GenAI4ELab/papercli-papers huggingface dataset:
VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues
Authors: Sirnam Swetha; Rohit Gupta; Parth Parag Kulkarni; David G Shatwell; Jeffrey A Chan Santiago; Nyle Siddiqui; Joseph Fioresi; Mubarak Shah
Venue: CVPR 2026 (main)

Abstract: Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly focus on questions answerable through explicit visual content - actions, objects, and events - directly observable within individual frames or short clips. To truly understand videos as humans do, models must go beyond what is directly shown, inferring hidden relationships and contextual cues that are only implied across frames. Current benchmarks fail to capture this essential aspect of video understanding. To address this gap, we introduce VRR-QA, a benchmark for Visual Relational Reasoning Beyond Explicit Cues. We curate our benchmark from creative and cinematic videos such as movies, that deliberately employ storytelling techniques which omit direct depictions of certain events or relations, requiring viewers to infer them. VRR-QA comprises 1K meticulously expert-annotated QA pairs drawn from 1K creative video clips covering 15 genres across 7 decades of content, from both live-action and animated titles. Our extensive evaluations on 14 leading VideoQA models reveals consistent and significant performance degradation, underscoring their reliance on surface-level visual cues and highlighting the difficulty of implicit reasoning. Even the best model substantially underperforms human baselines with only 64% accuracy. Performance variations across models further illustrate the complexity and diversity of the challenges presented by VRR-QA. By releasing both dataset and data collection framework, VRR-QA establishes a rigorous, diverse, and reproducible testbed for advancing VideoQA.
ESAM++: Efficient Online 3D Perception on the Edge
Authors: Qin Liu; Lavisha Aggarwal; Saptarashmi Bandyopadhyay; Vikas Bahirwani; Marc Niethammer; Ehsan Adeli; Andrea Colaco
Venue: CVPR 2026 (main)

Abstract: Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited and privacy is crucial. Recent state-of-the-art methods like EmbodiedSAM (ESAM) demonstrate the promise of online 3D perception by leveraging the Segment Anything Model (SAM) for real-time, fine-grained, and generalized 3D instance segmentation. However, ESAM still relies on a computationally expensive 3D sparse UNet for point cloud feature extraction, which accounts for the majority of the 3D inference time, hindering its practicality on resource-constrained devices. In this paper, we propose ESAM++, a lightweight and scalable alternative for online 3D scene perception tailored to edge devices without GPU acceleration. Our method introduces a 3D Sparse Feature Pyramid Network (SFPN) that efficiently captures multi-scale geometric features from streaming 3D point clouds while significantly reducing computational overhead and model size. We evaluate our approach on four challenging segmentation benchmarks, namely ScanNet, ScanNet200, SceneNN, and 3RScan, demonstrating that our model achieves competitive accuracy with up to 3x faster inference with a 2x smaller model size compared to ESAM, enabling practical deployment on edge devices.
CHAL: Causal-guided Hierarchical Anomaly-aware Learning for Moving Infrared Small Target Detection
Authors: Weiwei Duan; Luping Ji; Shipeng Lei; Sicheng Zhu; Jianghong Huang; Mao Ye
Venue: CVPR 2026 (main)

Abstract: Infrared small target detection is one highly special category of object detection, faced with tiny target imaging size and cluttered backgrounds. Currently, almost all existing methods are target-centered, directly learning the target features from backgrounds. However, due to weak target signals, they are often difficult in effectively capturing stable features. Sometimes, they cannot even distinguish real targets from background confounders. To overcome these problems, from an opposite perspective, we propose the first Causal-guided Hierarchical Anomaly-aware Learning (CHAL) framework. Breaking through target-centered paradigm, it focuses on background learning, while the targets are handled as the anomalies in backgrounds. In detail, to fulfill the goal, a spatio-temporal neural field is designed to model the background evolution patterns from generative perspective. Meanwhile, a hierarchical anomaly-aware learning is proposed to decompose anomaly discovery. Furthermore, to block the spurious correlations often caused by background confounders, and enhance true target causality, a causal-guiding mechanism is designed. The experiments on three infrared datasets verify the effectiveness and superiority of our CHAL. Even in visible-light scenarios, it still possesses obvious adaptivity. Source code will be open.
Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
Authors: Hanchao Liu; Fang-Lue Zhang; Shining Zhang; Tai-Jiang Mu; Shi-Min Hu
Venue: CVPR 2026 (main)

Abstract: Generating human motion that satisfies customized zero-shot goal functions, enabling applications such as controllable character animation and behavior synthesis for virtual agents, is a critical capability. While current approaches handle many unseen constraints, they fail on tasks with very challenging spatiotemporal restrictions, such as severe spatial obstacles or specified numbers of walking steps. To equip motion generators for these highly constrained tasks, we present a retrieval-guided method built on the training-free diffusion noise optimization framework. The key idea is to search within large motion datasets for guidance that can potentially satisfy difficult constraints. We introduce relational task parsing to group target constraints and identify the difficult ones to be handled by retrieved reference. A better initialization for diffusion noise is then obtained via a reward-guided mask that combines random noise with retrieved noise. By optimizing diffusion noise from this improved initialization, we successfully solve highly constrained generation tasks. By leveraging LLM for relational task parsing, the whole framework is further enabled to automatically reason for what to retrieve, improving the intelligence of moving agents under a training-free optimization scheme. Project page: https://hanchaoliu.github.io/RetrievalGuidedDNO/
DeepProtect: Proactive Face-Swapping Defense using Identity Blending and Attribute Distortion
Authors: Eungi Lee; Seung-hyeok Back; Hyung-Il Kim; Seok Bong Yoo
Venue: CVPR 2026 (main)

Abstract: Face-swapping deepfakes allow realistic identity transfer, which can serve creative purposes but increases the risk of identity abuse. A proactive defense aims to prevent deepfake creation by obstructing identity feature extraction from input images, essential for identity-driven face-swapping. Existing proactive defense approaches aim to protect faces by hindering accurate identity feature extraction, but tend to introduce visible artifacts and fail to degrade the visual quality of the face-swapping deepfakes. This work proposes a proactive face-swapping defense using identity blending and attribute distortion (DeepProtect) that integrates global identity fusion in the latent space and local prompt-driven adversarial watermarking to address these problems. This work dilutes distinct identity representations by channel-wise blending of multiple identities in the latent space and optimizing the generator for visual consistency. The proposed approach distorts facial components in the identity space, directly influencing how faces are reconstructed in deepfakes. This approach applies semantic directions derived from user-provided text prompts to embed imperceptible adversarial watermarks that selectively distort facial attributes, affecting the visual fidelity of deepfake results. The proposed method hinders face-swapping deepfakes while preserving the perceptual quality of the protected images, offering a robust and practical solution for facial privacy protection. The experimental results reveal that DeepProtect effectively defends against face-swapping deepfakes while preserving visual consistency.
R3-PCQA: Ray-Reprojection-Reinforcement for No-Reference 3D Point Cloud Quality Assessment
Authors: Junhyuk Seo; Sanghyuk Seo; Dawoon Kim; Heeseok Oh
Venue: CVPR 2026 (main)

Abstract: Prevailing no-reference 3D point cloud quality assessment methods predominantly treat 2D projections and 3D point clouds as independent modalities and rely on simplistic feature fusion, thereby neglecting fundamental mechanisms underlying human 3D perception. To address this limitation, we introduce R3-PCQA (Ray-Reprojection-Reinforcement 3D Point Cloud Quality Assessor), a novel and principled framework that explicitly encodes perceptual priors into the assessment pipeline: A geometric-aware ray-based reprojection pipeline simulates viewpoint-dependent observation of 3D structure. A reinforcement-learning-based quality-salient subcloud selector adaptively attends to perceptually informative regions. The global view attention module aggregates local quality responses across viewpoints, forming a unified representation that facilitates reliable cross-view inference. Extensive experiments demonstrate that R3-PCQA achieves state-of-the-art performance on SJTU-PCQA, WPC, and WPC2.0.
Learning Anchor in Dual Orthogonal Space for Fast Multi-view Clustering
Authors: Yalan Qin; Hanzhou Wu
Venue: CVPR 2026 (main)

Abstract: Large-scale multi-view clustering aims to explore the complementary and consistent information among different views in efficient manner. Despite the impressive performance gained by the existing methods, they just perform anchor learning in a single space with the orthogonal or some other constraints from the multi-view data, leading to undesired anchors. The anchors can simultaneously occur in more spaces and the complementary information among these spaces is able to be adopted for learning anchors. Meanwhile, the space with basis being the anchored cluster center is neglected to learn anchors by most existing works. In this work, we propose learning anchor in Dual Orthogonal Space for Fast Multi-view Clustering (DOSFMVC). DOSFMVC conducts anchor learning in dual orthogonal space, aiming at utilizing the complementary information among two spaces in producing anchors with high quality. DOSMFVC introduces the consensus anchored cluster center as basis of the extra space and clustering indicator of anchors based on this basis in anchor learning. The anchor learning and partition are integrated into a unified model, where the final cluster assignment can be adopted for clustering results. Extensive experiments confirm the superiority of our method compared with some state-of-the-art methods on several benchmark datasets.
History to Future: Evolving Agent with Experience and Thought for Zero-shot Vision-and-Language Navigation
Authors: Guangzhao Dai; Shuo Wang; Zihan Wang; Guo-Sen Xie; Yang Yang; Jinshan Pan; Qianru Sun; Xiangbo Shu
Venue: CVPR 2026 (main)

Abstract: Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve the agent's decision-making with future thought and history experience via Future Chain-of-Thought (F-CoT) and History Chain-of-Experience (H-CoE). F-CoT predicts future actions and landmarks as thoughts to assist navigation progress estimation and direction selection, while H-CoE summarizes historical trajectories and scenes as experience to improve navigation decision reliability. Both F-CoT and H-CoE cooperatively evolve the agent's decision-making. Extensive experiments in both the simulator and real-world environments demonstrate the effectiveness of our EvoNav.
CTCal: Rethinking Text-to-Image Diffusion Models via Cross-Timestep Self-Calibration
Authors: Xiefan Guo; Xinzhu Ma; Haiyu Zhang; Di Huang
Venue: CVPR 2026 (main)

Abstract: Recent advancements in text-to-image synthesis have been largely propelled by diffusion-based models, yet achieving precise alignment between text prompts and generated images remains a persistent challenge. We find that this difficulty arises primarily from the limitations of conventional diffusion loss, which provides only implicit supervision for modeling fine-grained text-image correspondence. In this paper, we introduce Cross-Timestep Self-Calibration (CTCal), founded on the supporting observation that establishing accurate text-image alignment within diffusion models becomes progressively more difficult as the timestep increases. CTCal leverages the reliable text-image alignment (i.e., cross-attention maps) formed at smaller timesteps with less noise to calibrate the representation learning at larger timesteps with more noise, thereby providing explicit supervision during training. We further propose a timestep-aware adaptive weighting to achieve a harmonious integration of CTCal and diffusion loss. CTCal is model-agnostic and can be seamlessly integrated into existing text-to-image diffusion models, encompassing both diffusion-based (e.g., SD 2.1) and flow-based approaches (e.g., SD 3). Extensive experiments on T2I-Compbench++ and GenEval benchmarks demonstrate the effectiveness and generalizability of the proposed CTCal. Our code is available at https://github.com/xiefan-guo/ctcal.
Learning Spatial-Temporal Consistency for 3D Semantic Scene Completion
Authors: Yujie Xue; Meng Wang; Ruihui Li; Fan Wu; Zhizhong Liu; Zhuo Tang; Kenli Li
Venue: CVPR 2026 (main)

Abstract: Camera-based Semantic Scene Completion (SSC) is able to comprehensively understand the entire scene, but it suffers from ambiguous predictions due to occlusions and incomplete information. Temporal SSC alleviates this issue, but existing models simply stack multi-frame temporal features, which can lead to inconsistencies between geometry and semantics over time. In this paper, we present ConSSC, a novel SSC method that learns Spatial-Temporal Consistency. It works by lifting historical frames into a 3D scene-level occupancy framework, aggregating 2D and 3D historical features from current voxels, and learning from 2D visibility and similarity cues in a temporal buffer. Specifically, our framework introduces two key components: the Hierarchical Voxel Refinement module, which extracts a coarse occupancy from depth and refines it through voxel-level representations, recovering missing information. The Temporal Semantic Aggregation module effectively integrates semantic features from different viewpoints and time points, enabling the reconstruction of occluded regions in the current frame using historical context, aggregating them into corresponding voxel features. Without additional sensors or data, ConSSC improves both geometric and semantic consistency. Extensive experiments on the SemanticKITTI and SSCBench-KITTI-360 datasets show that ConSSC outperforms state-of-the-art camera-based and temporal SSC baselines by a significant margin in terms of IoU and mIoU.
StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
Authors: Ke Xing; Longfei Li; Yuyang Yin; Hanwen Liang; Guixun Luo; Chen Fang; Jue Wang; Konstantinos N. Plataniotis; Xiaojie Jin; Yao Zhao; Yunchao Wei
Venue: CVPR 2026 (main)

Abstract: The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone.To address this challenge, we present **StereoWorld**, an **end-to-end framework** that repurposes a pretrained video generator for high-fidelity monocular-to-stereo video generation. Our framework jointly conditions the model on the monocular video input while explicitly supervising the generation with a **geometry-aware regularization** to ensure 3D structural fidelity.A spatio-temporal tiling scheme is further integrated to enable efficient, high-resolution synthesis.To enable large-scale training and evaluation, we curate a **high-definition stereo video dataset** containing over 11M frames aligned to natural human interpupillary distance (IPD).Extensive experiments demonstrate that StereoWorld substantially outperforms prior methods, generating stereo videos with superior visual fidelity and geometric consistency.
Low-Rank Test-Time Training for Pre-Trained Point Cloud Models
Authors: Ouyangzi Ye; Feifei Shao; Kexin Li; Yawei Luo; Zikai Song; Ping Liu; Fengda Zhang; Hongwei Wang; Jun Xiao
Venue: CVPR 2026 (main)

Abstract: Test-time training (TTT) enhances the robustness of pretrained models to out-of-distribution (OOD) data through auxiliary self-supervised tasks, without requiring labeled samples. However, existing TTT methods predominantly rely on decoder-based auxiliary objectives, which suffer from inefficient adaptation and weak coupling with the primary task. To solve these limitations, we revisit the mechanism of test-time training by analyzing masking-based pretrained models to uncover the fundamental source of their OOD robustness. Our investigation reveals that their generalization capability stems from a latent feature-level structural invariance, the consistency of encoded representations under masked perturbations. Building on this insight, we introduce LoTT-PC, a lightweight LoRA-based framework that operationalizes this invariance-preserving principle for 3D point cloud classification. LoTT-PC consists of two main components: (1) low-rank modulation units for parameter-efficient adaptation, and (2) a permutation-invariant alignment mechanism that enforces representation consistency through masked feature alignment. Extensive experiments on multiple benchmarks demonstrate that this unified design enables pretrained point cloud models to self-tune rapidly and reliably across diverse OOD scenarios, outperforming state-of-the-art methods by an average of 2.7% in accuracy under various corruption types.
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
Authors: Zhenjie Yang; Yilin Chai; Xiaosong Jia; Qifeng Li; Yuqian Shao; Xuekai Zhu; Haisheng Su; Junchi Yan
Venue: CVPR 2026 (main)

Abstract: End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensor data and robust handling of diverse and complex driving scenarios, particularly rare maneuvers such as aggressive turns. The recent success of the Mixture-of-Experts (MoE) architecture in Large Language Models (LLMs) demonstrates that expert specialization enables strong scalability. In this work, we propose DriveMoE, a novel MoE-based E2E-AD framework, with a Scene-Specialized Vision MoE and a Skill-Specialized Action MoE. First, we introduce Drive-p0, a Vision-Language-Action (VLA) baseline adapted from Embodied AI for autonomous driving, which serves as the foundation model for DriveMoE. Building on this, we strengthen perception through a carefully designed Vision MoE, where a router adaptively selects context-relevant camera views. This mechanism is inspired by human driving cognition, in which attention is directed to key visual cues rather than to all sensory inputs simultaneously. Beyond perception, we introduce an Action MoE that augments the framework by training a router to activate specialized expert modules tailored to distinct driving behaviors. Within the Action MoE, we implement two distinct styles(Token-level Router and Trajectory-level Router) and extensively explore their applicability in autonomous driving. In Bench2Drive closed-loop evaluations, DriveMoE demonstrates robust performance across diverse driving scenarios, alleviates the mode-averaging effect that limits existing models, and achieves state-of-the-art results with significant improvements over Drive-p0. We will release our code and models of DriveMoE and Drive-p0.
AgentDet: A Shared-Blackboard Multi-Agent Framework for Zero-/Few-Shot Object Detection
Authors: Haolin Li; Yaohua Wang; Ze Yan; Lijie Wen; Biqing Huang
Venue: CVPR 2026 (main)

Abstract: Large multimodal language models have made rapid progress on vision-language tasks, yet their potential for zero-/few-shot object detection (ZSOD/FSOD) under a closed set of target classes has yet to be fully explored.. ZSOD/FSOD is hampered by data scarcity and catastrophic forgetting. Although vision-language models (VLMs) demonstrated excellent performance on multiple benchmarks, they typically rely on large-scale visual pre-training, which is inconsistent with the goal of FSOD to test generalization to new categories under limited supervision. We introduce AgentDet, a shared-blackboard multi-agent framework that unifies ZSOD and FSOD via pseudo-incremental learning. AgentDet decouples detection into four cooperating roles--Agent-Scout, Agent-Pinner, Agent-Curator, and Agent-Judge--which collaboratively maintain a Shared Blackboard and a Knowledge Base. For efficiency, we only train Agent-Judge by updating its image encoder and LLM-based detection head, which is a lightweight recipe that encourages generalization to previously unseen categories. On PASCAL VOC and MS COCO ZSOD/FSOD datasets, AgentDet achieves strongly competitive performance with state-of-the-art results in several settings.
Pixel2Phys: Distilling Governing Laws from Visual Dynamics
Authors: Ruikun Li; Jun Yao; Yingfan Hua; Shixiang Tang; Biqing Qi; Bin Liu; Wanli Ouyang; Yan Lu
Venue: CVPR 2026 (main)

Abstract: Discovering physical laws directly from high-dimensional visual data is a long-standing human pursuit but remains a formidable challenge for machines, representing a fundamental goal of scientific intelligence. This task is inherently difficult because physical knowledge is low-dimensional and structured, whereas raw video observations are high-dimensional and redundant, with most pixels carrying little or no physical meaning. Extracting concise, physically relevant variables from such noisy data remains a key obstacle. To address this, we propose Pixel2Phys, a collaborative multi-agent framework adaptable to any Multimodal Large Language Model (MLLM). It emulates human scientific reasoning by employing a structured workflow to extract formalized physical knowledge through iterative hypothesis generation, validation, and refinement. By repeatedly formulating, and refining candidate equations on high-dimensional data, it identifies the most concise representations that best capture the underlying physical evolution. This automated exploration mimics the iterative workflow of human scientists, enabling AI to reveal interpretable governing equations directly from raw observations. Across diverse simulated and real-world physics videos, Pixel2Phys discovers accurate, interpretable governing equations and maintaining stable long-term extrapolation where baselines rapidly diverge.