GenAI4ELab/papercli-papers huggingface dataset:
Gravitation-Driven Semantic Alignment for Text Video Retrieval
Authors: Yi Yang; Zheng Wang; Xing Xu; Jingkuan Song; Heng Tao Shen
Venue: CVPR 2026 (main)

Abstract: The inherent semantic ambiguity of "many-to-many", where one video matches multiple texts and vice versa, aggravates the difficulty in text-video retrieval. The dominant deterministic embeddings only struggle to capture the mean semantics, while existing probabilistic methods fail to distinguish hard negatives for their imposing rigid uncertainty priors or ignoring the interaction between similarity and uncertainty. To this end, we propose a novel physics-inspired framework (GraviAlign) that decomposes the alignment of cross-modal semantic distributions into two orthogonal factors inspired by the Gravitational Force: (1) Semantic Attraction measuring gravitational alignment between distribution centers via uncertainty-derived "semantic mass" and "semantic distance"; (2) Geometric Overlap quantifying distribution intersection. Each factor has independent veto power to reject those matches with misalignment or poor overlap. Additionally, GraviAlign offers an efficient (O(D)), theoretically grounded alternative to intractable joint integrals. Extensive experiments on DiDeMo, MSR-VTT, and ActivityNet demonstrate our effectiveness and superiority, and solid ablation studies confirm the indispensability of two novel components.
Beyond Pixel Simulation: Pathology Image Generation via Diagnostic Semantic Tokens and Prototype Control
Authors: Minghao Han; Yichen Liu; Yizhou Liu; Zizhi Chen; Jingqun Tang; Xuecheng Wu; Dingkang Yang; Lihua Zhang
Venue: CVPR 2026 (main)

Abstract: In computational pathology, understanding and generation have evolved along disparate paths: advanced understanding models already exhibit diagnostic-level competence, whereas generative models largely simulate pixels. Progress remains hindered by three coupled factors: the scarcity of large, high-quality image-text corpora; the lack of precise, fine-grained semantic control, which forces reliance on non-semantic cues; and terminological heterogeneity, where diverse phrasings for the same diagnostic concept impede reliable text conditioning. We introduce UniPath, a semantics-driven pathology image generation framework that leverages mature diagnostic understanding to enable controllable generation. UniPath implements Multi-Stream Control: a Raw-Text stream; a High-Level Semantics stream that uses learnable queries to a frozen pathology MLLM to distill paraphrase-robust Diagnostic Semantic Tokens and to expand prompts into diagnosis-aware attribute bundles; and a Prototype stream that affords component-level morphological control via a prototype bank. On the data front, we curate a 2.65M image-text corpus and a finely annotated, high-quality 68K subset to alleviate data scarcity. For a comprehensive assessment, we establish a four-tier evaluation hierarchy tailored to pathology. Extensive experiments demonstrate UniPath's SOTA performance, including a Patho-FID of 80.9 (51% better than the second-best) and fine-grained semantic control achieving 98.7% of the real-image. The dataset and code can be obtained from https://github.com/Hanminghao/UniPath.
OmniGround: A Comprehensive Spatio-Temporal Grounding Benchmark for Real-World Complex Scenarios
Authors: Hong Gao; Jingyu Wu; Xiangkai Xu; Kangni Xie; Yunchen Zhang; Bin Zhong; Xurui Gao; Min-Ling Zhang
Venue: CVPR 2026 (main)

Abstract: Spatio-Temporal Video Grounding (STVG) aims to localize target objects in videos based on natural language descriptions. While Multimodal Large Language Models have shown promise, a significant gap remains between current models and real-world demands involving diverse objects and complex queries. We attribute this to limited benchmark scope, causing models to exhibit category bias, oversimplified reasoning, and poor linguistic robustness.To address these limitations, we introduce OmniGround, a comprehensive benchmark with 3,475 videos spanning 81 categories and complex real-world queries. We propose the Forward-Backward-Refinement (FBR) annotation pipeline for high-quality labels and DeepSTG, a systematic evaluation framework quantifying dataset quality beyond superficial statistics. Evaluations reveal performance average drops of 10.4% on complex real-world scenes, particularly with small/occluded objects and intricate spatial relations. Motivated by these, we propose PG-TAF, a training-free two-stage framework decomposing STVG into high-level temporal grounding and fine-grained spatio-temporal propagation. Experiments demonstrate PG-TAF achieves 25.6% and 35.6% improvements in m_tIoU and m_vIoU on OmniGround with consistent gains across four benchmarks.
GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
Authors: Aoran Xiao; Shihao Cheng; Yonghao Xu; Yexian Ren; Hongruixuan Chen; Naoto Yokoya
Venue: CVPR 2026 (main)

Abstract: Recent advances in multimodal large language models (MLLMs) have accelerated progress in domain-oriented AI, yet their development in geoscience and remote sensing (RS) remains constrained by distinctive challenges: wide-ranging disciplinary knowledge, heterogeneous sensor modalities, and a fragmented spectrum of tasks. To bridge these gaps, we introduce GeoMMBench, a comprehensive multimodal question-answering benchmark covering diverse RS disciplines, sensors, and tasks, enabling broader and more rigorous evaluation than prior benchmarks. Using GeoMMBench, we assess 36 open-source and proprietary large language models (LLMs), uncovering systematic deficiencies in domain knowledge, perceptual grounding, and reasoning--capabilities essential for expert-level geospatial interpretation. Beyond evaluation, we propose GeoMMAgent, a multi-agent framework that strategically integrates retrieval, perception, and reasoning through domain-specific RS models and tools. Extensive experimental results demonstrate that GeoMMAgent significantly outperforms standalone LLMs, underscoring the importance of tool-augmented agents for dynamically tackling complex geoscience and RS challenges.
FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control
Authors: Zhiyuan Zhang; Can Wang; Dongdong Chen; Jing Liao
Venue: CVPR 2026 (main)

Abstract: We present FlexTraj, a framework for image-to-video generation with flexible point trajectory control. FlexTraj introduces a unified point-based representation that encodes point with a temporally consistent trajectory ID, a segmentation ID, and an optional color channel for appearance cues, enabling both dense and sparse trajectory control. Instead of injecting trajectory conditions into the video generator through token concatenation or ControlNet, FlexTraj employs an efficient sequence-concatenation scheme that achieves faster convergence, stronger controllability, and efficient inference, while maintaining robustness under unaligned conditions. To train such a unified point trajectory-controlled video generator, FlexTraj adopts an annealing training strategy that gradually reduces reliance on complete supervision and aligned condition. Experimental results demonstrate that FlexTraj enables multi-granularity, alignment-agnostic trajectory control for video generation, supporting various applications such as motion cloning, drag-based image-to-video, motion interpolation, camera redirection, flexible action control and mesh animations.
Enhancing Accuracy of Uncertainty Estimation in Appearance-based Gaze Tracking with Probabilistic Evaluation and Calibration
Authors: Qiaojie Zheng; Jiucai Zhang; Amy Zhang; Xiaoli Zhang
Venue: CVPR 2026 (main)

Abstract: Accurate uncertainty estimation is essential for reliable appearance-based gaze tracking. However, domain shifts between training and testing often lead to incorrect uncertainty estimates, which is a problem overlooked in existing uncertainty-aware gaze tracking models. To overcome this problem efficiently, we formulate uncertainty estimation as a conditional distribution problem and treat the correction process as an output-level conditional distribution matching task. We therefore introduce a data-efficient post-hoc calibration method to align the predicted, high-error conditional distribution with the empirically observed distribution extracted from a small set of calibration samples. To more faithfully assess the accuracy of the resulting uncertainty estimates, we further introduce a new metric, Coverage Probability Error (CPE), to quantify the distribution-level mismatch between prediction and observation. We validate the calibration procedure across four domain shift scenarios to demonstrate improved uncertainty accuracy and its practical benefits.
Draft and Refine with Visual Experts
Authors: Sungheon Jeong; Ryozo Masukawa; Jihong Park; Sanggeon Yun; Wenjun Huang; Hanning Chen; Mahdi Imani; Mohsen Imani
Venue: CVPR 2026 (main)

Abstract: While recent Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning abilities, they often produce ungrounded, hallucinated responses by over-relying on linguistic priors rather than visual evidence. This critical limitation arises from the lack of a quantitative measure of how much these models actually rely on visual inputs during reasoning. We propose Draft and Refine (DnR), an agent framework driven by a novel question-conditioned utilization metric. This metric quantifies the model's actual reliance on visual evidence by first constructing a query-conditioned relevance map to localize question-specific evidence, and then assessing dependence through relevance-based probabilistic masking. Guided by this metric, the DnR agent refines its initial "draft" through targeted feedback from external visual experts. Each expert's output (e.g., boxes, masks) is rendered as visual cues on the image, and the LVLM is re-queried to select the response that yields the greatest improvement in utilization. This process strengthens visual grounding of predictions without retraining or architectural changes. Experiments across a broad range of VQA and captioning benchmarks demonstrate consistent accuracy gains and reduced hallucination. These results show that quantifying visual utilization provides a principled path for designing more interpretable and evidence-driven multimodal agent systems that effectively leverage visual experts.
Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement
Authors: Junrong Guo; Shancheng Fang; Yadong Qu; Hongtao Xie
Venue: CVPR 2026 (main)

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled automated generation of structured layouts from natural language descriptions. Existing methods typically follow a code-only paradigm that generates code to represent layouts, which are then rendered by graphic engines to produce final images. However, they are blind to the rendered visual outcome, making it difficult to guarantee readability and aesthetics. In this paper, we identify visual feedback as a critical factor in layout generation and propose Visual Feedback Layout Model (VFLM), a self-improving framework that leverages visual feedback iterative refinement. VFLM is capable of performing adaptive reflective generation, which leverages visual information to reflect on previous issues and iteratively generates outputs until satisfactory quality is achieved. It is achieved through reinforcement learning with a visually grounded reward model that incorporates OCR accuracy. By rewarding only the final generated outcome, we can effectively stimulate the model's iterative and reflective generative capabilities. Experiments across multiple benchmarks show that VFLM consistently outperforms advanced MLLMs, existing layout models, and code-only baselines, establishing visual feedback as critical for design-oriented MLLMs. Our code and data are available at https://github.com/FolSpark/VFLM.
Hier-COS: Making Deep Features Hierarchy-aware via Composition of Orthogonal Subspaces
Authors: Depanshu Sani; Saket Anand
Venue: CVPR 2026 (main)

Abstract: Traditional classifiers treat all class labels as mutually independent, thereby considering all negative classes to be equally incorrect. This approach fails severely in many real-world scenarios, where a known semantic hierarchy defines a partial order of preferences over negative classes. While hierarchy-aware feature representations have shown promise in mitigating this problem, their performance is typically assessed using metrics like Mistake Severity (MS) and Average Hierarchical Distance (AHD). In this paper, we highlight important shortcomings in existing hierarchical evaluation metrics, demonstrating that they are often incapable of measuring true hierarchical performance. Our analysis reveals that existing methods learn sub-optimal hierarchical representations, despite competitive MS and AHD scores. To counter these issues, we introduce Hierarchical Composition of Orthogonal Subspaces (Hier-COS), a novel framework for unified 'hierarchy-aware fine-grained' and 'hierarchical multi-label' classification. We show that Hier-COS is theoretically guaranteed to be consistent with the given hierarchy tree. Furthermore, our framework implicitly adapts the learning capacity for different classes based on their position within the hierarchy tree -- a vital property absent in existing methods. Finally, to address the limitations of evaluation metrics, we propose Hierarchically Ordered Preference Score (HOPS), a ranking-based metric that demonstrably overcomes the deficiencies of current evaluation standards. We benchmark Hier-COS on four challenging datasets, including the deep and imbalanced tieredImageNet-H (12-level) and iNaturalist-19 (7-level). Through extensive experiments, we demonstrate that Hier-COS achieves state-of-the-art performance across all hierarchical metrics for every dataset, while simultaneously beating the top-1 accuracy in all but one case. Lastly, we show that Hier-COS can effectively learn to transform the frozen features extracted from a pretrained backbone (ViT) to be hierarchy-aware, yielding substantial benefits for hierarchical classification performance.
Prototype-Guided Concept Erasure in Diffusion Models
Authors: Yuze Cai; Jiahao Lu; Hongxiang Shi; Yichao Zhou; Hong Lu
Venue: CVPR 2026 (main)

Abstract: Concept erasure is extensively utilized in image generation to prevent text-to-image models from generating undesired content. Existing methods can effectively erase narrow concepts that are specific and concrete, such as distinct intellectual properties (e.g. Pikachu) or recognizable characters (e.g. Elon Musk). However, their performance degrades on broad concepts such as "sexual" or "violent", whose wide scope and multi-faceted nature make them difficult to erase reliably.To overcome this limitation, we exploit the model's intrinsic embedding geometry to identify latent embeddings that encode a given concept. By clustering these embeddings, we derive a set of concept prototypes that summarize the model's internal representations of the concept, and employ them as negative conditioning signals during inference to achieve precise and reliable erasure. Extensive experiments across multiple benchmarks show that our approach achieves substantially more reliable removal of broad concepts while preserving overall image quality, marking a step towards safer and more controllable image generation.
DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation
Authors: Sankarshana Venugopal; Mohammad Mostafavi; Jonghyun Choi
Venue: CVPR 2026 (main)

Abstract: Diffusion-based image-to-image (I2I) translation excels in high-fidelity generation but suffers from slow sampling in state-of-the-art Diffusion Bridge Models (DBMs), often requiring dozens of function evaluations (NFEs). We introduce DBMSolver, a training-free sampler that exploits the semi-linear structure of DBM's underlying SDE and ODE via exponential integrators, yielding highly-efficient 1st- and 2nd-order solutions. This reduces NFEs by up to 5X while boosting quality (e.g., FID drops 53% on DIODE at 20 NFEs vs. 2nd-order baseline). Experiments on inpainting, stylization, and semantics-to-image tasks across resolutions up to 256x256 show DBMSolver sets new SOTA efficiency-quality tradeoffs, enabling real-world applicability. Our code is publicly available at https://github.com/snumprlab/dbmsolver.
UETrack: A Unified and Efficient Framework for Single Object Tracking
Authors: Ben Kang; Jie Zhao; Xin Chen; Wanting Geng; Bin Zhang; Lu Zhang; Dong Wang; Huchuan Lu
Venue: CVPR 2026 (main)

Abstract: With growing real-world demands, efficient tracking has received increasing attention. However, most existing methods are limited to RGB inputs and struggle in multi-modal scenarios. Moreover, current multi-modal tracking approaches typically use complex designs, making them too heavy and slow for resource-constrained deployment. To tackle these limitations, we propose UETrack, a unified and efficient framework for single object tracking. UETrack demonstrates high practicality and versatility, efficiently handling multiple modalities including RGB, Depth, Thermal, Event, and Language, and addresses the gap in efficient multi-modal tracking. It introduces two key components: a Token-Pooling-based Mixture-of-Experts mechanism that enhances modeling capacity through feature aggregation and expert specialization, and a Target-aware Adaptive Distillation strategy that selectively performs distillation based on sample characteristics, reducing redundant supervision and improving performance. Extensive experiments on 12 benchmarks across 3 hardware platforms show that UETrack achieves a superior speed-accuracy trade-off compared to pervious methods. For instance, UETrack-B achieves 69.2% AUC on LaSOT and runs at 163/56/60 FPS on GPU/CPU/AGX, demonstrating strong practicality and versatility. Code is available at https://github.com/kangben258/UETrack.
CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion
Authors: Yushan Han; Hui Zhang; Qiming Xia; Yi Jin; Yidong Li
Venue: CVPR 2026 (main)

Abstract: Collaborative perception empowers autonomous agents to share complementary information and overcome perception limitations. While early fusion offers more perceptual complementarity and is inherently robust to model heterogeneity, its high communication cost has limited its practical deployment, prompting most existing works to favor intermediate or late fusion. To address this, we propose a communication-efficient early collaborative perception framework that incorporates LiDAR completion to restore scene completeness under sparse transmission, dubbed as CoLC. Specifically, the CoLC integrates three complementary designs. First, each neighbor agent applies Foreground-Aware Point Sampling (FAPS) to selectively transmit informative points that retain essential structural and contextual cues under bandwidth constraints. The ego agent then employs Completion-Enhanced Early Fusion (CEEF) to reconstruct dense pillars from the received sparse inputs and adaptively fuse them with its own observations, thereby restoring spatial completeness. Finally, the Dense-Guided Dual Alignment (DGDA) strategy enforces semantic and geometric consistency between the enhanced and dense pillars during training, ensuring consistent and robust feature learning. Experiments on both simulated and real-world datasets demonstrate that CoLC achieves superior perception-communication trade-offs and remains robust under heterogeneous model settings.
Mixture of Style Experts for Diverse Image Stylization
Authors: Shihao Zhu; Ziheng Ouyang; Yijia Kang; Qilong Wang; Mi Zhou; Bo Li; Ming-Ming Cheng; Qibin Hou
Venue: CVPR 2026 (main)

Abstract: Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details. We introduce StyleExpert, a semantic-aware framework based on Mixture of Experts (MoE).Our framework employs a unified style encoder, trained on our large-scale dataset of content-style-stylized triplets, to embed diverse styles into a consistent latent space. This embedding is then used to condition a similarity-aware gating mechanism, which dynamically routes styles to specialized experts within the MoE architecture. Leveraging this MoE architecture, our method adeptly handles diverse styles spanning multiple semantic levels, from shallow textures to deep semantics. Extensive experiments show that StyleExpert outperforms existing approaches in preserving semantics and material details, while generalizing to unseen styles.
Consistent Instance Field for Dynamic Scene Understanding
Authors: Junyi Wu; Van Nguyen Nguyen; Benjamin Planche; Jiachen Tao; Changchang Sun; Zhongpai Gao; Zhenghao Zhao; Anwesa Choudhuri; Gengyu Zhang; Meng Zheng; Feiran Wang; Terrence Chen; Yan Yan; Ziyan Wu
Venue: CVPR 2026 (main)

Abstract: We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution. To realize this, we introduce a novel instance-embedded representation based on deformable 3D Gaussians, which jointly encode radiance and semantic information and are learned directly from input RGB images and instance masks through differentiable rasterization.Furthermore, we introduce new mechanisms to calibrate per-Gaussian identities and resample Gaussians toward semantically active regions, ensuring consistent instance representations across space and time. Experiments on HyperNeRF and Neu3D datasets demonstrate that our method significantly outperforms state-of-the-art methods on novel-view panoptic segmentation and open-vocabulary 4D querying tasks.
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
Authors: Alexandru Brateanu; Tingting Mu; Codruta O. Ancuti; Cosmin Ancuti
Venue: CVPR 2026 (main)

Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE techniques often rely on large models and multi-stage training, limiting practicality for edge deployment. Moreover, their dependence on a single color space introduces instability and visible exposure or color artifacts.To address these, we propose Multinex, an ultra-lightweight structured framework that integrates multiple fine-grained representations within a principled Retinex residual formulation. It decomposes an image into illumination and color prior stacks derived from distinct analytic representations, and learns to fuse these representations into luminance and reflectance adjustments required to correct exposure. By prioritizing enhancement over reconstruction and exploiting lightweight neural operations, Multinex significantly reduces computational cost, exemplified by its lightweight (45K parameters) and nano (0.7K parameters) versions. Extensive benchmarks show that all lightweight variants significantly outperform their corresponding lightweight SOTA models, and reach comparable performance to heavy models. Paper page available at https://albrateanu.github.io/multinex.