GenAI4ELab/papercli-papers huggingface dataset:
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Authors: Fangzhou Lin; Yuping Wang; Yuliang Guo; Zixun Huang; Xinyu Huang; Haichong Zhang; Kazunori Yamada; Zhengzhong Tu; Liu Ren; Ziming Zhang
Venue: CVPR 2026 (main)

Abstract: Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the simpler setting of homogeneous, dense prediction tasks, leaving the more realistic challenge of learning from structurally diverse tasks unexplored. To this end, we introduce NexusFlow, a novel, lightweight, and plug-and-play framework effective in both settings. NexusFlow introduces a set of surrogate networks with invertible coupling layers to align the latent feature distributions of tasks, creating a unified representation that enables effective knowledge transfer. The coupling layers are bijective, preserving information while mapping features into a shared canonical space. This invertibility avoids representational collapse and enables alignment across structurally different tasks without reducing expressive capacity.We first evaluate NexusFlow on the core challenge of domain-partitioned autonomous driving, where dense map reconstruction and sparse multi-object tracking are supervised in different geographic regions, creating both structural disparity and a strong domain gap. NexusFlow sets a new state-of-the-art result on nuScenes, outperforming strong partially supervised baselines. To demonstrate generality, we further test NexusFlow on NYUv2 using three homogeneous dense prediction tasks, segmentation, depth, and surface normals, as a representative N-task PS-MTL scenario. NexusFlow yields consistent gains across all tasks, confirming its broad applicability.
Exploring Spatiotemporal Feature Propagation for Video-Level Compressive Spectral Reconstruction: Dataset, Model and Benchmark
Authors: Lijing Cai; Zhan Shi; Chenglong Huang; Jinyao Wu; Qiping Li; Zikang Huo; Linsen Chen; Chongde Zi; Xun Cao
Venue: CVPR 2026 (main)

Abstract: Recently, Spectral Compressive Imaging (SCI) has achieved remarkable success, unlocking significant potential for dynamic spectral vision. However, existing reconstruction methods, primarily image-based, suffer from two limitations: (i) Encoding process masks spatial-spectral features, leading to uncertainty in reconstructing missing information from single compressed measurements, and (ii) The frame-by-frame reconstruction paradigm fails to ensure temporal consistency, which is crucial in the video perception. To address these challenges, this paper seeks to advance spectral reconstruction from the image level to the video level, leveraging the complementary features and temporal continuity across adjacent frames in dynamic scenes. Initially, we construct the first high-quality dynamic hyperspectral image dataset (DynaSpec), comprising 30 sequences obtained through frame-scanning acquisition. Subsequently, we propose the Propagation-Guided Spectral Video Reconstruction Transformer (PG-SVRT), which employs a spatial-then-temporal attention to effectively reconstruct spectral features from abundant video information, while using a bridged token to reduce computational complexity. Finally, we conduct simulation experiments to assess the performance of four SCI systems, and construct a DD-CASSI prototype for real-world data collection and benchmarking. Extensive experiments demonstrate that PG-SVRT achieves superior performance in reconstruction quality, spectral fidelity, and temporal consistency, while maintaining minimal FLOPs.
MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
Authors: Jian Zou; Xiaoyu Xu; Zhihua Wang; Yilin Wang; Balu Adsumilli; Kede Ma
Venue: CVPR 2026 (main)

Abstract: Learning-based video quality assessment (VQA) has advanced rapidly, yet progress is increasingly constrained by a disconnect between model design and dataset curation. Model-centric approaches often iterate on fixed benchmarks, while data-centric efforts collect new human labels without systematically targeting the weaknesses of existing VQA models. Here, we describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos that are both difficult for the base VQA model and diverse in content. Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features, with a greedy procedure balancing the two under a constrained labeling budget. Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples that are particularly informative for active fine-tuning. With only a 5% selected subset per target domain, the fine-tuned model improves mean SRCC from 0.651 to 0.722 and achieves the top gMAD rank, indicating strong adaptation and generalization.
Aligning Multi-Character Narrative Image Generation with Multi-Aspect Human Preferences
Authors: Ziyi Gao; Zhipeng Wei; Jingjing Chen; Zhiyu Tan; Hao Li; Yi-Ping Phoebe Chen
Venue: CVPR 2026 (main)

Abstract: Narrative image generation aims to create images featuring multiple distinct characters while capturing their interrelationships, posing significant challenges for current text-to-image diffusion models. As a result, general personalized methods often suffer from poor semantic alignment, identity blending, and aesthetic implausibility.These issues are inadequately captured by existing evaluation metrics such as CLIP, ArcFace, and conventional reward models, which fundamentally fail to align with human perceptual preferences. To align with human preferences, we first construct a fine-grained human preference dataset, NI-RLHF, by collecting both detailed human critiques and preference judgments across three core dimensions: prompt following, identity consistency, and visual quality.This comprehensive dataset facilitates the training of NIReward, a critique-based reward model capable of generating interpretable image evaluations.Building upon the interpretable reward signal from NIReward, we propose Adaptive Dominance-based Preference Optimization (ADPO) to balance learning across diverse preference dimensions while dynamically adapting to reward margins.Experimental results indicate that NIReward significantly outperforms existing evaluation models and reward models, and ADPO yields a significant improvement across the three key preference dimensions. By introducing NIReward and ADPO, our work paves the way for generating narrative images aligned with actual human preferences.
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Authors: Tairan Huang; Yulin Jin; Junxu Liu; Qingqing Ye; Haibo Hu
Venue: CVPR 2026 (main)

Abstract: Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existing black-box attacks focus on vector-based or discrete-action RL, and their effectiveness on image-based continuous control is limited by the large action space and excessive environment queries. We propose SEBA, a sample-efficient framework for black-box adversarial attacks on visual RL agents. SEBA integrates a shadow Q model that estimates cumulative rewards under adversarial conditions, a generative adversarial network that produces visually imperceptible perturbations, and a world model that simulates environment dynamics to reduce real-world queries. Through a two-stage iterative training procedure that alternates between learning the shadow model and refining the generator, SEBA achieves strong attack performance while maintaining efficiency. Experiments on MuJoCo and Atari benchmarks show that SEBA significantly reduces cumulative rewards, preserves visual fidelity, and greatly decreases environment interactions compared to prior black-box and white-box methods. The code is available at https://github.com/tairanhuang/seba online.
PP-Brep: Few-Shot B-rep Classification with Hybrid Graph Representation
Authors: Jiacheng Hao; Chunying Liu; Hao Guo; Ruohan Wang; Hongping Gan; Yilei Shi
Venue: CVPR 2026 (main)

Abstract: In industrial settings, classification of 3D CAD models are critical for efficient manufacturing. However, the limited availability of annotated CAD models presents an obstacle to achieving rapid adaptation in few-shot part classification scenarios. In this paper, we propose a hybrid graph representation and a pre-training and graph prompt framework for B-rep few-shot classification. Specifically, hybrid graph representation captures comprehensive and multi-level structural information of B-rep models by constructing local topology graph, global parallel graph and regional association hypergraph. A hierarchical graph network then fuses component-level structures with topological details in the hybrid graph. Reinforcement-augmented contrastive pre-training produces robust universal representations while in-place perturbation reduces training time. Structure-aware graph prompts finally produce node-specific cues, enabling few-shot B-rep part classification without heavy fine-tuning. Experiments on the TraceParts-11and FabWave-31 datasets show that our method outperforms existing general-purpose approaches. This work provides an efficient and state-of-the-art solution for few-shot B-rep part classification.
Multimodal Semantic Bias Mitigation for Diverse Text-To-3D Generation
Authors: Yukuan Min; Muli Yang; Jinhao Zhang; Yuxuan Wang; Yihang Zhu; Jiexi Yan; Cheng Deng
Venue: CVPR 2026 (main)

Abstract: The latest progress in text-to-3D generative models makes it possible to generate high-quality 3D content. Recent text-to-3D large model have achieved remarkable breakthroughs in multi-view consistency. However, their effectiveness is often affected by inherent biases, resulting in sensitivity to design settings such as prompt format, leading to difficulty understanding complex prompts. To help text-to3D generative models understand more diverse prompts, we propose a framework to localize and mitigate the bias in the current text-to-3D large model. Specifically, we first use the existing model to generate 3D content and use the quality evaluation model to identify the cross-modality bias. Then, we use the predicted quality score to quantify the contribution of the prompt text to the bias. Finally, in order to reduce these biases, we construct diverse pairwise examples to help the current text-to-3D large model construct unbiased visual-text connections. The experiment shows that our method has achieved competitive results and can provide higher quality, more diverse 3D content compared to existing methods.
Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
Authors: Rui Zhao; Bin Shi; Kai Sun; Bo Dong
Venue: CVPR 2026 (main)

Abstract: Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influenced by instance features, leading to the emergence of instance-dependent PLL (ID-PLL), a setting that more accurately reflects this relationship. A significant challenge in ID-PLL is instance entanglement, where instances from similar classes share overlapping features and candidate labels, resulting in increased class confusion. To address this issue, we propose a novel Class-specific Augmentation based Disentanglement (CAD) framework, which tackles instance entanglement by both intra- and inter-class regulations. For intra-class regulation, CAD amplifies class-specific features to generate class-wise augmentations and aligns same-class augmentations across instances. For inter-class regulation, CAD introduces a weighted penalty loss function that applies stronger penalties to more ambiguous labels, encouraging larger inter-class distances. By jointly applying intra- and inter-class regulations, CAD improves the clarity of class boundaries and reduces class confusion caused by entanglement. Extensive experimental results demonstrate the effectiveness of CAD in mitigating the entanglement problem and enhancing ID-PLL performance. The code is available at https://github.com/RyanZhaoIc/CAD.git.
STUR3D: Spatio-Temporal Unified Representation Learning for 3D Object Detection
Authors: Huijie Fan; Pengrui Huang; Qiang Wang; Baojie Fan; Jiahua Dong; Liangqiong Qu
Venue: CVPR 2026 (main)

Abstract: Existing surrounding-view 3D object detectors initialize high-confidence queries using current 2D information, while leveraging historical 3D features as priors. However, such heavy reliance on 2D cues introduces spatio-temporal inconsistencies between 2D and 3D representations. Specifically, 2D cues lack sufficient spatial information, limiting 3D localization capability. Moreover, insufficient temporal interaction often leads to object omission under occlusion. To address these challenges, we propose STUR3D, a unified framework establishing spatio-temporal alignment between 2D and 3D perception. First, we project temporal 3D features to the 2D image plane, empowering the 2D detector to distill representations essential for 3D localization, harmonizing cross-dimensional information. Second, we inject temporal cues into 2D detection, fostering  spatio-temporal reasoning, and ensuring robust 3D detection under dynamic scenes and occlusion. Additionally, we embed depth-aware geometric cues into features for 2D-to-3D lifting, mitigating inherent ambiguities. Extensive nuScenes experiments validate STUR3D, achieving SOTA on the test set with 57.9% mAP and 64.6% NDS.
InterRVOS: Interaction-Aware Referring Video Object Segmentation
Authors: Woojeong Jin; Seongchan Kim; Jaeho Lee; Seungryong Kim
Venue: CVPR 2026 (main)

Abstract: Referring video object segmentation (RVOS) aims to segment objects in a video described by a natural language expression. However, most existing approaches focus only on the referred object (typically the actor), even when the expression clearly describes an interaction involving multiple objects with distinct roles. In this paper, we introduce Interaction-Aware Referring Video Object Segmentation (InterRVOS), a novel task that focuses on explicit interaction modeling by requiring separate segmentation of actor and target objects.This formulation enables fine-grained understanding of object relationships, as many video events are defined by such interactions rather than individual objects. We present InterRVOS-127K, a large-scale dataset of over 127K automatically annotated expressions with distinct actor-target mask pairs, and propose ReVIOSa, a MLLM-based architecture that introduces interaction-aware special tokens and attention mask loss (AML) to enhance interaction-aware segmentation. We also propose a new evaluation protocol that separately evaluates actor and target segmentation for more accurate role distinction. Comprehensive experiments demonstrate that ReVIOSa outperforms existing baselines on the proposed InterRVOS-127K benchmark, with further analyses validating the necessity and effectiveness of both ReVIOSa and InterRVOS-127K.
VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents
Authors: George Eskandar; Fengyi Shen; Mohammad Altillawi; Dong Chen; Yang Bai; Liudi Yang; Ziyuan Liu
Venue: CVPR 2026 (main)

Abstract: Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be transferable to new environments without additional data collection. However, prior works can only operate on a single view at a time, while embodied AI tasks are commonly captured from multiple synchronized cameras to support policy learning. Naively applying single-view models independently to each camera leads to inconsistent appearance across views, and standard transformer architectures do not scale to multi-view settings due to the quadratic cost of cross-view attention. We present VideoWeaver, the first multimodal multi-view V2V translation framework. VideoWeaver is initially trained as a single-view flow-based V2V model. To achieve an extension to the multi-view regime, we propose to ground all views in a shared 4D latent space derived from a feed-forward spatial foundation model, namely, Pi3. This encourages view-consistent appearance even under wide baselines and dynamic camera motion. To scale beyond a fixed number of cameras, we train views at distinct diffusion timesteps, enabling the model to learn both joint and conditional view distributions. This in turn allows autoregressive synthesis of new viewpoints conditioned on existing ones. Experiments show superior or similar performance to the state-of-the-art on the single-view translation benchmarks and, for the first time, physically and stylistically consistent multi-view translations, including challenging egocentric and heterogeneous-camera setups central to world randomization for robot learning.
TopoSlide: Topologically-Informed Histopathology Whole Slide Image Representation Learning
Authors: Shahira Abousamra; Asmita Sood; Sylvia Plevritis
Venue: CVPR 2026 (main)

Abstract: Histopathology whole slide images (WSIs) are gigapixel images that present significant challenges in generating effective representations that capture both local histological features and their global spatial organization. Current pathology foundation models focus primarily on local patch-level features while neglecting the complex spatial relationships that pathologists rely on for diagnosis and prognosis. We introduce TopoSlide, a novel self-supervised representation learning framework that leverages persistent homology from topological data analysis to capture the global spatial organization of tissue architecture in WSIs. Our method decomposes slides into histologically meaningful clusters using patch-level embeddings, then characterizes their spatial arrangement through topological descriptors. We train a vision transformer to predict cluster topology from slide-level embeddings using a conditional multi-task objective that integrates local patch features with their topological attributes. Evaluated across lung adenocarcinoma and breast cancer cohorts, TopoSlide achieves superior performance improving histologic pattern retrieval by up to 15% in majority voting macro F1 score, and competitive survival and gene mutation predictions, while training on only hundreds of slides compared to hundreds of thousands for foundation models. Our results demonstrate that topology-aware learning provides a powerful inductive bias for pathology representation learning, enabling both improved performance and novel topology-based conditional retrieval capabilities for clinical applications. Our code and models are publicly available.
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
Authors: Yiyang Ma; Feng Zhou; Xuedan Yin; Pu Cao; Yonghao Dang; Jianqin Yin
Venue: CVPR 2026 (main)

Abstract: Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitigates these issues with complex pipelines that first perform a base-resolution (i.e., training-resolution) denoising process to guide HR generation. We instead explore the intrinsic generative mechanisms of DiTs and propose ResDiT, a training-free method that scales resolution efficiently. We identify the core factor governing spatial layout, position embeddings (PEs), and show that the original PEs encode incorrect positional information when extrapolated to HR, which triggers layout collapse. To address this, we introduce a PE scaling technique that rectifies positional encoding under resolution changes. To further remedy low-fidelity details, we develop a local-enhancement mechanism grounded in base-resolution local attention. We design a patch-level fusion module that aggregates global and local cues, together with a Gaussian-weighted splicing strategy that eliminates grid artifacts. Comprehensive evaluations demonstrate that ResDiT consistently delivers high-fidelity, high-resolution image synthesis and integrates seamlessly with downstream tasks, including spatially controlled generation.
REACH: Explicit Recovery Behavior for Diffusion Policies
Authors: Zundong Ke; Junlin Chen; Jiayi Zhu; Kuanhao Xia; Boyi Zhao; Jiayuan Gu
Venue: CVPR 2026 (main)

Abstract: Diffusion policies have emerged as a powerful paradigm for robot learning, but their inherent multi-modality can lead to a diverse set of plausible--though not always optimal--actions from a single observation. We posit that for a given task, an optimal action exists within this distribution. Inspired by negative prompting in generative models, we introduce a novel method that leverages an error detector to identify out-of-distribution (OOD) execution histories and uses them to construct negative action prompts. This allows our policy to steer away from suboptimal behaviors and converge towards higher-performance actions. We present a comprehensive ablation study demonstrating the effectiveness of positive, and negative prompts, and validate our approach on a suite of simulated benchmarks and real-world robotic tasks. Our results show that the proposed Negative-Prompt-guided Diffusion Policy achieves significant improvement in task performance by effectively filtering undesirable action modes.
Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs
Authors: Angela van Sprang; Laurens Samson; Ana Lucic; Erman Acar; Sennay Ghebreab; Yuki M. Asano
Venue: CVPR 2026 (main)

Abstract: We introduce two new benchmarks REST and REST+ (Render-Equivalence Stress Tests) to enable systematic evaluation of cross-modal inconsistency in multimodal large language models (MLLMs). MLLMs are trained to represent vision and language in the same embedding space, yet they cannot perform the same tasks in both modalities. Our benchmarks contain samples with the same semantic information in three modalities (image, text, mixed) and we show that state-of-the-art MLLMs cannot consistently reason over these different modalities. We evaluate 15 MLLMs and find that the degree of modality inconsistency varies substantially, even when accounting for problems with text recognition (OCR). Neither rendering text as image nor rendering an image as text solves the inconsistency. Even if OCR is correct, we find that visual characteristics (text colour and resolution, but not font) and the number of vision tokens have an impact on model performance. Finally, we find that our consistency score correlates with the modality gap between text and images, highlighting a mechanistic interpretation of cross-modal inconsistent MLLMs.