GenAI4ELab/papercli-papers huggingface dataset:
Towards Predicting Any Human Trajectory In Context
Authors: Ryo Fujii; Hideo Saito; Ryo Hachiuma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Predicting accurate future trajectories of pedestrians is essential for autonomous systems but remains a challenging task due to the need for adaptability in different environments and domains. A common approach involves collecting scenario-specific data and performing fine-tuning via backpropagation. However, the need to fine-tune for each new scenario is often impractical for deployment on edge devices. To address this challenge, we introduce TrajICL, an In-Context Learning (ICL) framework for pedestrian trajectory prediction that enables adaptation without fine-tuning on the scenario-specific data at inference time without requiring weight updates. We propose a spatio-temporal similarity-based example selection (STES) method that selects relevant examples from previously observed trajectories within the same scene by identifying similar motion patterns at corresponding locations. To further refine this selection, we introduce prediction-guided example selection (PG-ES), which selects examples based on both the past trajectory and the predicted future trajectory, rather than relying solely on the past trajectory. This approach allows the model to account for long-term dynamics when selecting examples. Finally, instead of relying on small real-world datasets with limited scenario diversity, we train our model on a large-scale synthetic dataset to enhance its prediction ability by leveraging in-context examples. Extensive experiments demonstrate that TrajICL achieves remarkable adaptation across both in-domain and cross-domain scenarios, outperforming even fine-tuned approaches across multiple public benchmarks.
Learning to Factorize Spatio-Temporal Foundation Models
Authors: Siru Zhong; Junjie Qiu; Yangyu Wu; Xingchen Zou; Zhongwen Rao; Bin Yang; Chenjuan Guo; Hao Xu; Yuxuan Liang
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Spatio-Temporal Foundation Models (STFMs) promise zero/few-shot generalization across various datasets, yet joint spatio-temporal pretraining is computationally prohibitive and struggles with domain-specific spatial correlations. To this end, we introduce FactoST, a factorized STFM that decouples universal temporal pretraining from spatio-temporal adaptation. The first stage pretrains a space-agnostic backbone with multi-frequency reconstruction and domain-aware prompting, capturing cross-domain temporal regularities at low computational cost. The second stage freezes or further fine-tunes the backbone and attaches an adapter that fuses spatial metadata, sparsifies interactions, and aligns domains with continual memory replay. Extensive forecasting experiments reveal that, in few-shot setting, FactoST reduces MAE by up to 46.4% versus UniST, uses 46.2% fewer parameters, and achieves 68% faster inference than OpenCity, while remaining competitive with expert models. We believe this factorized view offers a practical and scalable path toward truly universal STFMs. The code will be released upon notification.
Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
Authors: Youjia Zhang; Youngeun Kim; Young-Geun Choi; Hongyeob Kim; Huiling Liu; Sungeun Hong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference. Despite notable advances, several challenges still limit its broader applicability. First, most methods rely on backpropagation or iterative optimization, which limits scalability and hinders real-time deployment. Second, they lack explicit modeling of class-conditional feature distributions. This modeling is crucial for producing reliable decision boundaries and calibrated predictions, but it remains underexplored due to the lack of both source data and supervision at test time. In this paper, we propose ADAPT, an Advanced Distribution-Aware and backPropagation-free Test-time adaptation method. We reframe TTA as a probabilistic inference task by modeling class-conditional likelihoods using gradually updated class means and a shared covariance matrix. This enables closed-form, training-free inference. To correct potential likelihood bias, we introduce lightweight regularization guided by CLIP priors and a historical knowledge bank. ADAPT requires no source data, no gradient updates, and no full access to target data, supporting both online and transductive settings. Extensive experiments across diverse benchmarks demonstrate that our method achieves state-of-the-art performance under a wide range of distribution shifts with superior scalability and robustness.
UniViT: Unifying Image and Video Understanding in One Vision Encoder
Authors: Feilong Tang; Xiang An; Haolin Yang; Yin Xie; Kaicheng Yang; Ming Hu; Zheng Cheng; Xingyu Zhou; Zimin Ran; Imran Razzak; Ziyong Feng; Behzad Bozorgtabar; Jiankang Deng; Zongyuan Ge
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Despite the impressive progress of recent pretraining methods on multimodal tasks, existing methods are inherently biased towards either spatial modeling (e.g., CLIP) or temporal modeling (e.g., V-JEPA), limiting their joint capture of spatial details and temporal dynamics. To this end, we propose UniViT, a cluster-driven unified self-supervised learning framework that effectively captures the structured semantics of both image spatial content and video temporal dynamics through event-level and object-level clustering and discrimination. Specifically, we leverage offline clustering to generate semantic clusters across both modalities. For videos, multi-granularity event-level clustering progressively expands from single-event to structured multi-event segments, capturing coarse-to-fine temporal semantics; for images, object-level clustering captures fine-grained spatial semantics. However, while global clustering provides semantically consistent clusters, it lacks modeling of structured semantic relations (e.g., temporal event structures). To address this, we introduce a contrastive objective that leverages these semantic clusters as pseudo-label supervision to explicitly enforce structural constraints, including temporal event relations and spatial object co-occurrences, capturing structured semantics beyond categories. Meanwhile, UniViT jointly embeds structured object-level and event-level semantics into a unified representation space. Furthermore, UniViT introduces two key components: (i) Unified Rotary Position Embedding integrates relative positional embedding with frequency-aware dimension allocation to support position-invariant semantic learning and enhance the stability of structured semantics in the discrimination stage; and (ii) Variable Spatiotemporal Streams adapt to inputs of varying frame lengths, addressing the rigidity of conventional fixed-input approaches. Extensive experiments across varying model scales demonstrate that UniViT achieves state-of-the-art performance on linear probing, attentive probing, question answering, and spatial understanding tasks.
TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Authors: Jiahao Yu; Haozhuang Liu; Yeqiu Yang; Lu Chen; Wu Jian; Yuning Jiang; Bo Zheng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Regression models are crucial in recommender systems. However, retransformation bias problem has been conspicuously neglected within the community. While many works in other fields have devised effective bias correction methods, all of them are post-hoc cures externally to the model, facing practical challenges when applied to real-world recommender systems. Hence, we propose a preemptive paradigm to eradicate the bias intrinsically from the models via minor model refinement. Specifically, a novel TranSUN method is proposed with a joint bias learning manner to offer theoretically guaranteed unbiasedness under empirical superior convergence. It is further generalized into a novel generic regression model family, termed Generalized TranSUN (GTS), which not only offers more theoretical insights but also serves as a generic framework for flexibly developing various bias-free models. Comprehensive experimental results demonstrate the superiority of our methods across data from various domains, which have been successfully deployed in two real-world industrial recommendation scenarios, i.e. product and short video recommendation scenarios in Guess What You Like business domain in the homepage of Taobao App (a leading e-commerce platform with DAU > 300M), to serve the major online traffic.
RrED: Black-box Unsupervised Domain Adaptation via Rectifying-reasoning Errors of Diffusion
Authors: Yuwu Lu; Chunzhi Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Black-box Unsupervised Domain Adaptation (BUDA) aims to transfer source domain knowledge to an unlabeled target domain, without accessing the source data or trained source model. Recent diffusion models have significantly advanced the ability to generate images from texts. While they can produce realistic visuals across diverse prompts and demonstrate impressive compositional generalization, these diffusion-based domain adaptation methods focus solely on composition, overlooking their sensitivity to textual nuances. In this work, we propose a novel diffusion-based method, called Rectifying-reasoning Errors of Diffusion (RrED) for BUDA. RrED is a two-stage learning strategy under diffusion supervision to effectively enhance the target model via the decomposed text and visual encoders from the diffusion model. Specifically, RrED consists of two stages: Diffusion-Target model Rectification (DTR) and Self-rectifying Reasoning Model (SRM). In DTR, we decouple the image and text encoders within the diffusion model: the visual encoder integrates our proposed feature-sensitive module to generate inferentially-enhanced visuals, while the text encoder enables multi-modal joint fine-tuning. In SRM, we prioritize the BUDA task itself, leveraging the target model's differential reasoning capability to rectify errors during learning. Extensive experiments confirm that RrED significantly outperforms other methods on four benchmark datasets, demonstrating its effectiveness in enhancing reasoning and generalization abilities.
EgoThinker: Unveiling Egocentric Reasoning with Spatio-Temporal CoT
Authors: Baoqi Pei; Yifei Huang; Jilan Xu; Yuping He; Guo Chen; Fei Wu; Jiangmiao Pang; Yu Qiao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Egocentric video reasoning centers on an unobservable agent behind the camera who dynamically shapes the environment, requiring inference of hidden intentions and recognition of fine-grained interactions. This core challenge limits current multimodal large language models (MLLMs), which excel at visible event reasoning but lack embodied, first-person understanding. To bridge this gap, we introduce EgoThinker, a novel framework that endows MLLMs with robust egocentric reasoning capabilities through spatio-temporal chain-of-thought supervision and a two-stage learning curriculum. First, we introduce EgoRe-5M, a large-scale egocentric QA dataset constructed from 13M diverse egocentric video clips. This dataset features multi-minute segments annotated with detailed CoT rationales and dense hand–object grounding. Second, we employ SFT on EgoRe-5M to instill reasoning skills, followed by reinforcement fine-tuning (RFT) to further enhance spatio-temporal localization. Experimental results show that EgoThinker outperforms existing methods across multiple egocentric benchmarks, while achieving substantial improvements in fine-grained spatio-temporal localization tasks.
RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models
Authors: Yeongtak Oh; Dohyun Chung; Juhyeon Shin; Sangha Park; Johan Barthelemy; Jisoo Mok; Sungroh Yoon
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent multi-modal large language models (MLLMs) often struggle to generate personalized image captions, even when trained on high-quality captions. In this work, we observe that such limitations persist in existing post-training-based MLLM personalization methods. Specifically, despite being post-tuned with large-scale caption data through supervised fine-tuning (SFT), these models frequently fail to produce faithful descriptions in real-world scenarios, such as multi-concept image captioning. However, acquiring large-scale, high-quality captions for such complex settings is both costly and difficult. To address the data-centric nature of SFT, we propose a reinforcement learning (RL)-based post-training framework. To the best of our knowledge, this is the first RL-based approach to post-train MLLMs for personalized image captioning. Our method significantly enhances both visual recognition and personalized generation capabilities of MLLMs, and consistently outperforms existing SFT-based baselines, especially in the challenging multi-concept image captioning task. Project page: https://github.com/oyt9306/RePIC
DP²O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution
Authors: Rongyuan Wu; Lingchen Sun; Zhengqiang ZHANG; Shihao Wang; Tianhe Wu; Qiaosi Yi; Shuai Li; Lei Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Benefiting from pre-trained text-to-image (T2I) diffusion models, real-world image super-resolution (Real-ISR) methods can synthesize rich and realistic details. However, due to the inherent stochasticity of T2I models, different noise inputs often lead to outputs with varying perceptual quality. Although this randomness is sometimes seen as a limitation, it also introduces a wider perceptual quality range, which can be exploited to improve Real-ISR performance. To this end, we introduce Direct Perceptual Preference Optimization for Real-ISR (DP²O-SR), a framework that aligns generative models with perceptual preferences without requiring costly human annotations. We construct a hybrid reward signal by combining full-reference and no-reference image quality assessment (IQA) models trained on large-scale human preference datasets. This reward encourages both structural fidelity and natural appearance. To better utilize perceptual diversity, we move beyond the standard best-vs-worst selection and construct multiple preference pairs from outputs of the same model. Our analysis reveals that the optimal selection ratio depends on model capacity: smaller models benefit from broader coverage, while larger models respond better to stronger contrast in supervision. Furthermore, we propose hierarchical preference optimization, which adaptively weights training pairs based on intra-group reward gaps and inter-group diversity, enabling more efficient and stable learning. Extensive experiments across both diffusion- and flow-based T2I backbones demonstrate that DP²O-SR significantly improves perceptual quality and generalizes well to real-world benchmarks.
Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
Authors: Hubert Baniecki; Maximilian Muschalik; Fabian Fumagalli; Barbara Hammer; Eyke Hüllermeier; Przemyslaw Biecek
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Language-image pre-training (LIP) enables the development of vision-language models capable of zero-shot classification, localization, multimodal retrieval, and semantic understanding. Various explanation methods have been proposed to visualize the importance of input image-text pairs on the model's similarity outputs. However, popular saliency maps are limited by capturing only first-order attributions, overlooking the complex cross-modal interactions intrinsic to such encoders. We introduce faithful interaction explanations of LIP models (FIxLIP) as a unified approach to decomposing the similarity in vision-language encoders. FIxLIP is rooted in game theory, where we analyze how using the weighted Banzhaf interaction index offers greater flexibility and improves computational efficiency over the Shapley interaction quantification framework. From a practical perspective, we propose how to naturally extend explanation evaluation metrics, such as the pointing game and area between the insertion/deletion curves, to second-order interaction explanations. Experiments on the MS COCO and ImageNet-1k benchmarks validate that second-order methods, such as FIxLIP, outperform first-order attribution methods. Beyond delivering high-quality explanations, we demonstrate the utility of FIxLIP in comparing different models, e.g. CLIP vs. SigLIP-2.
FastJAM: a Fast Joint Alignment Model for Images
Authors: Omri Hirsch; Ron Shapira Weber; Shira Ifergane; Oren Freifeld
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Joint Alignment (JA) of images aims to align a collection of images into a unified coordinate frame, such that semantically-similar features appear at corresponding spatial locations. Most existing approaches often require long training times, large-capacity models,
and extensive hyperparameter tuning. We introduce FastJAM, a rapid, graph-based method that drastically reduces the computational complexity of joint alignment tasks. FastJAM leverages pairwise matches computed by an off-the-shelf image matcher, together with a rapid nonparametric clustering,  to construct a graph representing intra- and inter-image keypoint relations. 
A graph neural network propagates and aggregates these correspondences, efficiently predicting per-image homography parameters via image-level pooling. Utilizing an inverse-compositional loss, that eliminates the need
for a regularization term over the predicted transformations (and thus
also obviates the hyperparameter tuning associated with such terms), FastJAM performs image JA quickly and effectively. Experimental results on several benchmarks demonstrate that FastJAM achieves results better than existing modern JA methods in terms of alignment quality, while reducing computation time from hours or minutes to mere seconds. Our code is available at our project webpage, https://bgu-cs-vil.github.io/FastJAM/.
Multilevel neural simulation-based inference
Authors: Yuga Hikida; Ayush Bharti; Niall Jeffrey; Francois-Xavier Briol
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing a simulator. However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed. In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available. We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget.
Test3R: Learning to Reconstruct 3D at Test Time
Authors: Yuheng Yuan; Qiuhong Shen; Shizun Wang; Xingyi Yang; Xinchao Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce \textbf{Test3R}, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets ($I_1,I_2,I_3$), Test3R generates reconstructions from pairs ($I_1,I_2$) and ($I_1,I_3$). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image $I_1$. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at https://github.com/nopQAQ/Test3R.
One Sample is Enough to Make Conformal Prediction Robust
Authors: Soroush H. Zargarbashi; Mohammad Sadegh Akhondzadeh; Aleksandar Bojchevski
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: For any black-box model, conformal prediction (CP) returns prediction *sets* guaranteed to include the true label with high adjustable probability. Robust CP (RCP) extends the guarantee to the worst case noise up to a pre-defined magnitude.
For RCP, a well-established approach is to use randomized smoothing since it is applicable to any black-box model and provides smaller sets compared to deterministic methods. However, smoothing-based robustness requires many model forward passes per each input which is computationally expensive.
We show that conformal prediction attains some robustness even with *a single forward pass on a randomly perturbed input*. Using any binary certificate we propose a single sample robust CP (RCP1). Our approach returns robust sets with smaller average set size compared to SOTA methods which use many (e.g. $\sim 100$) passes per input. 
Our key insight is to certify the conformal procedure itself rather than individual conformity scores. Our approach is agnostic to the task (classification and regression).  We further extend our approach to smoothing-based robust conformal risk control.
Learning Human-Object Interaction as Groups
Authors: Jiajun Hong; Jianan Wei; Wenguan Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Human-Object Interaction Detection (HOI-DET) aims to localize human-object pairs and identify their interactive relationships. To aggregate contextual cues, existing methods typically propagate information across all detected entities via self‑attention mechanisms, or establish message passing between humans and objects with  bipartite graphs. However, they primarily focus on pairwise relationships, overlooking that interactions in real-world scenarios often emerge from collective behaviors ($\textit{i}.\textit{e}.$, multiple humans and objects engaging in joint activities). In light of this, we revisit relation modeling from a $\textit{group}$ view and propose GroupHOI, a framework that propagates contextual information in terms of $\textit{geometric proximity}$ and $\textit{semantic similarity}$. To exploit the geometric proximity, humans and objects are grouped into distinct clusters using a learnable proximity estimator based on spatial features derived from bounding boxes. In each group, a soft correspondence is computed via self-attention to aggregate and dispatch contextual cues. To incorporate the semantic similarity, we enhance the vanilla transformer-based interaction decoder with local contextual  cues from HO-pair features. Extensive experiments on HICO-DET and V-COCO benchmarks demonstrate the superiority of GroupHOI over the state-of-the-art methods. It also exhibits leading performance on the more challenging Nonverbal Interaction Detection (NVI-DET) task, which involves varied forms of higher-order interactions within groups.