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Chengjie Huang, Guile Wu, Dongfeng Bai, Bingbing Liu

Existing 3D point tracking methods mostly rely on heuristic designs or scene reconstruction, which incurs significant computational overhead and makes it difficult to meet the demands of real-time applications. To address this problem, in this work, we present a novel spatial tracker that leverages a feed-forward visual geometry transformer to predict the trajectories of arbitrary query points from monocular videos in real time. Specifically, we employ a query initialization mechanism to maintain and update a global feature vector and a set of frame-level feature vectors for each query point. Then, we propose a new spatial tracking framework, which consists of a visual geometry transformer backbone, a global embedding branch, a frame-level embedding branch, and a tracking head. The key innovation lies in the dual-branch embedding design, where the global embedding branch integrates geometry-grounded features of the entire video into global query features to optimize track information across the entire sequence and the frame-level branch combines geometry-grounded features of each respective frame into frame-level query features to refine fine-grained track coordinate predictions. Furthermore, to facilitate collaboration between the global branch and the frame-level branch, we introduce an interaction module which enables unidirectional or bidirectional information exchange between the global query features and frame-level query features. Extensive experiments on various point tracking benchmark datasets show that our approach achieves significantly fast spatial tracking speed compared with state-of-the-art methods, while maintaining comparable tracking accuracy.

Quanyu Zhang, Zhongyi Han, Hao Sun, Yongshun Gong, Xiaoyan Wang, Yilong Yin, Shuo Li

Pretraining on large-scale data followed by fine-tuning has become a standard paradigm for visual models. However, noise in the pretraining data can be absorbed by the model and carried into downstream tasks, causing catastrophic inheritance. Prior studies mainly link this issue to changes in the feature spectrum, arguing that noise reduces the strength of key feature components. Following this view, they aim to improve transferability by amplifying these components. However, these approaches focus only on spectral energy and implicitly assume that the feature directions remain fixed, which does not hold in practice. In this work, we revisit this view and reveal an overlooked effect: even mild pretraining noise can cause a clear rotation of the dominant feature subspace, despite negligible spectral energy degradation. To quantitatively characterize this phenomenon, we propose using the Principal Directional Angle (PDA) to measure the directional shift between the clean and noisy models. Building on this observation, we introduce the Feature Geometry Stabilization (FGS) framework, which aims to counteract the subspace rotation revealed by PDA by enhancing the geometric stability of the feature space through the synergistic interaction of perturbation consistency, variance-activation regularization, and feature consistency distillation. Experiments across multiple visual benchmarks demonstrate the effectiveness of FGS and verify the importance of stabilizing feature geometry to mitigate catastrophic inheritance.

Qihong Tang, Changhan Liu, Shaofeng Zhang, Wenbin Li, Qi Fan, Yang Gao

Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by relying on exemplar images, predefined categories, or textual descriptions. However, their reliance on image and text prompts often limits flexibility, restricting adaptability in real-world scenarios. In this paper, we introduce a novel Prompt-Free Universal Region Proposal Network (PF-RPN), which identifies potential objects without relying on external prompts. First, the Sparse Image-Aware Adapter (SIA) module performs initial localization of potential objects using a learnable query embedding dynamically updated with visual features. Next, the Cascade Self-Prompt (CSP) module identifies the remaining potential objects by leveraging the self-prompted learnable embedding, autonomously aggregating informative visual features in a cascading manner. Finally, the Centerness-Guided Query Selection (CG-QS) module facilitates the selection of high-quality query embeddings using a centerness scoring network.Our method can be optimized with limited data (e.g., 5% of MS COCO data) and applied directly to various object detection application domains for identifying potential objects without fine-tuning, such as underwater object detection, industrial defect detection, and remote sensing image object detection. Experimental results across 19 datasets validate the effectiveness of our method.

Yiheng Dong, Yi Lin, Shilong Huang, Xiyan Yang, Xin Yang

Automatic radiology report generation (RRG) aims to translate medical images into diagnostic text, reducing radiologists' workload and standardizing clinical documentation. Nonetheless, existing approaches mainly focus on single-time point analysis and fail to capture temporal disease evolution across longitudinal examinations. While recent longitudinal RRG (LRRG) approaches incorporate historical data, they often combine images from different time points within a single representation space, leading to blurred semantics and inconsistent temporal reasoning. In this work, we propose a Temporal Decoupling with Iterative MutualRefinement Model (TIM), a two-stage framework that explicitly decouples spatial pathology from temporal progression and iteratively refines reports through mutual feedback. Stage I performs temporal-decoupled representation learning, separating temporal evolution patterns from disease-specific features and generating radiology reports for both prior and current studies. Stage II introduces a mutual report refinement mechanism that identifies diagnostic inconsistencies within prior reports and iteratively rectifies both prior and current reports through error-sensitive feedback. Experiments on the Longitudinal-MIMIC dataset demonstrate that TIM surpasses existing single-image and longitudinal baselines, achieving new state-of-the-art performance across both language and clinical metrics. Code is available at https://github.com/yihengd/TIM.

Chuang Peng, Renshuai Tao, Zhongwei Ren, Xianglong Liu, Yunchao Wei

Automatic X-ray prohibited items detection is vital for security inspection and has been widely studied. Traditional methods rely on visual modal, often struggling with complex threats. While recent studies incorporate language to guide single-view images, human inspectors typically use dual-view images in practice. This raises the question: can the second view provide constraints similar to a language modality? In this work, we introduce DualXrayBench, the first comprehensive benchmark for X-ray inspection that includes multiple views and modalities. It supports eight tasks designed to test cross-view reasoning. In DualXrayBench, we introduce a dual-view caption corpus consisting of 45,613 dual-view images across 12 categories with corresponding captions. Building upon these data, we propose the Geometric (cross-view)-Semantic (cross-modality) Reasoner (GSR), a multimodal model that jointly learns correspondences between cross-view geometry and cross-modal semantics, treating the second-view images as a "language-like modality". To enale this, we construct the GSXray dataset, with structured Chain-of-Thought sequences: top , side, conclusion. Comprehensive evaluations on DualXrayBench demonstrate that GSR achieves significant improvements across all X-ray tasks, offering a new sight for real-world X-ray inspection.

Ziqiao Meng, Qichao Wang, Zhiyang Dou, Zixing Song, Zhipeng Zhou, Irwin King, Peilin Zhao

Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias reinforces short-range continuity but limits the model's ability to capture long-range dependencies, thereby weakening its capacity to enforce global structural properties such as symmetry, geometric consistency, and large-scale spatial regularities. Inspired by the level-of-detail (LoD) principle in shape modeling, we propose PointNSP, a coarse-to-fine generative framework that preserves global shape structure at low resolutions and progressively refines fine-grained geometry at higher scales through a next-scale prediction paradigm. This multi-scale factorization aligns the autoregressive objective with the permutation-invariant nature of point sets, enabling rich intra-scale interactions while avoiding brittle fixed orderings. Strictly following the baseline experimental setups, empirical results on ShapeNet benchmark demonstrate that PointNSP achieves state-of-the-art (SoTA) generation quality for the first time within the autoregressive paradigm. Moreover, it surpasses strong diffusion-based baselines in parameter, training, and inference efficiency. Finally, under dense generation with 8,192 points, PointNSP's advantages become even more pronounced, highlighting its strong scalability potential.

Yawen Yang, Feng Li, Shuqi Kong, Yunfeng Diao, Xinjian Gao, Zenglin Shi, Meng Wang

Recent rapid advancement of generative models has significantly improved the fidelity and accessibility of AI-generated synthetic images. While enabling various innovative applications, the unprecedented realism of these synthetics makes them increasingly indistinguishable from authentic photographs, posing serious security risks, such as media credibility and content manipulation. Although extensive efforts have been dedicated to detecting synthetic images, most existing approaches suffer from poor generalization to unseen data due to their reliance on model-specific artifacts or low-level statistical cues. In this work, we identify a previously unexplored distinction that real images maintain consistent semantic attention and structural coherence in their latent representations, exhibiting more stable feature transitions across network layers, whereas synthetic ones present discernible distinct patterns. Therefore, we propose a novel approach termed latent transition discrepancy (LTD), which captures the inter-layer consistency differences of real and synthetic images. LTD adaptively identifies the most discriminative layers and assesses the transition discrepancies across layers. Benefiting from the proposed inter-layer discriminative modeling, our approach exceeds the base model by 14.35% in mean Acc across three datasets containing diverse GANs and DMs. Extensive experiments demonstrate that LTD outperforms recent state-of-the-art methods, achieving superior detection accuracy, generalizability, and robustness.

Yongli Xiang, Ziming Hong, Zhaoqing Wang, Xiangyu Zhao, Bo Han, Tongliang Liu

Text-to-Image (T2I) diffusion models have demonstrated significant advancements in generating high-quality images, while raising potential safety concerns regarding harmful content generation. Safety-guidance-based methods have been proposed to mitigate harmful outputs by steering generation away from harmful zones, where the zones are averaged across multiple harmful categories based on predefined keywords. However, these approaches fail to capture the complex interplay among different harm categories, leading to "harmful conflicts" where mitigating one type of harm may inadvertently amplify another, thus increasing overall harmful rate. To address this issue, we propose Conflict-aware Adaptive Safety Guidance (CASG), a training-free framework that dynamically identifies and applies the category-aligned safety direction during generation. CASG is composed of two components: (i) Conflict-aware Category Identification (CaCI), which identifies the harmful category most aligned with the model's evolving generative state, and (ii) Conflict-resolving Guidance Application (CrGA), which applies safety steering solely along the identified category to avoid multi-category interference. CASG can be applied to both latent-space and text-space safeguards. Experiments on T2I safety benchmarks demonstrate CASG's state-of-the-art performance, reducing the harmful rate by up to 15.4% compared to existing methods.

Yuchen Zou, Huikai Shao, Lihuang Fang, Zhipeng Xiong, Dexing Zhong

Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic data must reflect the diversity of real palmprints, including both style variation and geometric variation. However, existing palmprint generation methods mainly focus on style translation, while geometric variation is either ignored or approximated by simple handcrafted augmentations. In this work, we propose FlowPalm, an optical-flow-driven palmprint generation framework capable of simulating the complex non-rigid deformations observed in real palms. Specifically, FlowPalm estimates optical flows between real palmprint pairs to capture the statistical patterns of geometric deformations. Building on these priors, we design a progressive sampling process that gradually introduces the geometric deformations during diffusion while maintaining identity consistency. Extensive experiments on six benchmark datasets demonstrate that FlowPalm significantly outperforms state-of-the-art palmprint generation approaches in downstream recognition tasks. Project page: https://yuchenzou.github.io/FlowPalm/

Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li 等

We present **ReViSe** (_**Re**asoning with **Vi**deo **S**parsity_), a framework that combines multi-round reasoning with adaptive frame selection for video question answering (VQA). Existing vision-language models (VLMs) uniformly sample video frames, which introduces redundancy or irrelevancy. In contrast, ReViSe*interactively selects informative frames through multi-round reasoning. To achieve this, ReViSe includes three modules: a multi-round conversation module that retains frame selection history as memory; a reasoning tracer that maintains a chain-of-thought across rounds; and a self-correction mechanism that enforces structural and behavioral validity. ReViSe integrates seamlessly with both proprietary and open-source VLMs. It supports proprietary models in a "plug-and-play" manner and enables reinforcement fine-tuning for open-source models. Experiments on multiple VQA benchmarks show that **ReViSe** improves the video understanding ability of VLMs by improving accuracy while reducing the number of frames used.

Baoteng Li, Xianghao Zang, Xinran Wang, Xiangyu Na, Zhixiang He, Hao Sun, Chi Zhang, Zhongjiang He, Tianwei Cao, Kongming Liang 等

Text-to-Image (T2I) generation has achieved remarkable progress in recent years. Meanwhile, reinforcement learning methods, particularly those based on Group Relative Policy Optimization (GRPO), have attracted widespread attention and been successfully applied to T2I tasks. However, the uniform sampling strategy commonly used during training often ignores the match between sample difficulty and the model's current learning capability, leading to low training efficiency. We argue that improving training efficiency requires continuously prioritizing prompts that match the model's evolving capability and remain actively learnable. To this end, we propose Curriculum Group Policy Optimization (CGPO), an adaptive curriculum training framework. During training, each prompt produces a group of images scored by a reward model. We use the variance of group rewards as an online proxy for prompt inconsistency. A higher variance suggests that the model has partially captured the prompt requirements but has not yet achieved stable mastery. Such prompts are more likely to provide useful learning signals, so we increase their sampling probabilities accordingly. Additionally, to address data imbalance in multi-category datasets, we design a category calibration method based on proportional fairness optimization, which balances training difficulty across categories. Experiments on GenEval, T2I-CompBench++, and DPG Bench demonstrate that our framework effectively improves generation performance.

Jianting Chen, Dianzhi Yu, Irwin King

Incremental learning (IL) arises from the need to continuously update models under limited data and computational resources. Most existing IL studies focus on data-scarce settings. They often develop complex methods that rely on heavy computation, while overlooking the computational resource constraints common in real-world scenarios. This motivates us to formalize the problem of Computational Resource-Aware Incremental Learning, which explicitly considers the computational budget during model training. To tackle this problem, we propose Smart Replay, an efficient memory rehearsal algorithm that adaptively allocates resources by scheduling the replay ratio across mini-batches. We cast replay-ratio optimization into an optimal control formulation that jointly minimizes new-task and memory losses. We further propose a heuristic Q-function to guide ratio adjustments, adaptively balancing short-term efficiency and long-term stability. Finally, we develop a practical algorithm that periodically updates the replay ratio during training. Experiments on multiple benchmarks validate that Smart Replay consistently outperforms fixed-replay baselines, achieving higher accuracy and lower forgetting under the same computational budget.

S Divakar Bhat, Amit Popat More, Mudit Soni, Bhuvan Aggarwal

Long-Tail Class Incremental Learning (LTCIL) combines two fundamental challenges: catastrophic forgetting of past tasks and severe class imbalance. Existing approaches mitigate one challenge at a time, through rehearsal, reweighting, or classifier alignment, but they typically assume static priors and rely on multi-stage training. In contrast, we propose AdaPrior, a simple Bayesian framework that treats LTCIL as a problem of dynamic prior misalignment. Our key idea is to estimate model-induced priors online via an exponential moving average and use them for (i) debiasing during training (AdaPrior Loss), and (ii) lightweight post-hoc correction at inference. The combined approach unifies loss-level and inference-level debiasing without additional stages or heavy computation. We provide theoretical analysis showing that AdaPrior's prior estimator converges to the true model prior and that its logit adjustment yields well calibrated posteriors under mild assumptions. Extensive experiments on CIFAR100-LT, Food-101-LT, ImageNet-LT-subset, and iNaturalist18-subset demonstrate consistent gains over recent LTCIL baselines. Beyond accuracy, AdaPrior improves calibration, and forgetting curves, making it a practical and scalable solution for long-tail continual learning.

Van Thanh Le, Yun Fu

While open-sourcing instruction-guided image editing models accelerates research, it surrenders control over their capabilities to anyone who downloads the weights. Existing protection methods are reactive: they verify ownership after generation, but the underlying model remains fully functional for unauthorized users. We introduce Visilock, where access control is baked into model weights, rendering the model unusable without a visual trigger in the input. The challenge is training a model that retains editing capability for authorized input and remains unusable for unauthorized input, without destabilizing training. Naive multi-task objectives create gradient conflicts that collapse training, while contrastive approaches like FMLock destroy the denoising manifold. We develop Dual Score Distillation, a dual-teacher framework where a degraded teacher defines locked behavior and an original teacher guides editing quality, eliminating gradient interference through separate frozen targets. A key risk is that released models could be unlocked through post-hoc fine-tuning. To prevent this, we initialize the student model from the degraded teacher so that it begins in a locked state, and only regains editing ability for authorized inputs via distillation. This impedes adversarial fine-tuning from recovering full editing capability. Evaluation on InstructPix2Pix shows authorized edits maintain baseline quality (CLIP-I: 0.821, DINO: 0.726) while unauthorized attempts degrade substantially (CLIP-I: 0.481, DINO: 0.072) with 41% and 90% drops in image and semantic similarity. The lock remains robust to key corruptions, spatial perturbations, and adversarial unlock fine-tuning. Code will be available at https://github.com/Luvata/VisiLock.

Zixuan Huang, Xiang Li, Zhaoyang Lv, James M. Rehg

Videos are continuous 2D projections of 3D worlds. After training on large video data, will global 3D understanding naturally emerge? We study this by quantifying the 3D understanding of existing Video Foundation Models (VidFMs) pretrained on vast video data. We propose the first model-agnostic framework that measures the 3D awareness of various VidFMs by estimating multiple 3D properties from their features via shallow read-outs. Our study presents meaningful findings regarding the 3D awareness of VidFMs on multiple axes. In particular, we show that state-of-the-art video generation models exhibit a strong understanding of 3D objects and scenes, despite not being trained on any 3D data. Such understanding can even surpass that of large expert models specifically trained for 3D tasks. Our findings, together with the 3D benchmarking of major VidFMs, provide valuable observations for building scalable 3D models.

Jisoo Kim, Heeseok Oh

Diffusion-based text-to-image (T2I) models have enabled remarkable generative capabilities, yet precise text-based image editing that preserves the original's structural and perceptual fidelity remains non-trivial. Existing approaches either rely on retraining with large bespoke datasets, incurring significant computational and curation costs, or adopt lightweight fine-tuning strategies that still require optimization and often fail in fine-grained or semantically complex edits. We propose NEAF (Natural image Editing with Attention Fusion), a novel zero-shot, universal tuning-free framework for arbitrary T2I models, obviating the need for dataset curation or retraining. NEAF introduces a lightweight, learnable XA-Conductor module that dynamically identifies salient cross-attention contributions pertinent to the edit. This module optimizes a weight vector to orchestrate an adaptive fusion of cross-attention maps derived from the source, edited, and reconstruction branches. This triadic-feedback optimization strategy ensures the precise instantiation of user directives while rigorously preserving the fidelity of quiescent regions. Extensive experiments validate NEAF as a flexible and general framework that consistently surpasses existing methods across diverse editing tasks, demonstrating particular dominance in complex, non-rigid editing scenarios where other approaches falter.

Mengzhu xu, Hanzhi Liu, Ningkang Peng, Qianyu Chen, Canran Xiao

Continual learning for video--language understanding is increasingly important as models face non-stationary data, domains, and query styles, yet prevailing solutions blur what should stay stable versus what should adapt, rely on static routing/capacity, or require replaying past videos. We aim to explicitly specify where stability lives and where plasticity should be focused under realistic memory and privacy constraints. We introduce Affordance-First Decomposition (AFD): videos are mapped to slowly varying affordance tokens that form a shared, time-aligned substrate, while a lightweight, query-routed, conflict-aware scheduler concentrates adaptation and grows capacity only when needed. The substrate is stabilized via weak alignment and teacher consistency, and training uses question-only replay. AFD achieves state-of-the-art across protocols: 51.6% average accuracy with -1.8% forgetting on domain-incremental VideoQA, ViLCo R@1@0.5 of 29.6% (MQ) and 20.7% (NLQ) with 18.4% stAP@0.25 (VQ), and 39.5% accuracy with -1.6% forgetting on time-incremental iVQA. Overall, AFD offers an explicit, interpretable split between a stable interaction-centered substrate and targeted adaptation.

Yucheng Liao, Jiajun Liang, Kaiqian Cui, Baoquan Zhao, Haoran Xie, Wei Liu, Qing Li, Xudong Mao

Instruction-based image editing through natural language has emerged as a powerful paradigm for intuitive visual manipulation. While recent models achieve impressive results on single edits, they suffer from severe quality degradation under multi-turn editing. Through systematic analysis, we identify progressive loss of high-frequency information as the primary cause of this quality degradation. We present FreqEdit, a training-free framework that enables stable editing across 10+ consecutive iterations. Our approach comprises three synergistic components: (1) high-frequency feature injection from reference velocity fields to preserve fine-grained details, (2) an adaptive injection strategy that spatially modulates injection strength for precise region-specific control, and (3) a path compensation mechanism that periodically recalibrates the editing trajectory to prevent over-constraint. Extensive experiments demonstrate that FreqEdit achieves superior performance in both identity preservation and instruction following compared to seven state-of-the-art baselines.

Peixuan Zhang, Zijian Jia, Kaiqi Liu, Shuchen Weng, Si Li, Boxin Shi

While recent advancements in generative models have achieved remarkable visual fidelity in video synthesis, creating coherent multi-shot narratives remains a significant challenge. To address this, keyframe-based approaches have emerged as a promising alternative to computationally intensive end-to-end methods, offering the advantages of fine-grained control and greater efficiency. However, these methods often fail to maintain cross-shot consistency and capture cinematic language. In this paper, we introduce STAGE, a SToryboard-Anchored GEneration workflow to reformulate the keyframe-based multi-shot video generation task. Instead of using sparse keyframes, we propose STEP^2 to predict a structural storyboard composed of start-end frame pairs for each shot. We introduce the multi-shot memory pack to ensure long-range entity consistency, the dual-encoding strategy for intra-shot coherence, and the two-stage training scheme to learn cinematic inter-shot transition. We also contribute the large-scale ConStoryBoard dataset, including high-quality movie clips with fine-grained annotations for story progression, cinematic attributes, and human preferences. Extensive experiments demonstrate that STAGE achieves superior performance in structured narrative control and cross-shot coherence. Our code will be available at this url.

Dong Wei, Huaijiang Sun, Fan Liu, Yuhui Zheng

Many recent human motion prediction methods adopt a multi-stage refinement framework, where each stage produces an initial guess of future poses for the next stage. These guesses are progressively refined towards the target prediction through a sequence of spatial-temporal reasoning stages.However, such a cascaded design incurs large computation and memory overheads that grow at least linearly with network depth, and lack an explicit stopping criteria.In this paper, we propose MotionDEQ, a deep equilibrium motion predictor that reformulates progressive guessing paradigm as a fixed point problem within an implicit layer. This formulation is conceptually equivalent to performing infinitely many refinement steps, but requires only O(1) training memory and can be solved efficiently through any black-box solvers. We carefully design this implicit refinement process by integrating Euclidean geometric transformations into equilibrium learning, allowing the entire network to be equivariant. We also find DEQs naturally fit the real-world scenario where motion data comes streamingly: the converged fixed point can be reused as a warm initial guess, to help recycle the redundant inference computation when making subsequent predictions.Our experiments demonstrate that MotionDEQ achieves the state-of-the-art prediction performances with superior memory efficiency, using fewer than 300K parameters with 55.3mm prediction error at 400ms on the Human3.6M dataset.