GenAI4ELab/papercli-papers huggingface dataset:
BoostSLT: Boosting Sign Language Translation via a Plug-and-Play Diffusion-Based Semantic Enhancer
Authors: Changzhou Han; Wanlun Ma; Xi Tang; Kun Hu; Sheng Wen; Yang Xiang
Venue: CVPR 2026 (main)

Abstract: Sign Language Translation (SLT) converts continuous sign videos into spoken language text, yet current models, whether gloss-based or gloss-free, struggle with long or discourse-level inputs. Recent architectures such as TwoStreamNetwork and CV-SLT have nearly saturated short-sentence accuracy, but their performance degrades on long sentences and multi-sentence paragraphs. In real scenarios such as news, interviews or daily conversations, signers naturally produce extended signing sequences with complex contextual dependencies. Moreover, identifying precise gloss boundaries remains a key obstacle, while gloss-based methods, though often superior, incur heavy annotation costs. The community therefore needs a solution that mitigates gloss dependency while preserving translation quality. We present BoostSLT, a context-aware framework for enhancing semantic consistency over long sign sequences without gloss supervision. Instead of requiring explicit gloss segmentation, BoostSLT introduces an Energy-Aware Temporal Segmentation (EAT-Seg) module that dynamically partitions videos into semantically coherent fragments, followed by a Diffusion-based Semantic Reconstruction (DSR) module that stitches and refines fragment-level translations into globally fluent paragraphs. The framework is plug-and-play and model-agnostic, seamlessly integrating with existing gloss-based or gloss-free pipelines across languages. Experiments on PHOENIX-2014T, CSL-Daily, and Auslan-Daily show consistent BLEU and ROUGE-L gains, confirming that diffusion-driven semantic reconstruction effectively bridges local accuracy and global coherence in long-form SLT. Code is available at github.com/K1sna/BoostSLT.
OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness
Authors: Phuc Nguyen; Anh N. Nhu; Ming C. Lin
Venue: CVPR 2026 (main)

Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectively estimates real-world-scale ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras, enabling robust trajectory dataset construction from rare driving events recorded in dashcam.Existing VO methods are trained on fixed observation frequency (e.g., 10Hz or 12Hz), completely overlooking temporal dynamics information. Many prior methods also require calibrated cameras with known intrinsic parameters. Consequently, their performance degrades when (1) deployed under unseen observation frequencies or (2) applied to uncalibrated cameras. These significantly limit their generalizability to many downstream tasks, such as extracting trajectories from dashcam footage.To address these challenges, OpenVO (1) explicitly encodes temporal dynamics information within a two-frame pose regression framework and (2) leverages 3D geometric priors derived from foundation models. We validate our method on three major autonomous-driving benchmarks - KITTI, nuScenes, and Argoverse 2 - achieving more than 20% performance improvement over state-of-the-art approaches. Under varying observation rate settings, our method is significantly more robust, achieving 46%-92% lower errors across all metrics.These results demonstrate the versatility of OpenVO for real-world 3D reconstruction and diverse downstream applications.
Learning and Aligning Click-Aware Shape Prior for Interactive Amodal Instance Segmentation
Authors: Junjie Chen; Junwei Lin; Ren Hong; Shengjie Liu; Yuming Fang; Feng Qian; Yifan Zuo
Venue: CVPR 2026 (main)

Abstract: Amodal instance segmentation aims to segment both visible and occluded regions of object instance, which are challenging due to lacking inference support under occlusion. Most existing methods employ the prior knowledge about object mask (shape prior) to support the amodal estimation, but the shape prior is not always compatible for object instances in the test stage. In this paper, we explore the task of interactive amodal segmentation, where a few user clicks are available for better segmenting the complete masks of object instances. For this task, we propose a novel framework based on learning and aligning click-aware shape prior (termed ClickPriorNet).  Specifically, we propose to learn click-aware shape prior with triplet loss, which forces the retrieved shape priors to have higher IoU with the ground-truth of target instance and thus could exactly facilitate the prediction. Besides, considering the inevitable mismatch between shape prior and target instance, we propose to adaptively align the shape prior with deformable attention. Overall, our model could make full use of the interactive clicks to retrieve and align shape priors, and thus could estimate more complete masks. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method. Code is available at https://github.com/chenbys/ClickPriorNet.
Hg-I2P: Bridging Modalities for Generalizable Image-to-Point-Cloud Registration via Heterogeneous Graphs
Authors: Pei An; Junfeng Ding; Jiaqi Yang; Yulong Wang; Jie Ma; Liangliang Nan
Venue: CVPR 2026 (main)

Abstract: Image-to-point-cloud (I2P) registration aims to align 2D images with 3D point clouds by establishing reliable 2D-3D correspondences. The drastic modality gap between images and point clouds makes it challenging to learn features that are both discriminative and generalizable, leading to severe performance drops in unseen scenarios. We address this challenge by introducing a heterogeneous graph that enables refining both cross-modal features and correspondences within a unified architecture. The proposed graph represents a mapping between segmented 2D and 3D regions, which enhances cross-modal feature interaction and thus improves feature discriminability. In addition, modeling the consistency among vertices and edges within the graph enables pruning of unreliable correspondences. Building on these insights, we propose a heterogeneous graph embedded I2P registration method, termed Hg-I2P. It learns a heterogeneous graph by mining multi-path feature relationships, adapts features under the guidance of heterogeneous edges, and prunes correspondences using graph-based projection consistency. Experiments on six indoor and outdoor benchmarks under cross-domain setups demonstrate that Hg-I2P significantly outperforms existing methods in both generalization and accuracy. Code is released on https://github.com/anpei96/hg-i2p-demo.
A Multi-Agent Perception-Action Alliance for Efficient Long Video Reasoning
Authors: Yichang Xu; Gaowen Liu; Ramana Rao Kompella; Tiansheng Huang; Sihao Hu; Fatih Ilhan; Selim Furkan Tekin; Zachary Yahn; Ling Liu
Venue: CVPR 2026 (main)

Abstract: This paper presents a multi-agent perception-action exploration alliance, dubbed A4VL, for efficient long-video reasoning. A4VL operates in a multi-round perception-action exploration loop with a selection of VLM agents. In each round,  the team of agents performs video question-answer (VideoQA) via perception exploration followed by action exploration.  During perception exploration, each agent learns to extract query-specific perception clue(s) from a few sampled frames and performs clue-based alignment to find the video block(s) that are most relevant to the query-specific event. During action exploration, A4VL performs video reasoning in three steps: (1) each agent produces its initial answer with rational, (2) all agents collaboratively scores one another through cross-reviews and relevance ranking, and (3) based on whether a satisfactory consensus is reached, the decision is made either to start a new round of perception-action deliberation by pruning (e.g., filtering out the lowest performing agent) and re-staging (e.g., new-clue and matching block based perception-action exploration), or to conclude by producing its final answer. The integration of the multi-agent alliance through multi-round perception-action exploration, coupled with event-driven partitioning and cue-guided block alignment, enables A4VL to effectively scale to real world long videos while preserving high quality video reasoning. Evaluation Results on five popular VideoQA benchmarks show that A4VL outperforms 18 existing representative VLMs and 11 recent methods optimized for long-video reasoning, while achieving significantly lower inference latency. Our code is released at https://github.com/git-disl/A4VL.
UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying
Authors: Chengyu Bai; Jintao Chen; Xiang Bai; Yilong Chen; Qi She; Ming Lu; Shanghang Zhang
Venue: CVPR 2026 (main)

Abstract: Recent advances in diffusion models and vision-language models (VLMs) have significantly enhanced the controllability of image editing. Methods like FlowEdit enable step-by-step editing along a visible, noise-free trajectory, where each intermediate result is a clear image, eliminating the need for full noise inversion. However, these approaches still operate in pixel space or VAE latent space, where intermediate outputs often suffer from visual artifacts, distortions, or unrealistic details--making reliable semantic evaluation difficult. Furthermore, they remain open-loop systems, applying static edits without feedback to guide or correct the editing process adaptively. We propose UniEdit-I, the first training-free, closed-loop image editing framework that operates entirely within the semantic latent space of a unified VLM by introducing an Understanding-Editing-Verifying (UEV) loop:(1) Understanding: parses the source image and editing instruction into a structured source prompt and a minimal target specification;(2) Editing: applies dynamic semantic offsets, with a configurable feedback weighting mechanism that adaptively modulates editing intensity based on real-time alignment feedback;(3) Verifying: leverages the VLM's own multimodal reasoning capability to evaluate the intermediate output along multiple semantic dimensions and trigger early stopping or refinement. By transforming the VLM from a post-hoc evaluator into an in-process conductor, UniEdit-I establishes the first semantics-driven, self-correcting closed-loop image editing pipeline. Evaluated on GEdit-Bench, UniEdit-I achieves state-of-the-art performance without any fine-tuning or architectural modifications, and even surpasses several large-scale pre-trained editors.
ZOO-Prune: Training-Free Token Pruning via Zeroth-Order Gradient Estimation in Vision-Language Models
Authors: Youngeun Kim; Youjia Zhang; Huiling Liu; Aecheon Jung; Sunwoo Lee; Sungeun Hong
Venue: CVPR 2026 (main)

Abstract: Large Vision-Language Models (VLMs) enable strong multimodal reasoning but incur heavy inference costs from redundant visual tokens. Token pruning alleviates this issue, yet existing approaches face limitations. Attention-based methods rely on raw attention scores, which are often unstable across layers and heads and can lead to redundant selections. Diversity-based methods improve robustness by selecting tokens far apart in feature space, but risk dropping regions needed for accurate prediction. We propose ZOO-Prune, a training-free framework built on the intuition that highly sensitive tokens have a stronger influence on the model's output and capture complementary visual cues rather than redundant ones. To achieve this, we estimate token sensitivity using zeroth-order perturbations at the lightweight projection layer. This measures how small random perturbations affect the projected features and enables efficient approximation of each token's influence without backpropagation. Extensive experiments across multiple VLMs and benchmarks show that ZOO-Prune consistently outperforms prior methods while pruning up to 94.4% of tokens without sacrificing accuracy. Our method also improves efficiency, reaching up to 2.30x faster end-to-end inference compared to the baseline. Code is available at https://aim-skku.github.io/ZOO-Prune.
OntoAug: Rethinking Generative Data Augmentation via Ontology Guidance
Authors: Shuo Wang; Zhichuan Wang; Jun Luo
Venue: CVPR 2026 (main)

Abstract: Generative data augmentation has created new opportunities for improving image recognition models. Despite these advances, most existing augmentation methods process images holistically, without accounting for the uneven distribution of discriminative information in classification tasks, where foreground subjects typically contain richer category-relevant signals than background context. Ignoring this imbalance can introduce unintended semantic shifts in generated samples, thereby weakening the model's ability to capture the intrinsic ontology of the target object. In fact, human perception operates in a similar manner: subject identity remains stable, while contextual variations are naturally tolerated as long as overall coherence is preserved. Inspired by this observation, we propose OntoAug, an ontology-oriented data augmentation framework that explicitly distinguishes between subject and environment. OntoAug separates foreground and background through structured layout control and guides diffusion models to generate samples with consistent subjects and diverse contextual variations. By explicitly introducing foreground masks and diverse background prompts during the generation process, the framework enhances both semantic fidelity and diversity in the synthesized data. Extensive experiments show that OntoAug achieves strong performance across multiple tasks, including image classification, few-shot learning, weakly supervised object localization (WSOL), and large vision-language model reasoning.
EnergyAction: Unimanual to Bimanual Composition with Energy-Based Models
Authors: Mingchen Song; Xiang Deng; Jie Wei; Dongmei Jiang; Liqiang Nie; Weili Guan
Venue: CVPR 2026 (main)

Abstract: Recent advances in unimanual manipulation policies have achieved remarkable success across diverse robotic tasks through abundant training data and well-established model architectures. However, extending these capabilities to bimanual manipulation remains challenging due to the lack of bimanual demonstration data and the complexity of coordinating dual-arm actions. Existing approaches either rely on extensive bimanual datasets or fail to effectively leverage pre-trained unimanual policies. To address this limitation, we propose EnergyAction, a novel framework that compositionally transfers unimanual manipulation policies to bimanual tasks through the Energy-Based Models (EBMs). Specifically, our method incorporates three key innovations. First, we model individual unimanual policies as EBMs and leverage their compositional properties to compose left and right arm actions, enabling the fusion of unimanual policies into a bimanual policy. Second, we introduce an energy-based temporal-spatial coordination mechanism through energy constraints, ensuring the generated bimanual actions are both temporal coherence and spatial feasibility. Third, we propose two different energy-aware denoising strategies that dynamically adapt denoising steps based on action quality assessment. These strategies ensure the generation of high-quality actions while maintaining superior computational efficiency compared to fixed-step denoising approaches. Experimental results demonstrate that EnergyAction effectively transfers unimanual knowledge to bimanual tasks, achieving superior performance on both simulated and real-world tasks with minimal bimanual data.
Immunizing Models Against Harmful Long-Horizon Fine-Tuning via Contractive Optimization Dynamics
Authors: Najibul Haque Sarker; Zaber Ibn Abdul Hakim; Ali Asgarov; Chia-Wei Tang; Alvi Md Ishmam; Chris Thomas
Venue: CVPR 2026 (main)

Abstract: Fine-tuning has become the default way to adapt powerful foundation models, but this also enables low-cost repurposing for harmful objectives. Existing immunization methods try to optimize local geometry or simulate short attacker horizons, and penalize observed loss drops. However, in practice, downstream tuners run thousands of updates and overcome these short-horizon defenses.In this paper, we propose CLAMP (Contractive Long-horizon Attacker Mitigation via Progress-bounding), an immunization method that traps harmful fine-tuning by shaping the attacker's optimization dynamics rather than only the initial landscape. Our key idea is to make harmful training locally contractive, making each update smaller than the last. This yields a closed-form bound on the attacker's training beyond the attacker's simulated training steps. We also introduce a Hessian-free directional curvature penalty, to create adversarial landscapes along harmful descent directions. Our bi-level objective minimizes the attacker's predicted improvement from train step zero to infinity. Experiments show our method withstands long-horizon fine-tuning across classification, generative, and autoregressive settings, substantially reduces harmful task adaptation, while preserving benign utility and fine-tuneability.
Chain-of-Thought Guided Multi-Modal Object Re-Identification
Authors: Ya Gao; Shihao Li; Zhaojun Liu; Aihua Zheng; Chenglong Li; Jin Tang
Venue: CVPR 2026 (main)

Abstract: With the rise of visual-language models, multi-modal ReID retrieves specific targets by integrating different spectra and textual descriptions. Existing methods merely adopt descriptive representation learning for image-text, ignoring the relationships among the intrinsic logical hierarchies of semantic features. Since Chain-of-Thought (CoT) can provide textual logical context and enhance semantic perception in large-model reasoning, we propose CoT-ReID, a CoT-guided framework that injects the Multi-modal Large Language Models (MLLMs) reasoning into multi-modal ReID. Specifically, we simulate the joint visual-textual logical decision-making of human reasoning, leveraging CoT textual logical reasoning to guide visual feature learning at the early, late, and decision-making level: At the early level, we embed the semantic reversion of CoT hierarchical reasoning into visual features to calibrate bottom-level features and emphasize visual hierarchical reasoning. Next, we take CoT hierarchical reasoning text as an anchor condition to constrain the consistency of visual cross-modal semantics. Finally, through the hierarchical reasoning process of CoT, we embed logically reasoned text attribute features into multi-modal decision-making, providing logical support for selecting discriminative identity features. By constructing CoT textual benchmarks and our proposed modules, our framework generates more robust multi-modal features in complex scenarios. Comprehensive experiments on four datasets (RGBNT100, MSVR310, WMVeID863, RGBNT201) demonstrate that our method outperforms existing approaches. Code will be released upon acceptance.
MimicTalker: A Multimodal Interactive and Memory-Enhanced Framework for Real-Time Dyadic 3D Head Generation
Authors: Yinuo Wang; Yanbo Fan; Xuan Wang; Boyao Zhou; Yu Guo; Yujun Shen; Fei Wang
Venue: CVPR 2026 (main)

Abstract: Dyadic interactive head generation aims to synthesize realistic head motions that respond both verbally and non-verbally to an interlocutor in real-time conversation. The existing works often focus on offline scenarios, and struggle with a shallow understanding of the multimodal conversational context while also lacking long-term coherence. To address these limitations, we propose MimicTalker, a novel method for producing real-time, contextually-aware, and long-term consistent interactive head motions. To this end, we propose a Multimodal Interactive Context Extraction (MICE) module to capture both instantaneous and long-term multimodal interactive information from the interlocutor. To enhance in-depth conversational understanding, we propose a Semantic-enhanced Dynamic Interaction (SDI) module to integrate the intentions and topics of the conversation, which are automatically extracted through an LLM-based analyzer. Further, we propose a semantic-guided Motion Style Memory (MSM) mechanism, enabling the long-term motion consistency throughout the conversation. We conduct experiments on both short conversational segments (25 seconds) and extended dialogues (6 minutes), and the comprehensive experiments demonstrate that our method significantly outperforms existing approaches.
S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs
Authors: Yuzhou Ji; Qijian Tian; He Zhu; Xiaoqi Jiang; Guangzhi Cao; Lizhuang Ma; Yuan Xie; Xin Tan
Venue: CVPR 2026 (main)

Abstract: Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS) each suffer from non-photorealistic rendering and significant degradation under sparse inputs. In this paper, we introduce Sparse to Dense lifting (S2D), a novel pipeline that bridges the two representations and achieves high-quality 3DGS reconstruction with minimal inputs. Specifically, the S2D lifting is two-fold. We first present an efficient one-step diffusion model that lifts sparse point cloud for high-fidelity image artifact fixing. Meanwhile, to reconstruct 3D consistent scenes, we also design a corresponding reconstruction strategy with random sample drop and weighted gradient for robust model fitting from sparse input views to dense novel views. Extensive experiments show that S2D achieves the best consistency in generating novel view guidance and first-tier sparse view reconstruction quality under different input sparsity. By reconstructing stable scenes with the least possible captures among existing methods, S2D enables minimal input requirements for 3DGS applications.
From Rays to Projections: Better Inputs for Feed-Forward View Synthesis
Authors: Zirui Wu; Zeren Jiang; Martin R. Oswald; Jie Song
Venue: CVPR 2026 (main)

Abstract: Feed-forward view synthesis models predict a novel view in a single pass with minimal 3D inductive bias. Existing works encode cameras as Plucker ray maps, which tie predictions to the arbitrary world coordinate gauge and make them sensitive to small camera transformations, thereby undermining geometric consistency. In this paper, we ask what inputs best condition a model for robust and consistent view synthesis. We propose projective conditioning, which replaces raw camera parameters with a target-view projective cue that provides a stable 2D input. This reframes the task from a brittle geometric regression problem in ray space to a well-conditioned target-view image-to-image translation problem. Additionally, we introduce a masked autoencoding pretraining strategy tailored to this cue, enabling the use of large-scale uncalibrated data for pretraining. Our method shows improved fidelity and stronger cross-view consistency compared to ray-conditioned baselines on our view-consistency benchmark. It also achieves state-of-the-art quality on standard novel view synthesis benchmarks.