GenAI4ELab/papercli-papers huggingface dataset:
RECS4R: Bridging Semantics and Geometry for Referring Remote Sensing Interpretation
Authors: Jinming Chai; Lingling Li; Licheng Jiao; Xiaoqiang Lu; Long Sun; Xu Liu; Wenping Ma; Weibin Li
Venue: CVPR 2026 (main)

Abstract: Referring expression comprehension and segmentation (RECS) task plays a vital role in remote sensing due to its high efficiency in multi-tasking. However, RECS has reached a performance bottleneck rooted in representational insufficiency, primarily due to cross-task representational fragmentation in multi-task interpretation. In this paper, we propose RECS4R, a unified multi-task framework to upgrade RECS performance. At the representation level, we introduce a language-guided unified contour decoding paradigm (LUCDP), using language-conditioned contours as intermediate carriers to synchronously decode VG and RIS, structurally preserving geometric and semantic consistency while enabling lightweight, efficient decoding. At refinement level, we introduce residual coarse-to-fine encoding (RCE), shifting fine stage from learning-from-scratch to error correction. At reaggregation level, we design channel isolated multi-scale fusion (CIMF) to achieve lossless feature fusion between channels. At regularization level, we employ gradient consistency loss (GCL) to enhance LUCDP and improve boundary adherence. Moreover, we validate RECS4R on remote-sensing and natural datasets, including RefDIOR, RRSIS-D, OPT-RSVG, RefCOCO, RefCOCO+, and RefCOCOg, and verify the image encoder under CNN, Transformer, and Mamba backbones, achieving advanced performance. The code will be publicly accessible at https://github.com/IPIU-XDU/RSFM.
LogCD: Local-to-global Consistency Distillation for Few-step Image Generation
Authors: Qingsong Xie; Zhenyi Liao; Chen Chen; Zhijie Deng; Haonan Lu
Venue: CVPR 2026 (main)

Abstract: Distilling latent diffusion models (LDMs)/rectified flow models (RFMs) into ones that are fast to sample from conditions is attracting huge interest. However, the majority of existing methods either need significant training resources or lead to quality degradation, especially in text-image alignment. To address these challenges, we propose Local-to-global Consistency Distillation (LogCD) to accelerate LDMs/RFMs via two-stage distillation. LogCD first performs local consistency distillation and then executes global consistency distillation to ensure the consistency along inference path. Besides, Latent Learned Perceptual Image Patch Similarity model is exploited to enhance perceptual consistency. Notably, LogCD exhibits high flexibility, allowing a single unified model to operate with 2 to 4 sampling steps. The model's performance improves seamlessly as the number of steps increases within this range. With only 70 A100 GPU hours, LogCD accelerates SDXL to achieve a 33.5 CLIP score with just 3 sampling steps, surpassing state-of-the-art accelerated models using even more steps.
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Authors: Wenliang Zhong; Rob Barton; Lucas Goncalves; Kushal Kumar; Feng Jiang; Hehuan Ma; Yuzhi Guo; Vidit Bansal; Karim Bouyarmane; Junzhou Huang
Venue: CVPR 2026 (main)

Abstract: Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the first universal framework that bridges these gaps through textual guidelines. To incorporate complex guidelines without task-specific training, we propose Generative Concept Proxy Modeling, which generates guideline-aware embeddings via concept proxy extraction. For scenarios requiring automatic cluster discovery, we introduce LLM Traversal based on Minimum Spanning Tree that selectively applies LLM reasoning for complex semantic judgments. Our method generalizes across diverse clustering scenarios spanning from general to fine-grained categorization, from global to local criteria, and from balanced to long-tail distributions. Our framework consistently outperforms specialized methods across diverse clustering tasks.
EW-DETR: Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer
Authors: Munish Monga; Vishal Chudasama; Pankaj Wasnik; C.V. Jawahar
Venue: CVPR 2026 (main)

Abstract: Real-world object detection must operate in evolving environments where new classes emerge, domains shift, and unseen objects must be identified as unknown--all without accessing prior data. We introduce Evolving World Object Detection (EWOD), a paradigm coupling incremental learning, domain adaptation, and unknown detection under exemplar-free constraints. To tackle EWOD, we propose EW-DETR framework that augments DETR-based detectors with three synergistic modules: Incremental LoRA Adapters for exemplar-free incremental learning under evolving domains; a Query-Norm Objectness Adapter that decouples objectness-aware features from DETR decoder queries; and Entropy-Aware Unknown Mixing for calibrated unknown detection. This framework generalises across DETR-based detectors, enabling state-of-the-art RF-DETR to operate effectively in evolving-world settings. We also introduce FOGS (Forgetting, Openness, Generalisation Score) to holistically evaluate performance across these dimensions. Extensive experiments on Pascal Series and Diverse Weather benchmarks show EW-DETR outperforms other methods, improving FOGS by 57.24%.
GOR-IS: 3D Gaussian Object Removal In the Intrinsic Space
Authors: Yonghao Zhao; Yupeng Gao; Jian Yang; Jin Xie; Beibei Wang
Venue: CVPR 2026 (main)

Abstract: Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-view images. Removing objects from such 3D representations is a fundamental editing task that requires complete and seamless inpainting of occluded regions, ensuring consistency in geometry and appearance. Although existing methods have made notable progress in improving inpainting consistency, they often neglect global lighting effects, leading to physically implausible results. Moreover, these methods struggle with view-dependent non-Lambertian surfaces, where appearance varies across viewpoints, leading to unreliable inpainting. In this paper, we present 3D Gaussian Object Removal in the Intrinsic Space (GOR-IS), a novel framework for physically consistent and visually coherent 3D object removal. Our approach decomposes the scene into intrinsic components and explicitly models light transport to maintain global lighting effects consistency. Furthermore, we introduce an intrinsic-space inpainting module that operates directly in the material and lighting domains, effectively addressing the challenges posed by non-Lambertian surfaces. Extensive experiments on both synthetic and real-world datasets demonstrate that our framework substantially improves the physical consistency and visual coherence of object removal, outperforming existing methods by 13% in perceptual similarity (LPIPS) and 2dB in peak signal-to-noise ratio (PSNR). Code is publicly available at https://applezyh.github.io/GOR-IS-project-page/
AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation
Authors: Sharath Girish; Viacheslav Ivanov; Tsai-Shien Chen; Hao Chen; Aliaksandr Siarohin; Sergey Tulyakov
Venue: CVPR 2026 (main)

Abstract: Recent advances in subject-driven video generation with large diffusion models have enabled personalized content synthesis conditioned on user-provided subjects. However, existing methods lack fine-grained temporal control over subject appearance and disappearance, which are essential for applications such as compositional video synthesis, storyboarding, and controllable animation. We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation. Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities, while seamlessly integrating with the pretrained video generation model positional embeddings. Additionally, we incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation. Through token-wise concatenation, AlcheMinT avoids any additional cross-attention modules and incurs negligible parameter overhead. We establish a benchmark evaluating multiple subject identity preservation, video fidelity, and temporal adherence. Experimental results demonstrate that AlcheMinT achieves visual quality matching state-of-the-art video personalization methods, while, for the first time, enabling precise temporal control over multi-subject generation within videos.
Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models
Authors: Gengwei Zhang; Jie Peng; Zhen Tan; Mufan Qiu; Hossein Nourkhiz Mahjoub; Vaishnav Tadiparthi; Kwonjoon Lee; Yanyong Zhang; Tianlong Chen
Venue: CVPR 2026 (main)

Abstract: The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to enhance their visual reasoning capabilities. Although many studies have reported improved performance, it remains unclear whether RL training truly enables models to learn from visual information. In this work, we propose the Hallucination-as-Cue Framework, an analytical framework designed to investigate the effects of RL-based post-training on multimodal reasoning models from the perspective of model hallucination. Specifically, we introduce hallucination-inductive, modality-specific corruptions that remove or replace essential information required to derive correct answers, thereby forcing the model to reason by hallucination. By applying these corruptions during both training and evaluation, our framework provides a unique perspective for diagnosing RL training dynamics and understanding the intrinsic properties of datasets. Through extensive experiments and analyses across multiple multimodal reasoning benchmarks, we reveal that the role of model hallucination for RL-training is more significant than previously recognized. For instance, we find that RL post-training under purely hallucination-inductive settings can still significantly improve models' reasoning performance, and in some cases even outperform standard training. These findings challenge prevailing assumptions about MLLM reasoning training and motivate the development of more modality-aware RL-based training designs.
QuietPrune: Query-Guided Early Token Pruning for Vision-Language Models
Authors: Tianxiao Gao; Shanwei Zhao; Shuo Fang; Shiai Zhu; Chenguang Ma
Venue: CVPR 2026 (main)

Abstract: Vision-language models (VLMs) demonstrate powerful capabilities in multimodal tasks. However, the large number of visual tokens imposes a significant computational cost. In this paper, we propose QuietPrune, a QUery-guIded Early Token Pruning method to remove redundant visual tokens within VLMs, thereby enhancing computational efficiency. Unlike previous late pruning methods, we recognize that implementing early pruning within the vision transformer (ViT) can achieve benefits in both latency reduction and accuracy maintenance. To address the semantic loss problem in early pruning, we design a lightweight adapter by performing an inverse transformation of the projector in VLMs. The proposed adapter converts the contextual query into a visual domain [Q-CLS] (Query [CLS]) token, providing textual guidance for ViT pruning. During pruning, we further introduce a semi-structured pruning scheme based on visual-textual relevance. Specifically, we group spatially adjacent 2 x2 tokens to accommodate the visual token merging operation prevalent in mainstream VLMs. We use the mean attention scores between the [Q-CLS] token and the visual tokens as the relevance metric for each group, avoiding additional computation. Pruning is then applied at the group level based on the relevance score, preserving positional continuity. After pruning, we aggregate the redundant tokens into a single token to maintain context cues. Our method achieves up to 19.0% reduction in prefill latency while outperforming 4.2% in accuracy on the recent Qwen3-VL and InternVL3 series compared to existing late pruning methods.
b-CLIP: Text-Conditioned Contrastive Learning for Multi-Granular Vision-Language Alignment
Authors: Fatimah Zohra; Chen Zhao; Hani Itani; Bernard Ghanem
Venue: CVPR 2026 (main)

Abstract: CLIP achieves strong zero-shot image-text retrieval by aligning global vision and text representations, yet it falls behind on fine-grained tasks even when fine-tuned on long, detailed captions. In this work, we propose b-CLIP, a multi-granular text-conditioned contrastive learning framework designed to achieve hierarchical alignment between multiple textual granularities--from full captions to sentences and phrases--and their corresponding visual regions. For each level of granularity, b-CLIP uses cross-attention to dynamically pool image patches into contextualized visual embeddings. To address the semantic overlap inherent in this hierarchy, we introduce the b-Contextualized Contrastive Alignment Loss (b-CAL), which parameterizes the trade-off between query-specific matching and intra-image contextualization under both Cross-Entropy and Binary Cross-Entropy formulations. Through extensive experiments, we demonstrate that b-CLIP significantly improves dense alignment, achieving 91.8% T2I 92.3% I2T at R@1 on Urban1K and 30.9% on FG-OVD (Hard), setting state-of-the-art among methods trained without hard negatives. b-CLIP establishes a robust, adaptive baseline for dense vision-language correspondence. The code and models are released at \href https://github.com/fzohra/B-CLIP  https://github.com/fzohra/B-CLIP .
IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion
Authors: Lizhou Lin; Songpengcheng Xia; Zengyuan Lai; Lan Sun; Jiarui Yang; Ling Pei
Venue: CVPR 2026 (main)

Abstract: Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-of-sight requirements, but existing IMU-based motion capture assumes an isolated human and ignores object contacts and dynamics. To bridge this gap, we present IMU-HOI, a novel framework that jointly recovers full-body human pose and 6-DoF object trajectory from sparse IMUs on the body and object, explicitly modeling human-object Interaction.Our approach first infers probabilistic hand-object contacts directly from IMU streams and uses them as a high-level signal to route between kinematic and inertial reasoning. These contact cues drive a three-stage fusion pipeline that refines human pose and root translation, and fuses hand-based forward kinematics with object-IMU integration for object motion, yielding coherent, drift-resilient trajectories for both human and object. Experiments on challenging human-object interaction scenarios demonstrate substantial accuracy gains over prior inertial motion capture methods. Moreover, IMU-HOI can be plugged into existing sparse-IMU mocap backbones with minimal changes, effectively extending the scope of purely inertial motion capture from isolated humans to full human-object interaction and joint motion estimation.
From Sketch to Fresco: Efficient Diffusion Transformer with Progressive Resolution
Authors: Shikang Zheng; Guantao Chen; Landis He; Jiacheng Liu; Yuqi Lin; Chang Zou; Linfeng Zhang
Venue: CVPR 2026 (main)

Abstract: Diffusion Transformers achieve impressive generative quality but remain computationally expensive due to iterative sampling. Recently, dynamic resolution sampling has emerged as a promising acceleration technique by reducing the resolution of early sampling steps. However, existing methods rely on heuristic re-noising at every resolution transition, injecting noise that breaks cross-stage consistency and forces the model to relearn global structure. In addition, these methods indiscriminately upsample the entire latent space at once without checking which regions have actually converged, causing accumulated errors, and visible artifacts. Therefore, we propose Fresco, a dynamic resolution framework that unifies re-noise and global structure across stages with progressive upsampling, preserving both the efficiency of low-resolution drafting and the fidelity of high-resolution refinement, with all stages aligned toward the same final target. Fresco achieves near-lossless acceleration across diverse domains and models, including 10 times speedup on FLUX, and 5 times on HunyuanVideo, while remaining orthogonal to distillation, quantization and feature caching, reaching 22 times speedup when combined with distilled models.
DSO: Direct Steering Optimization for Bias Mitigation
Authors: Lucas Monteiro Paes; Nivedha Sivakumar; Yinong Oliver Wang; Masha Fedzechkina; Barry-John Theobald; Luca Zappella; Nicholas Apostoloff
Venue: CVPR 2026 (main)

Abstract: Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Yet, VLM decisions are influenced by the perceived demographic attributes of people in the input, which can lead to biased outcomes like failing to identify women as doctors. Moreover, when reducing bias leads to performance loss, users may have varying needs for balancing bias mitigation with overall model capabilities, highlighting the demand for methods that enable controllable bias reduction during inference. Activation steering is a popular approach for inference-time controllability that has shown potential in inducing safer behavior in large language models (LLMs). However, we observe that current steering methods struggle to correct biases, where equiprobable outcomes across demographic groups are required. To address this, we propose Direct Steering Optimization (DSO) which uses reinforcement learning to find linear transformations for steering activations, tailored to mitigate bias while maintaining control over model performance. We demonstrate that DSO achieves state-of-the-art trade-off between fairness and capabilities on both VLMs and LLMs, while offering practitioners inference-time control over the trade-off. Overall, our work highlights the benefit of designing steering strategies that are directly optimized to control model behavior, providing more effective bias intervention than methods that rely on pre-defined heuristics for controllability.
From Pixel to Precision: Enhancing Handwritten Mathematical Expression Recognition with Image-Level Reward
Authors: Ze Liu; Kai Zhang; Xianquan Wang; Shuochen Liu; Jiaxian Yan; Yupeng Han; Qi Liu
Venue: CVPR 2026 (main)

Abstract: Handwritten mathematical expression recognition is hindered by a fundamental misalignment between the dual representations of LaTeX formulas: the symbolic text and the rendered visual image. This discrepancy means that textually distinct LaTeX sequences can produce visually identical outputs, while minor textual errors can cause catastrophic rendering failures. As a result, text-level reward mechanisms cannot perfectly assess the quality of model predictions, failing to effectively guide the model towards optimal performance during training. To overcome this limitation, we introduce the Image Matching Score (IMS), a lightweight yet effective reward based on the structural edit distance of column-wise image projections, which robustly quantifies the visual fidelity between rendered formulas. Leveraging IMS, we then propose Image-Matching driven Policy Optimization (IMPO), a training framework built upon Group Relative Policy Optimization (GRPO). This approach facilitates stable policy learning directly from our sequence-level visual reward, notably without the need for a separate value function network. Extensive experiments demonstrate that IMPO yields consistent performance gains across various backbone models on the challenging CROHME, HME100K, and M2E datasets. Our framework establishes new state-of-the-art results, improving the Expression Recognition Rate by an average of 1.1% and up to 1.37% over strong prior methods.
Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
Authors: Muyang Li; Yucheng Liu; Jianbo Ma; Elliot Osborne; Bo Han; Tongliang Liu
Venue: CVPR 2026 (main)

Abstract: Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works have explored various combinations of vision encoders and LLMs, there still lacks a principled understanding of what makes a vision encoder suitable for VLM alignment. In this paper, we systematically investigate this question via comprehensive experiments on a curated collection of 19 pre-trained vision encoders from diverse sources. We first demonstrate that common practices, such as choosing encoders with the largest size or highest zero-shot accuracy, consistently fail to identify optimal models. In fact, these metrics show only weak to moderate correlation with VLM performance. This intriguing finding begs a fundamental question: What factors of vision-encoders matter in VLM? Through comprehensive analysis, we identify that the structural similarity across modalities plays a crucial but previously overlooked role in vision-encoder selection, which we measure using the Gromov-Wasserstein distance as a proxy. From a theoretical perspective, we show that the learnability of cross-modality mapping can be provably associated with the Gromov-Wasserstein distance. Empirical verification on 60+ full VLM training runs shows that our proposed inference-only metric performs significantly better than alternative model selection strategies and exhibits a much stronger correlation with final VLM performance, thereby enabling efficient and effective prediction of VLM performance before full training.
TSTM: Temporal Segmentation for Task-relevant Mask in Visual Reinforcement Learning Generalization
Authors: Weicheng Du; Wenjia Meng; Zhengzhe Zhang; Yilong Yin; Xiankai Lu
Venue: CVPR 2026 (main)

Abstract: Achieving strong policy generalization to unseen environments remains a core challenge in visual reinforcement learning, and segmenting task-relevant regions to mitigate the influence of irrelevant visual cues has emerged as a promising direction. However, existing methods rely solely on the current observation, lack temporal information, and fail to exploit preceding observations, leaving learned policies susceptible to task-irrelevant background variations and ultimately degrading policy performance. In this paper, we propose temporal segmentation for task-relevant mask in visual reinforcement learning, named TSTM, which extracts task-relevant regions from sequential observations by exploiting temporal information, thereby producing more reliable masks and improving policy generalization. TSTM introduces a temporal segmentation network with an encoder-temporal-decoder architecture, where a convolutional LSTM module captures temporal dependencies across observations. To reduce inference overhead, we further develop a lightweight student network as an efficient substitute for the teacher network. The resulting task-relevant masks are encoded by a CNN-based encoder, and invariant representation learning is employed to improve robustness by enforcing consistency between representations of the original and augmented observation sequences. With these task-relevant representations, we train an actor-critic agent to learn a policy with strong generalization capability. Experimental results demonstrate that TSTM achieves superior generalization performance over existing state-of-the-art methods on most visual RL tasks.