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Yuxuan Liang, Fan Shi, Rui Zhu, Xu Li, Xiaolei Chen, Zhe Liu, Bin Li, Xiangyang Xue

Large Vision-Language Models (LVLMs) often hallucinate when visual evidence conflicts with world knowledge, i.e., in counterfactual scenarios. We propose Envision-Attend-Respond (EnAR), a training-free framework that leverages visual priors to steer the model's attention toward counterfactual elements in the image. The Envision stage constructs a visual impression by invoking a diffusion prior to perform latent perturbations, yielding a prior-consistent counterpart of the input image. The Attend stage processes the original image and its visual impression through the LVLM's vision encoder to localize counterfactual elements, forming a corresponding padded input. The Respond stage performs contrastive decoding between the original and padded inputs to suppress bias and enhance visual understanding. Empirically, EnAR consistently mitigates hallucinations and improves response fidelity, achieving a 10.82% gain on VLMBias and an average 6.9% improvement on POPE, demonstrating robustness across both counterfactual and general hallucination settings. Moreover, the framework remains effective across heterogeneous LVLM architectures, offering a new perspective for hallucination governance in multimodal reasoning.

Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang, Xilin Zhao, Xiaochun Cao, Qingming Huang

This paper investigates the challenging task of detecting backdoored text-to-image models under black-box settings and introduces a novel detection framework BlackMirror. Existing approaches typically rely on analyzing image-level similarity, under the assumption that backdoor-triggered generations exhibit strong consistency across samples. However, they struggle to generalize to recently emerging backdoor attacks, where backdoored generations can appear visually diverse. BlackMirror is motivated by an observation: across backdoor attacks, only partial semantic patterns within the generated image are steadily manipulated, while the rest of the content remains diverse or benign. Accordingly, BlackMirror consists of two components: MirrorMatch, which aligns visual patterns with the corresponding instructions to detect semantic deviations; and MirrorVerify, which evaluates the stability of these deviations across varied prompts to distinguish true backdoor behavior from benign responses. BlackMirror is a general, training-free framework that can be deployed as a plug-and-play module in Model-as-a-Service (MaaS) applications. Comprehensive experiments demonstrate that BlackMirror achieves accurate detection across a wide range of attacks. Code is available at this https://github.com/Ferry-Li/BlackMirror.

Ji Ma, Wei Suo, Peng Wang, Yanning Zhang

Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process.However, visual attention decay is a well-studied problem in Large Vision-Language Models (LVLMs). Considering the fundamental differences in reasoning processes between MCoT models and traditional LVLMs, we raise a basic question: Whether MCoT models have unique causes of hallucinations? To answer this question, we systematically investigate the hallucination patterns of MCoT models and find that fabricated texts are primarily generated in associative reasoning steps, which we term divergent thinking. Leveraging these insights, we introduce a simple yet effective strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate hallucinations. Extensive experiments show that our method outperforms existing methods by a large margin. More importantly, our proposed method can be conveniently integrated with other hallucination mitigation methods and further boost their performance. The code is publicly available at https://github.com/ASGO-MM/MCoT-hallucination.

Weixin Ye, Hongguang Zhu, Wei Wang, Yahui Liu, Mengyu Wang, Xuecheng Nie

Text-to-image (T2I) diffusion models have made significant strides in generating high-quality images. However, progressively manipulating certain attributes of generated images to meet the desired user expectations remains challenging, particularly for content with rich details, such as human faces. Some studies have attempted to address this by training slider modules. However, they follow a One-for-One manner, where an independent slider is trained for each attribute, requiring additional training whenever a new attribute is introduced. This not only results in parameter redundancy accumulated by sliders but also restricts the flexibility of practical applications and the scalability of attribute manipulation. To address this issue, we introduce the All-in-One Slider, a lightweight module that decomposes the text embedding space into sparse, semantically meaningful attribute directions. Once trained, it functions as a general-purpose slider, enabling interpretable and fine-grained continuous control over various attributes. Moreover, by recombining the learned directions, the All-in-One Slider supports zero-shot manipulation of unseen attributes (e.g., races and celebrities) and the composition of multiple attributes. Extensive experiments demonstrate that our method enables accurate and scalable attribute manipulation, achieving notable improvements compared to previous methods. Furthermore, our method can be extended to integrate with the inversion framework to perform attribute manipulation on real images, broadening its applicability to various real-world scenarios. The code and trained model will be released.

Tao Li, Xingran Liao, Mingliang Zhou

The development of AI-generated technology requires effective image quality assessment (AGIQA) methods to jointly evaluate visual quality and text-content alignment, ensuring that the generated content is both visually appealing and faithful to the user's instructions. Nevertheless, visual degradation and text-content misalignment often coincide, and it is difficult to tell whether a bad subjective evaluation arises from prompt noncompliance or rendering artifacts. As such, disentangling image content and rendering distortions is vital. We propose the dual-prior guided fusion network (DPGF-Net), which leverages image-side priors to disentangle distortions from content and combines them with text-side prompt templates to simulate their interactions, to address this issue. DPGF-Net employs a local text-conditioned aggregation branch to highlight semantically relevant and quality-sensitive regions in conjunction with a global modulation branch that captures holistic perceptual characteristics. Finally, adaptive fusion produces a single score. Experiments on three AGIQA datasets demonstrate that our method is highly correlated with human judgments, with lower prediction error and stable evaluation behavior. The source code is available at https://github.com/leeto221/AGIQA.

Rui Liu, Dian Yu, Lei Ke, Haolin Liu, Yujun Zhou, Zhenwen Liang, Haitao Mi, Pratap Tokekar, Dong Yu

Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalent group-based algorithms such as GRPO require multi-rollout sampling for each prompt. While more efficient single-rollout variants have recently been explored in text-only settings, we find that they suffer from severe instability in multimodal contexts, often leading to training collapse. To address this sample efficiency-stability trade-off, we introduce MSSR (Multimodal Stabilized Single-Rollout), a group-free RLVR framework that achieves both stable optimization and effective multimodal reasoning performance. MSSR achieves this via an entropy-based advantage-shaping mechanism that adaptively regularizes advantage magnitudes, preventing collapse and maintaining training stability. While such mechanisms have been used in group-based RLVR, we show that in the multimodal single-rollout setting they are not merely beneficial but essential for stability. In in-distribution evaluations, MSSR demonstrates superior rollout sample efficiency, achieving similar validation accuracy with half the training steps. When trained for the same number of steps, MSSR's performance surpasses the group-based baseline and shows consistent generalization improvements across five diverse reasoning-intensive benchmarks. Together, these results demonstrate that MSSR enables stable, sample-efficient, and effective RLVR for complex multimodal reasoning tasks.

Kai Ye, Qingtao Pan, Shuo Li

Large language models (LLMs) need reliable test-time control of hallucinations. Existing conformal methods for LLMs typically provide only marginal guarantees and rely on a single global threshold, which can under-cover hard prompts, over-cover easy ones, and produce oversized prediction sets. We propose Conditional Factuality Control (CFC), a post-hoc conformal framework that returns set-valued outputs with conditional coverage guarantees. CFC defines a continuous, feature-conditional acceptance threshold through augmented quantile regression on a latent "success" score, and deploys it through a fixed-point threshold rule at inference time. Theoretically, we show that CFC satisfies a conditional coverage guarantee under exchangeability and analyze its efficiency, proving that, under mild assumptions on the score distributions, the conditional rule is strictly more sample-efficient than marginal conformal prediction at the same target coverage. We further derive a PAC-style variant, CFC-PAC, which shrinks the nominal risk level based on a stability bound, yielding a finite-sample certificate that the conditional miscoverage deviates from the target by at most a slack. Empirically, on synthetic data, real-world reasoning and QA benchmarks, and a Flickr8k VLM setting, CFC and CFC-PAC consistently attain near-target coverage across difficulty groups while using smaller prediction sets than CP and non-CP baselines.

Zekun Li, Sizhe An, Chengcheng Tang, Chuan Guo, Ivan Shugurov, Linguang Zhang, Amy Zhao, Srinath Sridhar, Lingling Tao, Abhay Mittal

Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of models that unify motion-language generation and understanding remains largely underexplored. Existing approaches often fine-tune large language models (LLMs) on paired motion-text data, which can result in catastrophic forgetting of linguistic capabilities due to the limited scale of available text-motion pairs. Furthermore, prior methods typically convert motion into discrete representations via quantization to integrate with language models, introducing substantial jitter artifacts from discrete tokenization. To address these challenges, we propose LLaMo, a unified framework that extends pretrained LLMs through a modality-specific Mixture-of-Transformers (MoT) architecture. This design inherently preserves the language understanding of the base model while enabling scalable multimodal adaptation. We encode human motion into a causal continuous latent space and maintain the next-token prediction paradigm in the decoder-only backbone through a lightweight flow-matching head, allowing for streaming motion generation in real-time (>30 FPS). Leveraging the comprehensive language understanding of pretrained LLMs and large-scale motion-text pretraining, our experiments demonstrate that LLaMo achieves high-fidelity text-to-motion generation and motion-to-text captioning in general settings, especially zero-shot motion generation, marking a significant step towards a general unified motion-language large model. Here are our project homepage: https://kunkun0w0.github.io/project/LLaMo/

Qi Mao, Hao Cheng, Tinghan Yang, Libiao Jin, Siwei Ma

We present GNVC-VD, the first DiT-based generative neural video compression framework built upon an advancedvideo generation foundation model, where spatio-temporal latent compression and sequence-level generative refinement are unified within a single codec. Existing perceptual codecs primarily rely on pre-trained image generative priors to restorehigh-frequency details, but their frame-wise nature lacks temporal modeling and inevitably leads to perceptual flickering. To address this, GNVC-VD introduces a unified flow-matching latent refinement module that leverages a video diffusion transformer to jointly enhance intra- and inter-frame latents through sequence-level denoising, ensuring consistent spatio-temporal details. Instead of denoising from pure Gaussian noise as in video generation, GNVC-VD initializes refinement from decoded spatio-temporal latents and learns a correction term that adapts the diffusion prior to compression-induced degradation. A conditioning adaptor further injects compression-aware cues into intermediate DiT layers, enabling effective artifact removal while maintaining temporal coherenceunder extreme bitrate constraints. Extensive experiments show that GNVC-VD surpasses both traditional and learned codecs in perceptual quality and significantly reduces the flickering artifacts that persist in prior generative approaches, even below 0.01 bpp, highlighting the promise of integrating video-native generative priors into neural codecs for next-generation perceptual video compression.

Tao Liu, Kan Ren, Gang Wan, Shibo Wen

We propose a novel unsupervised framework for online video stabilization. Unlike deep learning-based stabilizers that require paired stable/unstable datasets, our method models the classical three-stage stabilization pipeline and integrates a multithreaded buffering mechanism, effectively addressing three key challenges of end-to-end learning: limited data, poor controllability, and inefficiency on resource-constrained hardware. Existing benchmarks mainly focus on handheld, forward-view, visible-light videos, restricting the application of stabilization in domains such as UAV nighttime remote sensing. To fill this gap, we introduce a new multimodal UAV aerial video dataset (UAV-Test). Experiments show that our approach consistently outperforms state-of-the-art online stabilizers in both quantitative metrics and visual quality, while achieving performance comparable to that of offline methods.

Xiuchao Wu, Pengfei Zhu, Jiangjing Lyu, Xinguo Liu, Jie Guo, Yanwen Guo, Weiwei Xu, Chengfei Lyu

Applying diffusion models to physically-based material estimation and generation has recently gained prominence. In this paper, we propose MatMart, a novel material reconstruction framework for 3D objects, offering the following advantages. First, MatMart adopts a two-stage reconstruction, starting with accurate material prediction from inputs and followed by prior-guided material generation for unobserved views, yielding high-fidelity results. Second, by utilizing progressive inference alongside the proposed view-material cross-attention (VMCA), MatMart enables reconstruction from an arbitrary number of input images, demonstrating strong scalability and flexibility. Finally, MatMart achieves both material prediction and generation capabilities through end-to-end optimization of a single diffusion model, without relying on additional pre-trained models, thereby exhibiting enhanced stability across various types of objects. Extensive experiments demonstrate that MatMart achieves superior performance in material reconstruction compared to existing methods.

Ali Aliev, Kamil Garifullin, Nikolay Yudin, Vera Soboleva, Alexander Molozhavenko, Ivan Oseledets, Aibek Alanov, Maxim Rakhuba

In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training data to adapt the model to a narrow task. However, there is an open question: how to combine several adapters tuned for different tasks into one which is able to yield adequate results on both tasks? Specifically, merging subject and style adapters for generative models remains unresolved. In this paper we seek to show that in the case of orthogonal fine-tuning (OFT), we can use structured orthogonal parametrization and its geometric properties to get the formulas for training-free adapter merging. In particular, we derive the structure of the manifold formed by the recently proposed Group-and-Shuffle (GS) orthogonal matrices, and obtain efficient formulas for the geodesics approximation between two points. Additionally, we propose a spectra restoration transform that restores spectral properties of the merged adapter for higher-quality fusion. We conduct experiments in subject-driven generation tasks showing that our technique to merge two GS orthogonal matrices is capable to unite concept and style features of different adapters. To the best of our knowledge, this is the first training-free method for merging multiplicative orthogonal adapters. Code is available via the link: https://github.com/ControlGenAI/OrthoFuse.

Guangrui Li, Zhengyu Zhu, Yongxin Ge

Test-Time Adaptive Segmentation (TTA-Seg) aims to adapt a trained segmentation model to test data under distribution shift in an unsupervised manner. Existing approaches typically utilize class-wise prototypes to capture and transfer the source distribution, but inevitably neglect the diversity within source samples. In this paper, we propose a new test-time adaptation paradigm based on the mixture-of-experts (MoE), where domain experts are designed to 1) better capture the source distribution, and 2) dynamically adjust their contribution in test case prediction. Specifically, during source training, prototypes are derived as the class-wise average for source pixel features. We then generate multiple experts through clustering these prototypes, providing each class with several experts with enhanced representativeness. At test time, each instance prediction is drawn from all experts' knowledge in an adaptive manner, i.e., a gating network assigns weights according to instance-expert correlations. To optimize the system, we devise a min-max entropy optimization scheme for the gating network but keeping the rest frozen, minimizing the entropy of model prediction but maximizing the entropy in expert selection. Consequently, the model is urged to derive confident predictions with effective utilization of domain experts, hence promoting the adaptation.Experiments on two scenarios, Test-time Adaptation (TTA) and the more challenging continual TTA, demonstrate that our approach achieves the new state-of-the-art performance.

Tianze Xia, Yongkang Li, Lijun Zhou, Jingfeng Yao, Kaixin Xiong, Haiyang Sun, Bing Wang, Kun Ma, Guang Chen, Hangjun Ye 等

World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motion planning as decoupled processes. To bridge this gap, we propose DriveLaW, a novel paradigm that unifies video generation and motion planning. By directly injecting the latent representation from its video generator into the planner, DriveLaW ensures inherent consistency between high-fidelity future generation and reliable trajectory planning. Specifically, DriveLaW consists of two core components: DriveLaW-Video, our powerful world model that generates high-fidelity forecasting with expressive latent representations, and DriveLaW-Act, a diffusion planner that generates consistent and reliable trajectories from the latent of DriveLaW-Video, with both components optimized by a three-stage progressive training strategy. New state-of-the-art results across both tasks demonstrate the power of our unified paradigm. DriveLaW not only significantly advances video prediction, surpassing the previous best-performing work by 33.3% in FID and 1.8% in FVD, but also sets a new record on the NAVSIM planning benchmark.

Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière, Emmanuel Koumandakis, Anis Kacem, Enjie Ghorbel, Djamila Aouada

In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D keypoints, followed by a Perspective-n-Point solver, have shown great performance. Direct approaches, which regress the pose in an end-to-end manner, are usually computationally more efficient but less accurate. However, direct pose regression heads rely on globally pooled features, ignoring spatial second-order statistics despite their informativeness in pose prediction. They also predict, in most cases, discontinuous pose representations that lack robustness. Herein, we therefore propose a covariance-pooled representation that encodes convolutional feature distributions as a symmetric positive definite (SPD) matrix. Moreover, we propose a novel pose encoding in the form of an SPD matrix via its Cholesky decomposition. Pose is then regressed in an end-to-end manner with a manifold-aware network head, taking into account the Riemannian geometry of SPD matrices. Experiments and ablations consistently demonstrate the relevance of second-order pooling and continuous representations for direct pose regression, including under partial occlusion.

Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Xiangtao Kong, Jixin Zhao, Shihao Wang, Lei Zhang

Large-scale pre-trained text-to-image (T2I) diffusion models, such as Stable Diffusion, can be finetuned for image super-resolution (SR) with highly realistic details. While impressive, pre-training such multi-modal models demands billions of high-quality text-image pairs and substantial computational resources, despite that SR is fundamentally an image-to-image (I2I) task. This raises a critical question: do we truly need multi-modal priors and billion-scale text-image data to solve a purely vision task? In this paper, we propose **VOSR**, a **V**ision-**O**nly **S**uper-**R**esolution framework that eliminates the need for textual priors and multi-modal pretraining. We identify two key limitations in previous image-based, uni-modal diffusion models: limited visual semantic guidance and unstable unconditional training. To this end, we leverage a pretrained vision encoder to inject semantic cues, and introduce a relaxed unconditional objective that partially uses the low-quality condition to stabilize training. To accelerate inference, we adopt a modified shortcut model for one-step SR with minimal quality degradation. VOSR is trained from scratch with significantly less data and a lower computational cost compared to T2I-based diffusion models. However, VOSR achieves comparable or even better performance than state-of-the-art T2I-tuned SR methods on both synthetic and real-world benchmarks, demonstrating its potential as a scalable and competitive alternative for generative SR. Codes and models will be made publicly available.

Xucong Wang, Pengkun Wang, Zhe Zhao, Liheng Yu, Rui Mao, Yang Wang

Prompt Learning (PL) has emerged as a parameter-efficient technique for adapting Vision-Language Models (VLMs) to downstream tasks. However, almost all existing PL methods are primarily designed and evaluated on well-curated datasets, overlooking a critical post-deployment phenomenon, i.e., the intrinsic connection between input resolution and storage-memory consumption. Specifically, to satisfy the stringent storage-memory constraints on edge devices, models are often limited to low-resolution inputs (e.g., <224x224 for CLIP-ViT/B-16) and generate fewer tokens (with the position embedding resized), which poses a unique challenge in performance robustness. To tackle this issue, we propose LOREAL, an efficient prompt self-distillation framework that learns resolution-invariant representations by excavating attribute semantics. At the heart of LOREAL is a dual-student architecture, i.e., two student models fed with inputs at different resolutions synergistically learn from each other. Building upon this, we contextualize the students' prompt with resolution-invariant attributes queried from the LLM, then leverage cross-modality meta-nets to generate attribute semantics. These meta-nets are bridged between the different encoders of two students, wherein we introduce Low-Level Distillation (LLD) and High-Level Distillation (HLD) to facilitate the learning of more cross-resolution representations. Extensive experiments show that LOREAL significantly improves VLMs' performance and robustness under varied resolution settings, underscoring significant practical utilities.

Ruichi Zhang, Chikai Shang, Jiacheng Yang, Mengke Li, Yang Zhou, Junlong Gao, Yang Lu

Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few. Recently, fine-tuning foundation models for long-tailed learning has gained attention due to their excellent performance. However, most existing methods focus solely on mitigating long-tailed distribution bias while overlooking concept confusion caused by the long-tailed distribution. In this paper, we study this problem and attribute it to the mutual exclusivity of single-label supervision under long-tailed distributions, which suppresses feature sharing among related classes and amplifies the dominance of head classes, leading to disrupted inter-class discriminality. To address this, we propose CUE, Concept-aware Multi-label Expansion, which introduces multi-label concept signals to preserve disrupted inter-class relationships. Specifically, CUE constructs concept sets by (i) extracting instance-level visual cues from zero-shot CLIP and (ii) generating class-level semantic cues with LLM; the two cues are incorporated via separately weighted Binary Logit-Adjustmen (BLA) auxiliary losses and jointly optimized with the baseline Logit-Adjustmen (LA) loss. Experiments on several long-tailed benchmarks, CUE achieves balanced and strong performance, surpassing recent state-of-the-art methods. Code is available at: https://github.com/zhangruichi/CUE.

Hyeseong Kim, Geonhui Son, Deukhee Lee, Dosik Hwang

Novel view synthesis from sparse-view inputs poses a significant challenge in 3D computer vision, particularly for achieving high-quality scene reconstructions with limited viewpoints. We introduce TWINGS, a framework that enhances 3D Gaussian Splatting (3DGS) by directly addressing point sparsity. We employ Thin Plate Splines (TPS), a smooth non-rigid deformation model that minimizes bending energy to estimate a globally coherent warp from control-point correspondences, to align backprojected points from estimated depth with triangulated 3D control points, yielding calibrated backprojected points. By sampling these calibrated points near the control points, TWINGS provides a fast and geometrically accurate initialization for 3DGS, ultimately improving structural detail preservation and color fidelity in reconstructed scenes. Extensive experiments on DTU, LLFF, and Mip-NeRF360 demonstrate that TWINGS consistently outperforms existing methods, delivering detailed and accurate reconstructions under sparse-view scenarios.

Jinkai Zheng, Jiaqing Wei, Xinxiang Jin, Yaoqi Sun, Xichun Sheng, Ming Li, Liangqiong Qu, Xinchen Liu, Wu Liu

In recent years, the gait parsing sequence has become increasingly popular due to its higher information entropy than the binary silhouette and the keypoint-based skeleton. However, existing parsing-based gait recognition methods have not fully explored the complex, non-linear relationships between features at different positions, semantic, and temporal dynamics levels, i.e., higher-order correlations. To unleash the power of parsing between human body parts and temporal dynamics, this paper proposes a novel hypergraph-based gait recognition framework, named HyperGait. The HyperGait contains a global head and two elaborately-designed modules. In particular, the Spatial Hypergraph Convolutional Module (SHCM) and the Temporal Hypergraph Convolutional Module (THCM) are designed to explore the high-order spatial-level and temporal-level features, respectively.The SHCM extracts fine-grained relationships between human body parts through the hypergraph.The THCM performs the high-order temporal information between temporally related human body parts.Comprehensive experiments on two large-scale gait datasets, i.e., Gait3D and SUSTech1K, show the superior performance of our proposed HyperGait.In highly challenging real-world scenarios, with only parsing as input, our HyperGait achieves the Rank-1 accuracy of 80.5% on the Gait3D dataset.