GenAI4ELab/papercli-papers huggingface dataset:
ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding
Authors: Shuo Cao; Nan Ma; Jiayang Li; Xiaohui Li; Lihao Shao; Kaiwen Zhu; Yu Zhou; Yuandong Pu; Jiarui Wu; Jiaquan Wang; Bo Qu; Wenhai Wang; Yu Qiao; Dajuin Yao; Yihao Liu
Venue: CVPR 2026 (main)

Abstract: The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present: (1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated IAA dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public.
Unifying Precise Keyframes and Semantic Control via Multi-level Diffusion
Authors: Linjun Wu; Jiejia Yu; Leyang Jin; He Wang; Bowen Zheng; Xu Yang; Hao Jiang; Fei Xia; Fei Ling; Jun Deng; Xiaogang Jin
Venue: CVPR 2026 (main)

Abstract: Text-conditioned human motion in-betweening leverages keyframes for spatio-temporal control, with text providing high-level semantic guidance for the transitions. However, existing methods are unable to establish a coherent alignment between textual semantics and the spatio-temporal constraints provided by keyframes, often resulting in insufficiently constrained motions with unintended behavior. Moreover, they struggle with precise spatial control, often generating motions that deviate from keyframe constraints. To address these issues, we propose a multi-level diffusion framework that integrates textual semantics with implicit cues from keyframe sequences to modulate global motion dynamics, while leveraging individual keyframes to guide local transitions around them. During inference, to ensure strict keyframe adherence, we propose a novel trajectory refinement strategy that adjusts the root positions of the generated motion, followed by diffusion imputation to refine the poses of the generated keyframes. Additionally, our framework enables semantics-preserving motion editing, allowing for plausible modifications while retaining the original motion semantics. Extensive experiments demonstrate that our method generates high-quality motions that strictly satisfy keyframe constraints while achieving precise semantic alignment.
Improving Sparse Autoencoder with Dynamic Attention
Authors: Dongsheng Wang; Jinsen Zhang; Dawei Su; Hui Huang
Venue: CVPR 2026 (main)

Abstract: Recently, sparse autoencoders (SAEs) have emerged as a promising technique for interpreting activations in foundation models by disentangling features into a sparse set of concepts. However, identifying the optimal level of sparsity for each neuron remains challenging in practice: excessive sparsity might lead to poor reconstruction, whereas insufficient sparsity harms interpretability. While existing activation functions such as ReLU and TopK provide certain sparsity guarantees, they typically require additional sparsity regularization or cherry-picked hyperparameters. We show in this paper that adaptive sparse attention mechanisms using sparsemax can bridge this trade-off, due to their ability to determine the number of concepts in a data-dependent manner.Specifically, we first explore a new class of SAEs based on the cross-attention architecture with the latent features as queries and the learnable dictionary as the key and value matrices. To encourage sparse pattern learning, we employ a sparsemax-based attention strategy that automatically infers a sparse set of concepts according to the complexity of each neuron, resulting in a more flexible and efficient activation function. Through comprehensive evaluation and visualization, we show that our approach successfully achieves lower reconstruction loss while producing high-quality concepts. Moreover, the sparsity level automatically determined by our approach can serve as tuning guidance to improve existing SAEs. The code is available at https://github.com/qyj-bkjx/Sparsemax-SAE.
SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
Authors: Qianxun Xu; Chenxi Song; Yujun Cai; Chi Zhang
Venue: CVPR 2026 (main)

Abstract: Recent advances in text-to-video diffusion models have enabled high-fidelity and temporally coherent video synthesis. However, current models are predominantly optimized for single-event generation. When handling multi-event prompts, without explicit temporal grounding, such models often produce blended or collapsed scenes that break the intended narrative. To address this limitation, we present SwitchCraft, a training-free framework for multi-event video generation. Our key insight is that uniform prompt injection across time ignores the correspondence between events and frames. To this end, we introduce Event-Aligned Query Steering (EAQS), which steers frame-level attention to align with relevant event prompts. Furthermore, we propose Auto-Balance Strength Solver (ABSS), which adaptively balances steering strength to preserve temporal consistency and visual fidelity. Extensive experiments demonstrate that SwitchCraft substantially improves prompt alignment, event clarity, and scene consistency compared with existing baselines, offering a simple yet effective solution for multi-event video generation.
DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution
Authors: Zhengyao Lv; Menghan Xia; Xintao Wang; Kwan-Yee K. Wong
Venue: CVPR 2026 (main)

Abstract: Diffusion-based video super-resolution (VSR) achieves remarkable fidelity but suffers from prohibitive sampling cost. While distribution matching distillation (DMD) accelerates diffusion models to one-step generation, directly applying it to VSR leads to training instability and degraded, insufficient supervision.To address these issues, we propose DUO-VSR, a three-stage framework centered on a DUal-Stream Distillation strategy that integrates distribution matching and adversarial supervision for One-step VSR.We first adopt a Progressive Guided Distillation Initialization to stabilize subsequent training through trajectory-preserving distillation.We then introduce a Dual-Stream Distillation Strategy to jointly optimize DMD and Real-Fake Score Feature GAN (RFS-GAN) streams, with the latter providing complementary adversarial supervision using features from both real and fake score models.Finally, a Preference-Guided Refinement aligns the student with perceptual quality preferences.Comprehensive experiments demonstrate that DUO-VSR achieves superior visual quality and efficiency over previous one-step VSR methods.
HiFICL: High-Fidelity In-Context Learning for Multimodal Tasks
Authors: Xiaoyu Li; Yuhang Liu; Xuanshuo Kang; Zheng Luo; Fangqi Lou; Xiaohua Wu; Zihan Xiong
Venue: CVPR 2026 (main)

Abstract: In-Context Learning (ICL) is a significant paradigm for Large Multimodal Models (LMMs), using a few in-context demonstrations (ICDs) for new task adaptation. However, its performance is sensitive to demonstration configurations and computationally expensive. Mathematically, the influence of these demonstrations can be decomposed into a dynamic mixture of the standard attention output and the context values. Current approximation methods simplify this process by learning a "shift vector". Inspired by the exact decomposition, we introduce High-Fidelity In-Context Learning (HiFICL) to more faithfully model the ICL mechanism. HiFICL consists of three key components: 1) a set of "virtual key-value pairs" to act as a learnable context, 2) a low-rank factorization for stable and regularized training, and 3) a simple end-to-end training objective. From another perspective, this mechanism constitutes a form of context-aware Parameter-Efficient Fine-Tuning (PEFT). Extensive experiments show that HiFICL consistently outperforms existing approximation methods on several multimodal benchmarks.
Hear you are: Teaching LLMs Spatial Reasoning with Vision and Spatial Sound
Authors: Hyeonggon Ryu; Joon Son Chung; David Harwath
Venue: CVPR 2026 (main)

Abstract: Many audio-visual learning methods have focused on aligning audio and visual information, either through semantic or temporal correspondence. However, most of these works have utilized monaural audio, which does not contain information about the spatial location of the sound source. In contrast, humans and other animals utilize binaural hearing to perceive this spatial information. Combining spatial sound and visual perception enables powerful high-level reasoning: for example, a person looking for their phone may hear the ringing sound coming from a backpack sitting on a table, and quickly infer that the missing phone is inside the backpack. In this paper, we investigate the problem of Audio-Visual Spatial Reasoning. We design a spatial audio-visual question answering dataset to cover scenarios where semantic correspondence between audio and visual signals is absent but spatial alignment exists, as well as cases with multiple audio-visual semantic correspondences that require spatial reasoning to disambiguate. We propose a model that learns spatial comprehension across the audio and vision modalities by connecting them with a large language model and experimentally demonstrate that spatial sound perception is an essential part of our task.
BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection
Authors: Melissa Schween; Mathis Kruse; Bodo Rosenhahn
Venue: CVPR 2026 (main)

Abstract: We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generated from images. Our work follows a multimodal approach, embedding object and relationship tokens from scene graphs with a language model to leverage semantic knowledge from the real world. A normalizing flow model is used to learn bijective transformations that map object-relation-object triplets from scene graphs to a simple base distribution (typically Gaussian), allowing anomaly detection through likelihood estimation. We evaluate our approach on the SARD dataset containing office and dining room scenes. Our method achieves around 10% better AUROC results compared to the current state-of-the-art model, while simultaneously being five times faster. Through ablation studies, we demonstrate superior robustness and universality, particularly regarding the use of synonyms, with our model maintaining stable performance while the baseline shows 17.5% deviation. This work demonstrates the strong potential of learning-based methods for relationship anomaly detection in scene graphs. Our code is available at https://github.com/mschween/BUSSARD.
Energy Waveify and Redistribution for Test-Time Adaptation: A Control System Perspective
Authors: Zhenbin Wang; Lei Zhang; Lituan Wang; Zhenwei Zhang; Guangwu Qian; Yan Wang; Wei Huang
Venue: CVPR 2026 (main)

Abstract: This work tackles a key challenge in test-time energy adaptation: prohibitive time overhead arising from recent state-of-the-art test-time adaptation (TTA) methods, which are built on energy models relying on iterative Monte Carlo or Langevin dynamics sampling with multiple stochastic updates per test instance to approximate energy gradients. We tackle the problem from an innovative control system perspective by i) describing the energy as a complex-valued wave, where the amplitude encodes energy uncertainty and the phase characterizes its evolution, and ii) maintaining a time-dependent wave equation that interprets TTA as a control system evolution process. By enforcing the control system law of probability current conservation, our method directs probability current away from high-energy (error-prone) regions toward low-energy (accurate) ones, achieving adaptive energy redistribution without additional stochastic sampling while preserving the overall normalization of the energy landscape. Experimentally, the proposed method significantly outperforms baseline methods across several public benchmark datasets, with adaptive time being only 1/3   1/7 of that required by the compared Top-1 to Top-3 baselines. Code is available at https://github.com/wongzbb/APT.
Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation
Authors: Shubhankar Borse; Phuc Pham; Farzad Farhadzadeh; Seokeon Choi; Phong Nguyen; Anh Tran; Sungrack Yun; Munawar Hayat; Fatih Porikli
Venue: CVPR 2026 (main)

Abstract: Despite recent advances in personalized image generation, existing models consistently fail to produce reliable multi-human scenes, often merging or losing facial identity. We present Ar2Can, a novel two-stage framework that disentangles spatial planning from identity rendering for multi-human generation. The Architect predicts structured layouts, specifying where each person should appear. The Artist then synthesizes photorealistic images, guided by a spatially-grounded face matching reward that combines Hungarian spatial alignment with identity similarity. This approach ensures faces are rendered at correct locations and faithfully preserve reference identities. We develop two Architect variants, seamlessly integrated with our diffusion-based Artist model. This is optimized via Group Relative Policy Optimization (GRPO) using compositional rewards for count accuracy, image quality, and identity matching. Evaluated on the MultiHuman-Testbench, Ar2Can achieves substantial improvements in both count accuracy and identity preservation, while maintaining high perceptual quality. Notably, our method achieves these results using primarily synthetic data, without requiring real multi-human images. Project page: https://qualcomm-ai-research.github.io/ar2can/
OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
Authors: Henry Herzog; Favyen Bastani; Yawen Zhang; Gabriel Tseng; Joseph Redmon; Hadrien Sablon; Ryan Park; Jacob Morrison; Alexandra Buraczynski; Karen Farley; Josh Hansen; Andrew Howe; Patrick Alan Johnson; Mark Otterlee; Ted Schmitt; Hunter Pitelka; Stephen Daspit; Rachel Ratner; Christopher Wilhelm; Sebastian Wood; Mike Jacobi; Hannah Kerner; Evan Shelhamer; Ali Farhadi; Ranjay Krishna; Patrick Beukema
Venue: CVPR 2026 (main)

Abstract: Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present Helios: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. Helios achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings Helios achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy Helios as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The Helios platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. Helios source code, training data, and pre-trained weights are available at REDACTED.
InfiniBench: Infinite Benchmarking for Visual Spatial Reasoning with Customizable Scene Complexity
Authors: Haoming Wang; Qiyao Xue; Wei Gao
Venue: CVPR 2026 (main)

Abstract: Modern vision-language models (VLMs) are expected to have abilities of spatial reasoning with diverse scene complexities, but evaluating such abilities is difficult due to the lack of benchmarks that are not only diverse and scalable but also fully customizable. Existing benchmarks offer limited customizability over the scene complexity and are incapable of isolating and analyzing specific VLM failure modes under distinct spatial conditions. To address this gap, instead of individually presenting benchmarks for different scene complexities, in this paper we present InfiniBench, a fully automated, customizable and user-friendly benchmark generator that can synthesize a theoretically infinite variety of 3D scenes with parameterized control on scene complexity. InfiniBench uniquely translates scene descriptions in natural language into photo-realistic videos with complex and physically plausible 3D layouts. This is achieved through three key innovations: 1) a LLM-based agentic framework that iteratively refines procedural scene constraints from scene descriptions; 2) a flexible cluster-based layout optimizer that generates dense and cluttered scenes previously intractable for procedural methods; and 3) a task-aware camera trajectory optimization method that renders scenes into videos with full object coverage as VLM input. Experiments demonstrate that InfiniBench outperforms state-of-the-art procedural and LLM-based 3D generation methods in prompt fidelity and physical plausibility, especially in high-complexity scenarios. We further showcased the usefulness of InfiniBench, by generating benchmarks for representative spatial reasoning tasks including measurement, perspective-taking and spatiotemporal tracking. The source codes of InfiniBench are available at https://github.com/pittisl/infinibench, and some sample benchmarks being generated by InfiniBench are available at https://huggingface.co/datasets/Haoming645/infinibench.
DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces
Authors: Li Zhang; Mingyu Mei; Ailing Wang; Xianhui Meng; Yan Zhong; Xinyuan Song; Liu Liu; Rujing Wang; Zaixing He; Cewu Lu
Venue: CVPR 2026 (main)

Abstract: Articulated object pose estimation is a core task in embodied AI and computer vision. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this paper, we introduce DICArt (DIsCrete Diffusion for Articulated Object Pose Estimation), a novel framework that formulates pose estimation as a conditional discrete diffusion process. Instead of operating in a continuous domain, DICArt progressively denoises a noisy pose representation through a learned reverse diffusion procedure to recover the ground-truth pose.To improve modeling fidelity, we propose a flexible flow decider that dynamically determines whether each token should be denoised or reset, effectively balancing the real and noise distributions during diffusion. Additionally, we incorporate a hierarchical kinematic coupling strategy, estimating the pose of each rigid part hierarchically to respect the object's kinematic structure.We validate DICArt on both synthetic and real-world datasets with multi-hinged articulated objects. Experimental results demonstrate its superior performance and robustness over state-of-the-art baselines. By integrating discrete generative modeling with structural priors, DICArt offers a new paradigm for reliable category-level 6D pose estimation in complex environments. Codewill be publicly available upon acceptance.
Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection
Authors: Ruichao Yang; Wei Gao; Xiaobin Zhu; Jing Ma; Hongzhan Lin; Ziyang Luo; Bo-Wen Zhang; Xu-Cheng Yin
Venue: CVPR 2026 (main)

Abstract: Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We present Probabilistic Concept Graph Reasoning (PCGR), an interpretable, modular, and evolvable framework that reframes multimodal misinformation detection (MMD) as structured, concept-based reasoning. PCGR follows a build-then-infer paradigm, which first constructs a graph of human-understandable concept nodes, including novel high-level concepts automatically discovered and validated by multimodal large language models (MLLMs), and then applies hierarchical attention over this concept graph to infer claim veracity. This design produces interpretable reasoning chains linking evidence to conclusions. Experiments demonstrate that PCGR achieves state-of-the-art MMD accuracy and robustness to emerging manipulation types, outperforming prior methods in both coarse detection and fine-grained manipulation recognition.
AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Affordance Correspondence
Authors: Jiawei Zhang; Kaizhe Hu; Yingqian Huang; Yuanchen Ju; Zhengrong Xue; Huazhe Xu
Venue: CVPR 2026 (main)

Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity. Leveraging powerful 3D generative models and vision foundation models (VFMs), the proposed AffordGen framework overcomes this limitation by utilizing the semantic  correspondence of meaningful keypoints across large-scale 3D meshes to generate new robot manipulation trajectories. This large-scale, affordance-aware dataset is then used to train a robust, closed-loop visuomotor policy, combining the semantic generalizability of affordances with the reactive robustness of end-to-end learning. Experiments in simulation and the real world show that policies traine with AffordGen achieve high success rates and enable zero-shot generalization to truly unseen objects, significantly improving data efficiency in robot learning.
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
Authors: Zhihao Peng; Cheng Wang; Shengyuan Liu; Zhiying Liang; Zanting Ye; Min Jie Ju; Peter YM Woo; Yixuan Yuan
Venue: CVPR 2026 (main)

Abstract: Brain imaging analysis is crucial for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly supporting it. However, current brain imaging visual question-answering (VQA) benchmarks either cover a limited number of imaging modalities or are restricted to coarse-grained pathological descriptions, hindering a comprehensive assessment of MLLMs across the full clinical continuum. To address these, we introduce OmniBrainBench, the first comprehensive multimodal VQA benchmark specifically designed to assess the multimodal comprehension capabilities of MLLMs in brain imaging analysis with closed- and open-ended evaluations. OmniBrainBench comprises 15 distinct brain imaging modalities collected from 30 verified medical sources, yielding 9,527 validated VQA pairs and 31,706 images. It simulates clinical workflows and encompasses 15 multi-stage clinical tasks rigorously validated by a professional radiologist. Evaluations of 24 state-of-the-art models, including open-source general-purpose, medical, and proprietary MLLMs, highlight the substantial challenges posed by OmniBrainBench. Experiments reveal that proprietary MLLMs like Gemini-2.5-Pro (66.58%) outperform open-source and medical MLLMs yet lag far behind physicians (91.35%), while medical MLLMs show wide variance in closed- and open-ended VQA. Open-source general-purpose MLLMs generally trail but excel in specific tasks, and all MLLMs fall short in complex preoperative reasoning, revealing a critical visual-to-clinical gap. OmniBrainBench establishes a new standard to assess MLLMs in brain imaging analysis, highlighting the gaps against physicians. We publicly release our benchmark and code at link.