Orionnnn/hub-stats huggingface dataset:
DINOv3
Authors: name: Oriane Siméoni, name: Huy V. Vo, name: Maximilian Seitzer, name: Federico Baldassarre, name: Maxime Oquab, name: Cijo Jose, name: Vasil Khalidov, name: Marc Szafraniec, name: Seungeun Yi, name: Michaël Ramamonjisoa, name: Francisco Massa, name: Daniel Haziza, name: Luca Wehrstedt, name: Jianyuan Wang, name: Timothée Darcet, name: Théo Moutakanni, name: Leonel Sentana, name: Claire Roberts, name: Andrea Vedaldi, name: Jamie Tolan, name: John Brandt, name: Camille Couprie, name: Julien Mairal, name: Hervé Jégou, name: Patrick Labatut, name: Piotr Bojanowski
Published: 2025-08-13 18:00:55

Self-supervised learning holds the promise of eliminating the need for manual
data annotation, enabling models to scale effortlessly to massive datasets and
larger architectures. By not being tailored to specific tasks or domains, this
training paradigm has the potential to learn visual representations from
diverse sources, ranging from natural to aerial images -- using a single
algorithm. This technical report introduces DINOv3, a major milestone toward
realizing this vision by leveraging simple yet effective strategies. First, we
leverage the benefit of scaling both dataset and model size by careful data
preparation, design, and optimization. Second, we introduce a new method called
Gram anchoring, which effectively addresses the known yet unsolved issue of
dense feature maps degrading during long training schedules. Finally, we apply
post-hoc strategies that further enhance our models' flexibility with respect
to resolution, model size, and alignment with text. As a result, we present a
versatile vision foundation model that outperforms the specialized state of the
art across a broad range of settings, without fine-tuning. DINOv3 produces
high-quality dense features that achieve outstanding performance on various
vision tasks, significantly surpassing previous self- and weakly-supervised
foundation models. We also share the DINOv3 suite of vision models, designed to
advance the state of the art on a wide spectrum of tasks and data by providing
scalable solutions for diverse resource constraints and deployment scenarios.
MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and
  Multispectral Earth Observation Data
Authors: name: Antoine Labatie, name: Michael Vaccaro, name: Nina Lardiere, name: Anatol Garioud, name: Nicolas Gonthier
Published: 2025-08-14 17:58:45

Self-supervised learning holds great promise for remote sensing, but standard
self-supervised methods must be adapted to the unique characteristics of Earth
observation data. We take a step in this direction by conducting a
comprehensive benchmark of fusion strategies and reconstruction target
normalization schemes for multimodal, multitemporal, and multispectral Earth
observation data. Based on our findings, we propose MAESTRO, a novel adaptation
of the Masked Autoencoder, featuring optimized fusion strategies and a tailored
target normalization scheme that introduces a spectral prior as a
self-supervisory signal. Evaluated on four Earth observation datasets, MAESTRO
sets a new state-of-the-art on tasks that strongly rely on multitemporal
dynamics, while remaining highly competitive on tasks dominated by a single
mono-temporal modality. Code to reproduce all our experiments is available at
https://github.com/ignf/maestro.
X-Node: Self-Explanation is All We Need
Authors: name: Prajit Sengupta, name: Islem Rekik
Published: 2025-08-14 09:00:45

Graph neural networks (GNNs) have achieved state-of-the-art results in
computer vision and medical image classification tasks by capturing structural
dependencies across data instances. However, their decision-making remains
largely opaque, limiting their trustworthiness in high-stakes clinical
applications where interpretability is essential. Existing explainability
techniques for GNNs are typically post-hoc and global, offering limited insight
into individual node decisions or local reasoning. We introduce X-Node, a
self-explaining GNN framework in which each node generates its own explanation
as part of the prediction process. For every node, we construct a structured
context vector encoding interpretable cues such as degree, centrality,
clustering, feature saliency, and label agreement within its local topology. A
lightweight Reasoner module maps this context into a compact explanation
vector, which serves three purposes: (1) reconstructing the node's latent
embedding via a decoder to enforce faithfulness, (2) generating a natural
language explanation using a pre-trained LLM (e.g., Grok or Gemini), and (3)
guiding the GNN itself via a "text-injection" mechanism that feeds explanations
back into the message-passing pipeline. We evaluate X-Node on two graph
datasets derived from MedMNIST and MorphoMNIST, integrating it with GCN, GAT,
and GIN backbones. Our results show that X-Node maintains competitive
classification accuracy while producing faithful, per-node explanations.
Repository: https://github.com/basiralab/X-Node.
TexVerse: A Universe of 3D Objects with High-Resolution Textures
Authors: name: Yibo Zhang, name: Li Zhang, name: Rui Ma, name: Nan Cao
Published: 2025-08-14 17:43:25

We introduce TexVerse, a large-scale 3D dataset featuring high-resolution
textures. While recent advances in large-scale 3D datasets have enhanced
high-resolution geometry generation, creating high-resolution textures
end-to-end remains underexplored due to the lack of suitable datasets. TexVerse
fills this gap with a curated collection of over 858K unique high-resolution 3D
models sourced from Sketchfab, including more than 158K models with physically
based rendering (PBR) materials. Each model encompasses all of its
high-resolution variants, bringing the total to 1.6M 3D instances. TexVerse
also includes specialized subsets: TexVerse-Skeleton, with 69K rigged models,
and TexVerse-Animation, with 54K animated models, both preserving original
skeleton and animation data uploaded by the user. We also provide detailed
model annotations describing overall characteristics, structural components,
and intricate features. TexVerse offers a high-quality data resource with
wide-ranging potential applications in texture synthesis, PBR material
development, animation, and various 3D vision and graphics tasks.
FantasyTalking2: Timestep-Layer Adaptive Preference Optimization for
  Audio-Driven Portrait Animation
Authors: name: MengChao Wang, name: Qiang Wang, name: Fan Jiang, name: Mu Xu
Published: 2025-08-15 06:43:46

Recent advances in audio-driven portrait animation have demonstrated
impressive capabilities. However, existing methods struggle to align with
fine-grained human preferences across multiple dimensions, such as motion
naturalness, lip-sync accuracy, and visual quality. This is due to the
difficulty of optimizing among competing preference objectives, which often
conflict with one another, and the scarcity of large-scale, high-quality
datasets with multidimensional preference annotations. To address these, we
first introduce Talking-Critic, a multimodal reward model that learns
human-aligned reward functions to quantify how well generated videos satisfy
multidimensional expectations. Leveraging this model, we curate Talking-NSQ, a
large-scale multidimensional human preference dataset containing 410K
preference pairs. Finally, we propose Timestep-Layer adaptive multi-expert
Preference Optimization (TLPO), a novel framework for aligning diffusion-based
portrait animation models with fine-grained, multidimensional preferences. TLPO
decouples preferences into specialized expert modules, which are then fused
across timesteps and network layers, enabling comprehensive, fine-grained
enhancement across all dimensions without mutual interference. Experiments
demonstrate that Talking-Critic significantly outperforms existing methods in
aligning with human preference ratings. Meanwhile, TLPO achieves substantial
improvements over baseline models in lip-sync accuracy, motion naturalness, and
visual quality, exhibiting superior performance in both qualitative and
quantitative evaluations. Ours project page:
https://fantasy-amap.github.io/fantasy-talking2/
Controlling Multimodal LLMs via Reward-guided Decoding
Authors: name: Oscar Mañas, name: Pierluca D'Oro, name: Koustuv Sinha, name: Adriana Romero-Soriano, name: Michal Drozdzal, name: Aishwarya Agrawal
Published: 2025-08-15 17:29:06

As Multimodal Large Language Models (MLLMs) gain widespread applicability, it
is becoming increasingly desirable to adapt them for diverse user needs. In
this paper, we study the adaptation of MLLMs through controlled decoding. To
achieve this, we introduce the first method for reward-guided decoding of MLLMs
and demonstrate its application in improving their visual grounding. Our method
involves building reward models for visual grounding and using them to guide
the MLLM's decoding process. Concretely, we build two separate reward models to
independently control the degree of object precision and recall in the model's
output. Our approach enables on-the-fly controllability of an MLLM's inference
process in two ways: first, by giving control over the relative importance of
each reward function during decoding, allowing a user to dynamically trade off
object precision for recall in image captioning tasks; second, by giving
control over the breadth of the search during decoding, allowing the user to
control the trade-off between the amount of test-time compute and the degree of
visual grounding. We evaluate our method on standard object hallucination
benchmarks, showing that it provides significant controllability over MLLM
inference, while consistently outperforming existing hallucination mitigation
methods.
SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via
  Class-Conditioned Image Translation
Authors: name: Guido Manni, name: Clemente Lauretti, name: Loredana Zollo, name: Paolo Soda
Published: 2025-08-08 16:16:43

Deep learning has revolutionized medical imaging, but its effectiveness is
severely limited by insufficient labeled training data. This paper introduces a
novel GAN-based semi-supervised learning framework specifically designed for
low labeled-data regimes, evaluated across settings with 5 to 50 labeled
samples per class. Our approach integrates three specialized neural networks --
a generator for class-conditioned image translation, a discriminator for
authenticity assessment and classification, and a dedicated classifier --
within a three-phase training framework. The method alternates between
supervised training on limited labeled data and unsupervised learning that
leverages abundant unlabeled images through image-to-image translation rather
than generation from noise. We employ ensemble-based pseudo-labeling that
combines confidence-weighted predictions from the discriminator and classifier
with temporal consistency through exponential moving averaging, enabling
reliable label estimation for unlabeled data. Comprehensive evaluation across
eleven MedMNIST datasets demonstrates that our approach achieves statistically
significant improvements over six state-of-the-art GAN-based semi-supervised
methods, with particularly strong performance in the extreme 5-shot setting
where the scarcity of labeled data is most challenging. The framework maintains
its superiority across all evaluated settings (5, 10, 20, and 50 shots per
class). Our approach offers a practical solution for medical imaging
applications where annotation costs are prohibitive, enabling robust
classification performance even with minimal labeled data. Code is available at
https://github.com/GuidoManni/SPARSE.
StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image
  Translation
Authors: name: Seungmi Lee, name: Kwan Yun, name: Junyong Noh
Published: 2025-08-15 04:29:46

We introduce StyleMM, a novel framework that can construct a stylized 3D
Morphable Model (3DMM) based on user-defined text descriptions specifying a
target style. Building upon a pre-trained mesh deformation network and a
texture generator for original 3DMM-based realistic human faces, our approach
fine-tunes these models using stylized facial images generated via text-guided
image-to-image (i2i) translation with a diffusion model, which serve as
stylization targets for the rendered mesh. To prevent undesired changes in
identity, facial alignment, or expressions during i2i translation, we introduce
a stylization method that explicitly preserves the facial attributes of the
source image. By maintaining these critical attributes during image
stylization, the proposed approach ensures consistent 3D style transfer across
the 3DMM parameter space through image-based training. Once trained, StyleMM
enables feed-forward generation of stylized face meshes with explicit control
over shape, expression, and texture parameters, producing meshes with
consistent vertex connectivity and animatability. Quantitative and qualitative
evaluations demonstrate that our approach outperforms state-of-the-art methods
in terms of identity-level facial diversity and stylization capability. The
code and videos are available at
[kwanyun.github.io/stylemm_page](kwanyun.github.io/stylemm_page).
Thyme: Think Beyond Images
Authors: name: Yi-Fan Zhang, name: Xingyu Lu, name: Shukang Yin, name: Chaoyou Fu, name: Wei Chen, name: Xiao Hu, name: Bin Wen, name: Kaiyu Jiang, name: Changyi Liu, name: Tianke Zhang, name: Haonan Fan, name: Kaibing Chen, name: Jiankang Chen, name: Haojie Ding, name: Kaiyu Tang, name: Zhang Zhang, name: Liang Wang, name: Fan Yang, name: Tingting Gao, name: Guorui Zhou
Published: 2025-08-15 17:59:49

Following OpenAI's introduction of the ``thinking with images'' concept,
recent efforts have explored stimulating the use of visual information in the
reasoning process to enhance model performance in perception and reasoning
tasks. However, to the best of our knowledge, no open-source work currently
offers a feature set as rich as proprietary models (O3), which can perform
diverse image manipulations and simultaneously enhance logical reasoning
capabilities through code. In this paper, we make a preliminary attempt in this
direction by introducing Thyme (Think Beyond Images), a novel paradigm for
enabling MLLMs to transcend existing ``think with images'' approaches by
autonomously generating and executing diverse image processing and
computational operations via executable code. This approach not only
facilitates a rich, on-the-fly set of image manipulations (e.g., cropping,
rotation, contrast enhancement) but also allows for mathematical computations,
all while maintaining high autonomy in deciding when and how to apply these
operations. We activate this capability through a two-stage training strategy:
an initial SFT on a curated dataset of 500K samples to teach code generation,
followed by a RL phase to refine decision-making. For the RL stage, we manually
collect and design high-resolution question-answer pairs to increase the
learning difficulty, and we propose GRPO-ATS (Group Relative Policy
Optimization with Adaptive Temperature Sampling), an algorithm that applies
distinct temperatures to text and code generation to balance reasoning
exploration with code execution precision. We conduct extensive experimental
analysis and ablation studies. Comprehensive evaluations on nearly 20
benchmarks show that Thyme yields significant and consistent performance gains,
particularly in challenging high-resolution perception and complex reasoning
tasks.
PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical
  Register Indexing
Authors: name: Zhuoqun Li, name: Xuanang Chen, name: Hongyu Lin, name: Yaojie Lu, name: Xianpei Han, name: Le Sun
Published: 2025-08-14 23:43:46

Paper search is an important activity for researchers, typically involving
using a query with description of a topic to find relevant papers. As research
deepens, paper search requirements may become more flexible, sometimes
involving specific details such as module configuration rather than being
limited to coarse-grained topics. However, previous paper search systems are
unable to meet these flexible-grained requirements, as these systems mainly
collect paper abstracts to construct index of corpus, which lack detailed
information to support retrieval by finer-grained queries. In this work, we
propose PaperRegister, consisted of offline hierarchical indexing and online
adaptive retrieval, transforming traditional abstract-based index into
hierarchical index tree for paper search, thereby supporting queries at
flexible granularity. Experiments on paper search tasks across a range of
granularity demonstrate that PaperRegister achieves the state-of-the-art
performance, and particularly excels in fine-grained scenarios, highlighting
the good potential as an effective solution for flexible-grained paper search
in real-world applications. Code for this work is in
https://github.com/Li-Z-Q/PaperRegister.
Puppeteer: Rig and Animate Your 3D Models
Authors: name: Chaoyue Song, name: Xiu Li, name: Fan Yang, name: Zhongcong Xu, name: Jiacheng Wei, name: Fayao Liu, name: Jiashi Feng, name: Guosheng Lin, name: Jianfeng Zhang
Published: 2025-08-14 17:59:31

Modern interactive applications increasingly demand dynamic 3D content, yet
the transformation of static 3D models into animated assets constitutes a
significant bottleneck in content creation pipelines. While recent advances in
generative AI have revolutionized static 3D model creation, rigging and
animation continue to depend heavily on expert intervention. We present
Puppeteer, a comprehensive framework that addresses both automatic rigging and
animation for diverse 3D objects. Our system first predicts plausible skeletal
structures via an auto-regressive transformer that introduces a joint-based
tokenization strategy for compact representation and a hierarchical ordering
methodology with stochastic perturbation that enhances bidirectional learning
capabilities. It then infers skinning weights via an attention-based
architecture incorporating topology-aware joint attention that explicitly
encodes inter-joint relationships based on skeletal graph distances. Finally,
we complement these rigging advances with a differentiable optimization-based
animation pipeline that generates stable, high-fidelity animations while being
computationally more efficient than existing approaches. Extensive evaluations
across multiple benchmarks demonstrate that our method significantly
outperforms state-of-the-art techniques in both skeletal prediction accuracy
and skinning quality. The system robustly processes diverse 3D content, ranging
from professionally designed game assets to AI-generated shapes, producing
temporally coherent animations that eliminate the jittering issues common in
existing methods.
When Explainability Meets Privacy: An Investigation at the Intersection
  of Post-hoc Explainability and Differential Privacy in the Context of Natural
  Language Processing
Authors: name: Mahdi Dhaini, name: Stephen Meisenbacher, name: Ege Erdogan, name: Florian Matthes, name: Gjergji Kasneci
Published: 2025-08-14 09:34:29

In the study of trustworthy Natural Language Processing (NLP), a number of
important research fields have emerged, including that of
explainability and privacy. While research interest in both
explainable and privacy-preserving NLP has increased considerably in recent
years, there remains a lack of investigation at the intersection of the two.
This leaves a considerable gap in understanding of whether achieving
both explainability and privacy is possible, or whether the two are at
odds with each other. In this work, we conduct an empirical investigation into
the privacy-explainability trade-off in the context of NLP, guided by the
popular overarching methods of Differential Privacy (DP) and Post-hoc
Explainability. Our findings include a view into the intricate relationship
between privacy and explainability, which is formed by a number of factors,
including the nature of the downstream task and choice of the text
privatization and explainability method. In this, we highlight the potential
for privacy and explainability to co-exist, and we summarize our findings in a
collection of practical recommendations for future work at this important
intersection.
STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Authors: name: Yushi Lan, name: Yihang Luo, name: Fangzhou Hong, name: Shangchen Zhou, name: Honghua Chen, name: Zhaoyang Lyu, name: Shuai Yang, name: Bo Dai, name: Chen Change Loy, name: Xingang Pan
Published: 2025-08-14 17:58:05

We present STream3R, a novel approach to 3D reconstruction that reformulates
pointmap prediction as a decoder-only Transformer problem. Existing
state-of-the-art methods for multi-view reconstruction either depend on
expensive global optimization or rely on simplistic memory mechanisms that
scale poorly with sequence length. In contrast, STream3R introduces an
streaming framework that processes image sequences efficiently using causal
attention, inspired by advances in modern language modeling. By learning
geometric priors from large-scale 3D datasets, STream3R generalizes well to
diverse and challenging scenarios, including dynamic scenes where traditional
methods often fail. Extensive experiments show that our method consistently
outperforms prior work across both static and dynamic scene benchmarks.
Moreover, STream3R is inherently compatible with LLM-style training
infrastructure, enabling efficient large-scale pretraining and fine-tuning for
various downstream 3D tasks. Our results underscore the potential of causal
Transformer models for online 3D perception, paving the way for real-time 3D
understanding in streaming environments. More details can be found in our
project page: https://nirvanalan.github.io/projects/stream3r.
A Survey on Diffusion Language Models
Authors: name: Tianyi Li, name: Mingda Chen, name: Bowei Guo, name: Zhiqiang Shen
Published: 2025-08-14 17:47:22

Diffusion Language Models (DLMs) are rapidly emerging as a powerful and
promising alternative to the dominant autoregressive (AR) paradigm. By
generating tokens in parallel through an iterative denoising process, DLMs
possess inherent advantages in reducing inference latency and capturing
bidirectional context, thereby enabling fine-grained control over the
generation process. While achieving a several-fold speed-up, recent
advancements have allowed DLMs to show performance comparable to their
autoregressive counterparts, making them a compelling choice for various
natural language processing tasks. In this survey, we provide a holistic
overview of the current DLM landscape. We trace its evolution and relationship
with other paradigms, such as autoregressive and masked language models, and
cover both foundational principles and state-of-the-art models. Our work offers
an up-to-date, comprehensive taxonomy and an in-depth analysis of current
techniques, from pre-training strategies to advanced post-training methods.
Another contribution of this survey is a thorough review of DLM inference
strategies and optimizations, including improvements in decoding parallelism,
caching mechanisms, and generation quality. We also highlight the latest
approaches to multimodal extensions of DLMs and delineate their applications
across various practical scenarios. Furthermore, our discussion addresses the
limitations and challenges of DLMs, including efficiency, long-sequence
handling, and infrastructure requirements, while outlining future research
directions to sustain progress in this rapidly evolving field. Project GitHub
is available at https://github.com/VILA-Lab/Awesome-DLMs.