GenAI4ELab/papercli-papers huggingface dataset:
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
Authors: Ting Zhou; Daoyuan Chen; Qirui Jiao; Bolin Ding; Yaliang Li; Ying Shen
Venue: CVPR 2026 (main)

Abstract: Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook the intricacies of emotion, behavior, and cross-modal alignment. We introduce HumanVBench, a comprehensive video benchmark designed to rigorously probe these capabilities across 16 fine-grained tasks. A cornerstone of our work is a novel and scalable benchmark construction methodology, featuring two automated pipelines that synthesize high-quality video annotations and challenging multiple-choice questions with minimal human labor. By leveraging state-of-the-art models for annotation and systematically converting model-induced errors into plausible distractors, our framework provides a generalizable "machine" for creating nuanced evaluation suites. Our extensive evaluation of 30 leading MLLMs on HumanVBench reveals critical deficiencies, particularly in perceiving subtle emotions and aligning speech with visual cues, with even top proprietary models falling short of human performance. We open-source HumanVBench and our synthesis pipelines to catalyze the development of more socially intelligent and capable video MLLMs.
Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration
Authors: Sicheng Mo; Thao Nguyen; Richard Zhang; Nick Kolkin; Siddharth Srinivasan Iyer; Eli Shechtman; Krishna Kumar Singh; Yong Jae Lee; Bolei Zhou; Yuheng Li
Venue: CVPR 2026 (main)

Abstract: In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be generated collaboratively. We propose Group Diffusion, unlocking the attention mechanism to be shared across images, rather than limited to just the patches within an image. This enables images to be jointly denoised at inference time, learning both intra and inter-image correspondence. We observe a clear scaling effect -- larger group sizes yield stronger cross-sample attention and better generation quality. Furthermore, we introduce a qualitative measure to capture this behavior and show that its strength closely correlates with FID. Built on standard diffusion transformers, our GroupDiff achieves up to 32.2% FID improvement on ImageNet-256x256. Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.
MoRe: Motion-aware Feed-forward 4D Reconstruction Transformer
Authors: Juntong Fang; Zequn Chen; Weiqi Zhang; Donglin Di; Xuancheng Zhang; Chengmin Yang; Yu-Shen Liu
Venue: CVPR 2026 (main)

Abstract: Reconstructing dynamic 4D scenes remains challenging due to the presence of moving objects that corrupt camera pose estimation. Existing optimization methods alleviate this issue with additional supervision, but they are mostly computationally expensive and impractical in real-time applications. To address these limitations, we propose MoRe, a feedforward 4D reconstruction network that efficiently recovers dynamic 3D scenes from monocular videos. Built upon a strong static reconstruction backbone, MoRe employs an attention-forcing strategy to disentangle dynamic motion from static structure. To further enhance robustness, we fine-tune the model on large-scale, diverse datasets encompassing both dynamic and static scenes. Moreover, our grouped causal attention captures temporal dependencies and adapts to varying token lengths across frames, ensuring temporally coherent geometry reconstruction. Extensive experiments on multiple benchmarks demonstrate that MoRe achieves high-quality dynamic reconstructions with exceptional efficiency.
SIMPLEPOSTER: A SIMPLE BASELINE FOR PRODUCT POSTER GENERATION
Authors: Benlei Cui; Fangao Zeng; Weitao Jiang; Yuwen Zhai; Haiwen Hong; Longtao Huang; Hui Xue; Wenxiang Shang; Pipei Huang
Venue: CVPR 2026 (main)

Abstract: Product poster generation presents unique challenges beyond general-purpose de-sign: it demands not only aesthetic composition and accurate text rendering, butalso strict preservation of the product subject and precise control over dense,multi-line text layouts. While general image editing models struggle with text lay-out control and subject consistency, existing specialized approaches--often builtupon inpainting frameworks--still suffer from unintended subject extension andinaccurate text synthesis. A common solution involves integrating auxiliary mod-ules such as ControlNet to condition on subject structure and text layout, but theseapproaches introduce significant architectural complexity and training overhead.In this work, we challenge the necessity of such complexity and demonstrate thatminimalist adaptation is sufficient. We introduce SimplePoster, a minimalist yetpowerful inpainting-based framework that enables faithful subject preservationand position-controllable text rendering--entirely without external controllers likeControlNet. SimplePoster rests on two key insights: (1) full-parameter fine-tuningalone effectively suppresses subject extension by aligning the model's internalrepresentations with domain-specific priors; and (2) a lightweight character-levelposition encoding strategy enables end-to-end, spatially grounded text generation.Experiments show that SimplePoster achieves near-perfect subject preservation(98.7% of cases with strict subject preservation), significantly outperforming boththe state-of-the-art editing model SeedEdit3.0 (55.2%) and the specialized ap-proach PosterMaker (85.3%). It further demonstrates superior text rendering ac-curacy, even in challenging scenarios with complex multi-line layouts. We believeSimplePoster establishes a simple yet strong baseline for product poster genera-tion. We question the necessity of such complexity and demonstrate that minimalist designs suffice. We propose SimplePoster, a simple yet effective inpainting-based framework that achieves faithful subject preservation and position-controllable text rendering without relying on external controllers like ControlNet. SimplePoster is based on two key insights: (1) full-parameter fine-tuning effectively suppresses subject extension; and (2) a training-free character-level position encoding strategy enables end-to-end, geometry-aware text generation. Remarkably, SimplePoster achieves a near-perfect subject preservation rate (98.7%), significantly outperforming SOTA models SeedEdit 3.0 (55.2%) and PosterMaker (85.3%). It also excels in text rendering accuracy. We believe SimplePoster establishes a simple yet strong baseline for product poster generation. Code, models and benchmark will be released upon acceptance.
Simple-ViLMedSAM: Simple Text Prompts Meet Vision-Language Models for Medical Image Segmentation
Authors: Chengcan Qian; Dong Nie; Geng Chen; Daoqiang Zhang; Xuyun Wen
Venue: CVPR 2026 (main)

Abstract: Medical image segmentation is challenging due to limited annotated data, high labeling costs, and substantial image heterogeneity. Although large-scale vision foundation models (e.g., SAM) have shown great potential in this field, existing SAM-based methods typically rely on expert-defined geometric prompts or complex clinical text prompts, which limits their generalizability across diverse medical image segmentation tasks. To overcome these challenges, we propose Simple-ViLMedSAM, a CLIP-SAM integration framework that enables high-accuracy segmentation in zero-shot and few-shot settings using only simple text queries, that is, using only basic anatomical or disease-related text labels. At its core is an Implicit Pos-Prompter (IPP), which generates attribution maps containing implicit positional cues to replace traditional geometric prompts. IPP incorporates a multi-modal information bottleneck and an affinity-based refinement strategy to ensure high-quality guidance from CLIP-SAM interactions. To further enhance segmentation, we introduce a Bidirectional Interaction Decoder (BID) that employs bidirectional cross-attention to align IPP's positional maps with SAM's pixel-level features. By jointly modeling global semantics and local details, BID significantly improves segmentation accuracy. Extensive experiments on four public datasets demonstrate that Simple-ViLMedSAM consistently outperforms existing methods in both zero-shot and few-shot medical image segmentation tasks, using only simple text queries. The code will be publicly available upon acceptance.
Towards Policy-Adaptive Image Guardrail: Benchmark and Method
Authors: Caiyong Piao; Zhiyuan Yan; Haoming Xu; Yunzhen Zhao; Kaiqing Lin; Feiyang Xu; Shuigeng Zhou
Venue: CVPR 2026 (main)

Abstract: Accurate rejection of sensitive or harmful visual content, i.e., harmful image guardrail, is critical in many application scenarios. This task must continuously adapt to the evolving safety policies and content across various domains and over time. However, traditional classifiers, confined to fixed categories, require frequent retraining when new policies are introduced. Vision-language models (VLMs) offer a more adaptable and generalizable foundation for dynamic safety guardrails. Despite this potential, existing VLM-based safeguarding methods are typically trained and evaluated under only a fixed safety policy. We find that these models are heavily overfitted to the seen policy, fail to generalize to unseen policies, and even lose the basic instruction-following ability and general knowledge. To address this issue, in this paper we make two key contributions. First, we benchmark the cross-policy generalization performance of existing VLMs with SafeEditBench, a new evaluation suite. SafeEditBench leverages image-editing models to convert unsafe images into safe counterparts, producing policy-aligned datasets where each safe-unsafe image pair remains visually similar except for localized regions violating specific safety rules. Human annotators then provide accurate safe/unsafe labels under five distinct policies, enabling fine-grained assessment of policy-aware generalization. Second, we introduce SafeGuard-VL, a reinforcement learning-based method with verifiable rewards (RLVR) for robust unsafe-image guardrails. Instead of relying solely on supervised fine-tuning (SFT) under fixed policies, SafeGuard-VL explicitly optimizes the model with policy-grounded rewards, promoting verifiable adaptation across evolving policies. Extensive experiments verify the effectiveness of our method for unsafe image guardrails across various policies.
SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking
Authors: Qiuyang Zhang; Jiujun Cheng; Qichao Mao; Cong Liu; Yu Fang; Yuhong Li; Mengying Ge; Shangce Gao
Venue: CVPR 2026 (main)

Abstract: Spiking Neural Networks (SNNs) promise energy-efficient vision, but applying them to RGB visual tracking remains difficult: Existing SNN tracking frameworks either do not fully align with spike-driven computation or do not fully leverage neurons' spatiotemporal dynamics, leading to a trade-off between efficiency and accuracy. To address this, we introduce SpikeTrack, a spike-driven framework for energy-efficient RGB object tracking. SpikeTrack employs a novel asymmetric design that uses asymmetric timestep expansion and unidirectional information flow, harnessing spatiotemporal dynamics while cutting computation. To ensure effective unidirectional information transfer between branches, we design a memory-retrieval module inspired by neural inference mechanisms. This module recurrently queries a compact memory initialized by the template to retrieve target cues and sharpen target perception over time. Extensive experiments demonstrate that SpikeTrack achieves the state-of-the-art among SNN-based trackers and remains competitive with advanced ANN trackers. Notably, it surpasses TransT on LaSOT dataset while consuming only 1/26 of its energy. To our knowledge, SpikeTrack is the first spike-driven framework to make RGB tracking both accurate and energy efficient.
Beyond Single-View Sufficiency: CVBench for Cross-View Human Understanding
Authors: Tianchen Guo; Chen Liu; Xin Yu
Venue: CVPR 2026 (main)

Abstract: Human perception of social environments is inherently a multi-view synthesis problem, requiring the integration of complementary and often occluded information across space and time. However, existing benchmarks for Multimodal Large Language Models (MLLMs) are overwhelmingly predicated on a "sufficient-view" assumption, rewarding single-view pattern recognition while failing to evaluate cross-view fusion. To address this critical gap, we introduce CVBench, a large-scale, multi-task benchmark for cross-view human understanding. CVBench comprises 3,000 challenging questions across 12 spatial and temporal tasks, where every item is designed with verifiable single-view insufficiency, mandating that models synthesize disparate evidence to resolve ambiguities. Our comprehensive evaluation of state-of-the-art open and closed-source MLLMs reveals a substantial performance gap, with the best models falling nearly 50 points behind human performance. We identify a systemic failure mechanism across all models: a dominant "Single-View Bias", whereby models ignore conflicting evidence and default to the most confident but incorrect single-view prediction. This demonstrates that current MLLMs lack the fundamental mechanisms for geometric grounding, identity persistence, and true spatio-temporal fusion. CVBench provides a rigorous diagnostic framework to catalyze the development of next-generation, cross-view-aware architectures.
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition
Authors: Minxue Tang; Yangyang Yu; Aolin Ding; Maziyar Baran Pouyan; Taha Belkhouja; Yujia Bao
Venue: CVPR 2026 (main)

Abstract: Recognizing visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challenging for current pre-trained foundation models such as LLMs and VLMs. While finetuning pre-trained models can improve accuracy in recognizing such implicit patterns, it is usually infeasible due to a lack of training data and high computational overhead. In this paper, we propose ADAMAB, an efficient embedding calibration framework for few-shot pattern recognition. To maximally reduce the computational costs, ADAMAB trains embedder-agnostic light-weight calibrators on top of fixed embedding models without accessing their parameters. To mitigate the need for large-scale training data, we introduce an adaptive data augmentation strategy based on the Multi-Armed Bandit (MAB) mechanism. With a modified upper confidence bound algorithm, ADAMAB diminishes the gradient shifting and offers theoretically guaranteed convergence in few-shot training. Our multi-modal experiments justify the superior performance of ADAMAB, with up to 40% accuracy improvement when training with less than 5 initial data samples of each class.
Energy-GS: Image Energy-guided Pose Alignment Gaussian Splatting with redesigned pose gradient flow
Authors: Yu Gao; Lutong Su; Ruixiang Huang; Tianji Jiang; Jiadong Tang; Yufeng Yue; Yi Yang
Venue: CVPR 2026 (main)

Abstract: High-quality 3D scene representation in radiance fields relies on accurate camera poses which are often difficult to acquire in real-world scenarios. An effective solution is to use RGB images for the joint optimization of radiance fields and camera poses, an approach that has been well explored in NeRF series methods. However, unlike NeRF, joint optimization in 3D Gaussian Splatting (3DGS) often requires additional regularization or prior spatial knowledge to reach comparable performance. To eliminate these dependencies, we introduce Energy-GS, a pose-aware Gaussian splatting framework that jointly optimizes scene representation and camera poses using only RGB images. We observe that pose gradients in joint optimization are unstable due to the point-based rendering mechanism. Furthermore, unlike NeRF's spatial sampling framework that enables coarse-to-fine pose alignment, rasterization-based 3DGS lacks controllable sampling and thus cannot support progressive pose refinement. To address these challenges, we redesign the optimization strategy of Gaussian primitives and introduce an image-energy-guided constraint that encourages progressive alignment of camera poses. Experiments on both synthetic and real-world datasets show that Energy-GS can effectively optimize the scene reconstruction and resolve camera pose misalignment at the same time. Benefiting from reliance on only RGB images, we believe this work provides promising insights for visual localization and dense mapping applications such as SLAM. The Code is available at \href https://github.com/SkylerGao/ENGS  https://github.com/SkylerGao/ENGS
Sparsity as a Key: Unlocking New Insights from Latent Structures for Out-of-Distribution Detection
Authors: Ahyoung Oh; Wonseok Shin; Songkuk Kim
Venue: CVPR 2026 (main)

Abstract: Sparse Autoencoders (SAEs) have demonstrated significant success in interpreting Large Language Models (LLMs) by decomposing dense representations into sparse, semantic components. However, their potential for analyzing Vision Transformers (ViTs) remains largely under-explored. In this work, we present the first application of SAEs to the ViT [CLS] token for out-of-distribution (OOD) detection, addressing the limitation of existing methods that rely on entangled feature representations. We propose a novel framework utilizing a Top-k SAE to disentangle the dense [CLS] features into a structured latent space. Through this analysis, we reveal that in-distribution (ID) data exhibits consistent, class-specific activation patterns, which we formalize as Class Activation Profiles (CAPs). Our study uncovers a key structural invariant: while ID samples preserve a stable pattern within CAPs, OOD samples systematically disrupt this structure. Leveraging this insight, we introduce a scoring function based on the divergence of core energy profiles to quantify the deviation from ideal activation profiles. Our method achieves strong results on the FPR95 metric--critical for safety-sensitive applications--across multiple benchmarks, while also achieving competitive AUROC. Overall, our findings demonstrate that the sparse, disentangled features revealed by SAEs can serve as a powerful, interpretable tool for robust OOD detection in vision models.
TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment
Authors: Bingyi Cao; Koert Chen; Kevis-Kokitsi Maninis; Kaifeng Chen; Arjun Karpur; Ye Xia; Sahil Dua; Tanmaya Dabral; Guangxing Han; Bohyung Han; Joshua Ainslie; Alex Bewley; Mithun Jacob; René Wagner; Washington Ramos; Krzysztof Choromanski; Mojtaba Seyedhosseini; Howard Zhou; Andre Araujo
Venue: CVPR 2026 (main)

Abstract: Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation and depth prediction. However, a fundamental capability that these models still struggle with is aligning dense patch representations with text embeddings of corresponding concepts. In this work, we investigate this critical issue and propose novel techniques to enhance this capability in foundational vision-language models. First, we reveal that a patch-level distillation procedure significantly boosts dense patch-text alignment -- surprisingly, the patch-text alignment of the distilled student model strongly surpasses that of the teacher model. This observation inspires us to consider modifications to pretraining recipes, leading us to propose iBOT++, an upgrade to the commonly-used iBOT masked image objective, where unmasked tokens also contribute directly to the loss. This dramatically enhances patch-text alignment of pretrained models. Additionally, to improve vision-language pretraining efficiency and effectiveness, we modify the exponential moving average setup in the learning recipe, and introduce a caption sampling strategy to benefit from synthetic captions at different granularities. Combining these components, we develop TIPSv2, a new family of image-text encoder models suitable for a wide range of downstream applications. Through comprehensive experiments on 9 tasks and 20 datasets, we demonstrate strong performance, generally on par with or better than recent vision encoder models. Code and models are released via our project page at https://gdm-tipsv2.github.io/.
NAMI: Efficient Image Generation via Bridged Progressive Rectified Flow Transformers
Authors: Yuhang Ma; Bo Cheng; Shanyuan Liu; Hongyi Zhou; Liebucha Wu; Dawei Leng; Yuhui Yin
Venue: CVPR 2026 (main)

Abstract: Flow-based Transformer models have achieved state-of-the-art image generation performance, but often suffer from high inference latency and computational cost due to their large parameter sizes. To improve inference efficiency without compromising quality, we propose Bridged Progressive Rectified Flow Transformers (NAMI), which decompose the generation process across temporal, spatial, and architectural demensions. We divide the rectified flow into different stages according to resolution, and use a BridgeFlow module to connect them. Fewer Transformer layers are used at low-resolution stages to generate image layouts and concept contours, and more layers are progressively added as the resolution increases. Experiments demonstrate that our approach achieves fast convergence and reduces inference time while ensuring generation quality. The main contributions of this paper are summarized as follows: (1) We introduce Bridged Progressive Rectified Flow Transformers that enable multi-resolution training, accelerating model convergence; (2) NAMI leverages piecewise flow and spatial cascading of Diffusion Transformer (DiT) to rapidly generate images, reducing inference time by 64% for generating 1024x1024 resolution images; (3) We propose a BridgeFlow module to align flows between different stages; (4) We propose the NAMI-1K benchmark to evaluate human preference performance, aiming to mitigate distributional bias and comprehensively assess model effectiveness. The results show that our model is competitive with state-of-the-art models.
Temporal Equilibrium MeanFlow: Bridging the Scale Gap for One-Step Generation
Authors: Yuanpeng Tu; Yunpeng Chen; Xinyu Zhang; Chao Liao; Hengshuang Zhao
Venue: CVPR 2026 (main)

Abstract: MeanFlow is a powerful few-step generative framework that can be trained from scratch, but its performance degrades significantly when the one-step loss uses a large portion of training data. This stems from a temporal scale imbalance: gradients from different stages of generation contribute unevenly, leading to unstable optimization--evident in blurry samples and high FID scores. The core issue is a conflict between two opposing forces: terms that amplify variance over long time spans and strong constraints needed near the start of generation, which a fixed sampling strategy cannot reconcile. To resolve this, we propose Temporal Equilibrium MeanFlow (TEMF), which balances these competing demands through two simple yet effective components: (1) a temporal equilibrium weighting function that equalizes gradient influence across all time scales, and (2) a dynamic boundary scheduler that gradually shifts training focus--from stabilizing early steps to refining the full trajectory as training progresses. Without changing the model architecture, TEMF retains true one-step generation with classifier-free guidance, achieving a state-of-the-art FID of 2.62 on ImageNet 256x256--achieving the best results among diffusion- and flow-based one-step methods.