GenAI4ELab/papercli-papers huggingface dataset:
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
Authors: Weile Chen; Bingchen Miao; Qifan Yu; Wendong Bu; Guoming Wang; Wenqiao Zhang; Shengyu Zhang; Juncheng Li; Siliang Tang
Venue: CVPR 2026 (main)

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines or expensive expert trajectories, limiting their adaptability to complex, dynamic environments. To address these challenges, we propose SCALE (Self-Cognitive-Aware Learning and Exploration), which leverages three adversarial roles--Selector, Predictor, and Judger to autonomously discover the agent's limitations and expand its cognitive boundaries through environmental exploration. Moreover, we propose SCALE-Hop, a graph exploration strategy that facilitates global planning and helps agents avoid local exploration traps. To further support learning, we construct SCALE-20k, a large-scale dataset collected from 19 real-world websites, containing diverse task types and structured demonstrations generated from SCALE's exploration traces. Experimental results show that our approach significantly improves the performance and generalization of multiple MLLMs in various web environments. Our framework offers a scalable and generalizable solution for building truly autonomous and adaptive web agents.
EarlyTom: Early Token Compression Completes Fast Video Understanding
Authors: Hesong Wang; Xin Jin; Lu Lu; Chenhaowen Li; Jian Chen; Qiang Liu; Huan Wang
Venue: CVPR 2026 (main)

Abstract: Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is still hindered by the inefficiency introduced by processing massive amounts of visual tokens. Although recent approaches achieve extremely low token retention ratios while maintaining accuracy comparable to full-token baselines, most of them perform compression only at the late stage of prefilling, leaving the efficiency of the vision encoder unoptimized. In this paper, we first show that vision encoding contributes a large portion to the time-to-first-token (TTFT). Therefore, instead of compressing visual tokens only after the vision encoder, performing compression inside the encoder still leaves substantial room for exploration. Based on this insight, we propose EarlyTom, a training-free token compression framework that performs early-stage visual token compression inside the vision encoder, enabling significantly better TTFT reduction and higher throughput. In addition, we introduce a decoupled spatial token selection strategy that improves the overall compression effectiveness. EarlyTom reduces TTFT by up to 2.65x and FLOPs by up to 61% on a single NVIDIA A100 GPU for the LLaVA-OneVision-7B model, while maintaining accuracy comparable to the full-token baseline. These improvements substantially enhance the practicality of deploying Video-LLMs in real-world production scenarios.
Black-Box Domain Adaptation for Object Detection with Retention-Driven Knowledge Compression
Authors: Yuwu Lu; Chunzhi Liu
Venue: CVPR 2026 (main)

Abstract: Black-Box Domain Adaptation (BBDA) is a highly practical yet challenging strategy that enables the deployment of pre-trained detectors to new unlabeled target domains without accessing source data or models. Compared to previous domain adaptation studies, BBDA not only provides stronger data privacy protection but also offers greater portability. Despite growing interest, existing BBDA strategies remain difficult to apply directly to object detection, as most prior works focus on classification and segmentation tasks that do not involve bounding box localization and rely on different learning mechanisms. In this paper, inspired by lifelong learning, we propose Retention-Driven Knowledge Compression (RDKC), which applies a brain-inspired continual learning process to BBDA for object detection. Specifically, RDKC consists of two key components: Memory Retention (MR) and Scene Compression (SC). MR is designed specifically for object detection under the BBDA setting, where it performs memorized contrastive learning on partitioned regions to better utilize informative cues from reliable areas while filtering out potential noise from noise prediction labels. SC introduces a contrastive mechanism between near- and far-view regions, which enables the model to better learn from far-view regions under the guidance of near-view cues. Experimental results demonstrate that under the BBDA setting, RDKC outperforms previous SOTA methods across all evaluated benchmarks, achieving superior performance improvements.
Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training
Authors: Hexiao Lu; Xiaokun Sun; Zeyu Cai; Hao Guo; Ying Tai; Jian Yang; Zhenyu Zhang
Venue: CVPR 2026 (main)

Abstract: We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton--a fundamental representation of biological forms--to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing.
ReaGEN: Adaptive Generation of Structured Chains-of-Thought for Efficient Multimodal Reasoning
Authors: Ruiqing Tian; Mohan Sai Singamsetti; Di Niu; Bahador Rashidi
Venue: CVPR 2026 (main)

Abstract: Large Vision Language Models (LVLMs) exhibit strong perceptual and linguistic capabilities yet struggle with complex visual reasoning tasks that require structured, compositional, and adaptive inference. Existing approaches either rely on costly inference-time exploration--such as multi-path or tree-based Chain-of-Thought (CoT) search--or on expensive post-training with large curated CoT datasets. We propose ReaGEN, a lightweight framework for the adaptive generation of structured reasoning chains that enhances reasoning without modifying the underlying vision-language model (VLM). ReaGEN first employs a teacher-guided evolutionary search to collect sample specific CoT structure, leveraging attention-derived stage importance to capture how information flows across reasoning stages. These adaptive CoT structures are then used to train a compact generator (GEN) that learns to refine and improve CoT structures by reflecting on attention feedback from the reasoning process. At inference, the GEN dynamically produces question-adaptive structured CoTs, and can be iteratively invoked to refine them based on the VLM's internal state--achieving the flexibility of deep search with single-path efficiency. Across diverse multimodal reasoning benchmarks, ReaGEN achieves up to +26 accuracy points over test-time scaling methods while reducing the average inference-time token usage by 79%, making it a scalable approach for structured reasoning generation in self-hosted or open-source VLMs with access to internal attention signals.
High Resolution Neural Video Coding with Bi-directional Confidence-Guided Reference Information Modeling
Authors: Feng Ye; Kai Zhang; Li Zhang; Chuanmin Jia
Venue: CVPR 2026 (main)

Abstract: Exploiting bi-directional context prediction has long been recognized as a key direction for improving compression efficiency in neural video coding. However, existing neural B-frame codecs still exhibit limited performance gains, particularly in high-resolution videos with large motion, where optical flow estimation becomes unreliable and balanced prediction fusion introduces distortions. To address these challenges, we present the first High-Resolution bi-directional neural video coding method, termed as HR-NVC, which non-uniformly integrates confidence-guided predictive cues from both temporal directions to achieve more reliable and efficient compression. Specifically, we propose Spatio-Temporal Anchored Motion Estimation, which introduces virtual anchor frames and low-resolution priors to significantly improve estimation robustness under large displacements. We further design a Hierarchical Motion Representation that converges multi-scale motion with temporal references, enabling compact and adaptive modeling of motion reliability across resolutions. Finally, a Bi-Contextual Asymmetric Harmonization module performs confidence-guided fusion of bidirectional references, effectively suppressing unreliable contexts and restoring structural consistency near occlusion and scene transition regions. Notably, our model is the first end-to-end-optimized video codec evaluated on 4K-resolution videos, establishing a new benchmark for higher-resolution NVC and achieving state-of-the-art performance among neural B-frame codecs.
WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories
Authors: Yisu Zhang; Chenjie Cao; Tengfei Wang; Xuhui Zuo; Junta Wu; Jianke Zhu; Chunchao Guo
Venue: CVPR 2026 (main)

Abstract: Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due to limited camera controllability and inconsistent generated content when viewed from distinct camera trajectories.In this paper, we propose WorldStereo, a novel framework that bridges camera-guided video generation and 3D reconstruction via two dedicated geometric memory modules. Formally, the global-geometric memory enables precise camera control while injecting coarse structural priors through incrementally updated point clouds.Moreover, the spatial-stereo memory constrains the model's attention receptive fields with 3D correspondence to focus on fine-grained details from the memory bank.These components enable WorldStereo to generate multi-view-consistent videos under precise camera control, facilitating high-quality 3D reconstruction.Furthermore, the flexible control branch-based WorldStereo shows impressive efficiency, benefiting from the distribution matching distilled VDM backbone without joint training.Extensive experiments across both camera-guided video generation and 3D reconstruction benchmarks demonstrate the effectiveness of our approach. Notably, we show that WorldStereo acts as a powerful world model, tackling diverse scene generation tasks (whether starting from perspective or panoramic images) with high-fidelity 3D results. Models will be released.
CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference
Authors: Nurjahan Sultana; Moi Hoon Yap; Xinqi Fan; Wenqi Lu
Venue: CVPR 2026 (main)

Abstract: Models for AI-based skin cancer screening suffer a severe performance drop when shifting from expert dermoscopic (source) images to consumer-grade clinical (target) photos, hindering real-world deployment. Existing domain adaptation methods often ignore crucial semantic invariants, such as clinical concepts. While new foundation models like MONET can provide this semantic information as dense, probabilistic scores, this metadata is unavailable at test time, creating a deployment paradox for practical image-only screening tools. We address this gap by proposing CoFiDA-M, a privileged information framework that learns from concepts at training time but deploys as an image-only model. Our method trains a teacher network that uses MONET concept probabilities to guide a FiLM modulator, transforming visual features into a semantically "edited" feature space. A lightweight, image-only student is then trained to reproduce this edited representation, not just the teacher's final predictions. This distillation "bakes" the clinical reasoning into the student's weights.On a challenging multi-dataset benchmark, our image-only student significantly outperforms state-of-the-art approaches, especially in melanoma recall. Our work provides a practical and generalizable framework for leveraging noisy, probabilistic metadata as privileged information, demonstrating strong cross-dataset robustness and potential for real-world deployment beyond dermatology. The code will be made publicly available upon acceptance.
GSV2X: Geometry-Aware Uncertainty Modeling and Orthogonal Fusion for Robust Roadside Perception
Authors: Jianqiang Xu; Gensheng Pei; Huafeng Liu; Yazhou Yao
Venue: CVPR 2026 (main)

Abstract: Reliable 3D perception from multi-view roadside sensors hinges on the robust fusion of camera and LiDAR data, a task complicated by geometric misalignments and sensor calibration errors. This paper presents GSV2X, a fusion framework that tackles these challenges through two core contributions. First, to achieve robustness against spatial uncertainty, we lift 2D image features into a unified Bird's-Eye-View (BEV) space by representing them as 3D Gaussian distributions. By incorporating learnable perturbations guided by camera geometry, our model explicitly accounts for potential calibration inaccuracies. Second, to maximize the synergy between modalities, we propose a new orthogonal fusion module. This module employs constrained attention to enforce orthogonality between camera and LiDAR features, effectively disentangling redundant information and promoting the learning of complementary representations. Extensive experiments on the challenging RCooper dataset demonstrate that GSV2X sets a new state-of-the-art in multi-view roadside perception and exhibits remarkable robustness in complex, real-world scenarios.
FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model
Authors: Xiang Chen; Jinshan Pan; Jiangxin Dong; Jian Yang; Jinhui Tang
Venue: CVPR 2026 (main)

Abstract: Recent studies have witnessed significant advances in image restoration foundation models driven by improvements in the scale and quality of pre-training data. In this work, we find that the data mixture proportions from different restoration tasks are also a critical factor directly determining the overall performance of all-in-one image restoration models. To this end, we propose a high-capacity diffusion-based image restoration foundation model, FoundIR-v2, which adopts a data equilibrium scheduling paradigm to dynamically optimize the proportions of mixed training datasets from different tasks. By leveraging the data mixing law, our method ensures a balanced dataset composition, enabling the model to achieve consistent generalization and comprehensive performance across diverse tasks. Furthermore, we introduce an effective Mixture-of-Experts (MoE)-driven scheduler into generative pre-training to flexibly allocate task-adaptive diffusion priors for each restoration task, accounting for the distinct degradation forms and levels exhibited by different tasks. Extensive experiments demonstrate that our method can address over 50 sub-tasks across a broader scope of real-world scenarios and achieves favorable performance against state-of-the-art approaches.
Learning Coordinate-based Convolutional Kernels for Continuous SE(3) Equivariant and Efficient Point Cloud Analysis
Authors: Jaein Kim; Hee Bin Yoo; Dong-Sig Han; Byoung-Tak Zhang
Venue: CVPR 2026 (main)

Abstract: A symmetry on rigid motion is one of the salient factors in efficient learning of 3D point cloud problems. Group convolution has been a representative method to extract equivariant features, but its realizations have struggled to retain both rigorous symmetry and scalability simultaneously. We advocate utilizing the intertwiner framework to resolve this trade-off, but previous works on it, which did not achieve complete SE(3) symmetry or scalability to large-scale problems, necessitate a more advanced kernel architecture. We present Equivariant Coordinate-based Kernel Convolution, or ECKConv. It acquires SE(3) equivariance from the kernel domain defined in a double coset space, and its explicit kernel design using coordinate-based networks enhances its learning capability and memory efficiency. The experiments on diverse point cloud tasks, e.g., classification, pose registration, part segmentation, and large-scale semantic segmentation, validate the rigid equivariance, memory scalability, and outstanding performance of ECKConv compared to state-of-the-art equivariant methods.
PRISM: Learning a Shared Primitive Space for Transferable Skeleton Action Representation
Authors: Di Yang; Yaohui Wang; Shuai Shao; François Brémond; Jiangtao Wang
Venue: CVPR 2026 (main)

Abstract: Real-world human action understanding remains challenging due to long-tailed label distributions, compositional motion patterns, and viewpoint variations. Existing skeleton-based methods often lack a structured and transferable representation of motion, and task-specific models for generation, classification, and detection are usually trained independently, resulting in fragmented pipelines and limited cross-task generalization. We present PRISM, a PRImitive-centric Skeleton Modeling framework that learns a shared motion representation from a motion generation objective and transfers it to perception tasks. PRISM represents each action sequence as a trajectory in a primitive coefficient space, which captures how a set of learned atomic motion primitives contribute to the observed motion. A structured decomposition module learns this representation in a physically grounded and view-invariant manner via motion generation. Instead of enforcing joint or unified training across tasks, PRISM provides a single primitive-centric representation that can be sequentially transferred to downstream classification and frame-wise detection through lightweight task heads. This representation introduces structure, compositionality, and improved generalization across distinct supervisions. PRISM consistently improves performance on long-tailed and multi-label datasets and enables interpretable reasoning over compositional and rare actions. Extensive experimental results show that the structured primitive space serves as a transferable and robust foundation for diverse action understanding tasks in real-world datasets.
SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearning
Authors: Cai Selvas-Sala; Lei Kang; Lluis Gomez
Venue: CVPR 2026 (main)

Abstract: As multimodal models like CLIP become integral to downstream systems, the need to remove sensitive information is critical. However, machine unlearning for contrastively-trained encoders remains underexplored, and existing evaluations fail to diagnose fine-grained, association-level forgetting. We introduce SALMUBench (Sensitive Association-Level Multimodal Unlearning), a benchmark built upon a synthetic dataset of 60K persona-attribute associations and two foundational models: a Compromised model polluted with this data, and a Clean model without it. To isolate unlearning effects, both are trained from scratch on the same 400M-pair \texttt retain  base, with the Compromised model additionally trained on the \texttt sensitive  set. We propose a novel evaluation protocol with structured holdout sets (\texttt holdout_identity , \texttt holdout_association ) to precisely measure unlearning efficacy and collateral damage. Our benchmark reveals that while utility-efficient deletion is feasible, current methods exhibit distinct failure modes: they either fail to forget effectively or over-generalize by erasing more than intended. SALMUBench sets a new standard for comprehensive unlearning evaluation, and we publicly release our dataset, models, evaluation scripts, and leaderboards to foster future research.
Stay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video Captioning
Authors: Seung Hyup Baek; Jimin Lee; Hyeongkeun Lee; Jae Won Cho
Venue: CVPR 2026 (main)

Abstract: Dense Video Captioning (DVC) is a challenging multimodal task that involves temporally localizing multiple events within a video and describing them with natural language. While query-based frameworks enable the simultaneous, end-to-end processing of localization and captioning, their reliance on shared queries often leads to significant multi-task interference between the two tasks, as well as temporal redundancy in localization. In this paper, we propose utilizing role-specific queries that separate localization and captioning into independent components, allowing each to exclusively learn its role. We then employ contrastive alignment to enforce semantic consistency between the corresponding outputs, ensuring coherent behavior across the separated queries. Furthermore, we design a novel suppression mechanism in which mutual temporal overlaps across queries are penalized to tackle temporal redundancy, supervising the model to learn distinct, non-overlapping event regions for more precise localization. Additionally, we introduce a lightweight module that captures core event concepts to further enhance semantic richness in captions through concept-level representations. We demonstrate the effectiveness of our method through extensive experiments on major DVC benchmarks YouCook2 and ActivityNet Captions.
MusicInfuser: Making Video Diffusion Listen and Dance
Authors: Susung Hong; Ira Kemelmacher-Shlizerman; Brian Curless; Steven M. Seitz
Venue: CVPR 2026 (main)

Abstract: We introduce MusicInfuser, an approach that aligns pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized with specified music tracks. Rather than training a multimodal audio-video or audio-motion model from scratch, our method demonstrates how existing video diffusion models can be efficiently adapted to align with musical inputs. We propose a novel layer-wise adaptability criterion based on a guidance-inspired constructive influence function to select adaptable layers, significantly reducing training costs while preserving rich prior knowledge, even with limited, specialized datasets. Experiments show that MusicInfuser effectively bridges the gap between music and video, generating novel and diverse dance movements that respond dynamically to music. Furthermore, our framework generalizes well to unseen music tracks, longer video sequences, and unconventional subjects, outperforming baseline models in consistency and synchronization. All of this is achieved without requiring motion data, with training completed on a single GPU within a day.