokenizer vocabulary size to infinity in Transformer, our method significantly unleashes powerful scaling capabilities to infinity compared to vanilla VAR. Extensive experiments indicate Infinity outperforms AutoRegressive Text-to-Image models by large margins, matches or surpasses leading diffusion models. Without extra optimization, Infinity generates a 1024x1024 image in 0.8s, 2.6xfaster than SD3-Medium, making it the fastest Text-to-Image model. Models and codes will be released to promote the further exploration of Infinity for visual generation.