194 citations · 1.1k across the 147 of their papers we have counts for
167 papers
Feed-Forward Multi-View Feature Coding for 3D Gaussian Splatting
Seungmi Choi, Ji-Hyoun Choi, Suhyun Jo +2
Feed-forward 3D Gaussian Splatting (3DGS) methods enable efficient reconstruction of 3D scenes from sparse multi-view images in a single forward pass, without scene-specific optimi…
Semantically Steerable Image Tokenization
Danilo de Goede, Mohammad Mahdi Derakhshani, Yuki M. Asano +1
Adaptive image tokenizers compress an image into a variable number of tokens matched to its complexity, and recent work makes this allocation controllable: conditioned on a target…
Less from More: Reinforcing Sparse Video Reasoning from Dense References
Wenfang Sun, Yingjun Du, Cees G. M. Snoek
Video-language models commonly assume that more temporal observations lead to more reliable reasoning. We question this assumption and argue that the key challenge is not merely pr…
PlaySuite: A Large-Scale Benchmark for Interactive Visual Intelligence
Dheeraj Varghese, Anna Vettoruzzo, Walter Simoncini +5
Recent advances in multimodal foundation models yield strong performance on static perception and reasoning benchmarks, yet such evaluations largely overlook a central aspect of in…
LiFT: Loop Flow Transformers
Mohammad Mahdi Derakhshani, Pedro M. P. Curvo, Gertjan J. Burghouts +2
We introduce Loop Flow Transformers (LiFT), a family of looped generative models that scales computation by repeatedly applying a shared Diffusion Transformer (DiT) core, with only…
A Hierarchy-Aware Video-Language Model Evaluation and Hyperbolic Baseline for Surgery
Ana Manzano Rodríguez, Pascal Mettes, Marlies P. Schijven +1
Surgical procedures follow a phase-to-step hierarchy, yet the video-language models used to recognize them are evaluated with flat per-level metrics that ignore cross-level coheren…
FineHOI: Part-Aware Dense Representations for Zero-Shot Human-Object Interaction Detection
Francesco Tonini, Lorenzo Vaquero, Mohammad Mahdi Derakhshani +3
Human-Object Interaction (HOI) detection aims to localize humans and objects in images and classify their interactions. Zero-shot HOI focuses on recognizing interactions that are n…
Fourier Self-Supervision for Fine-Grained Generalized Category Discovery
Sarah Rastegar, Mina Ghadimi Atigh, Pascal Mettes +2
Generalized Category Discovery aims to recognize known categories while identifying novel ones within unlabeled data. Existing methods, typically based on self-supervision and cont…
See Only When Needed: Context-Aware Attention Intervention for Mitigating Hallucinations in LVLMs
Yuqing Lei, Wenbo Lyu, Yingjun Du +3
Large Vision-Language Models (LVLMs) excel at multimodal tasks but remain prone to object hallucinations. Prior training-free remedies often uniformly strengthen visual signals, wh…
RegionReasoner: Region-Grounded Multi-Round Visual Reasoning
Wenfang Sun, Hao Chen, Yingjun Du +2
Large vision-language models have achieved remarkable progress in visual reasoning, yet most existing systems rely on single-step or text-only reasoning, limiting their ability to…
Adapting Vision-Language Models for E-commerce Understanding at Scale
Matteo Nulli, Vladimir Orshulevich, Tala Bazazo +9
E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision-Language Models (VLMs) enab…
Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis
Valentina Lilova, Toyesh Chakravorty, Julian I. Bibo +5
Benchmarking 3D spatial understanding of foundation models is essential for real-world applications such as robotics and autonomous driving. Existing evaluations often rely on down…
REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation
Martin Sedlacek, Pavlo Yefanov, Georgy Ponimatkin +7
Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions. However, a key challenge lies in their ability to gen…
GateRA: Token-Aware Modulation for Parameter-Efficient Fine-Tuning
Jie Ou, Shuaihong Jiang, Yingjun Du +1
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, DoRA, and HiRA, enable lightweight adaptation of large pre-trained models via low-rank updates. However, existing PEFT…
TAP-CT: 3D Task-Agnostic Pretraining of Computed Tomography Foundation Models
Tim Veenboer, George Yiasemis, Eric Marcus +4
Existing foundation models (FMs) in the medical domain often require extensive fine-tuning or rely on training resource-intensive decoders, while many existing encoders are pretrai…
Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
Zhen Zeng, Leijiang Gu, Zhangling Duan +4
Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods can remove such content, they o…
Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs
Ziqi Wang, Chang Che, Qi Wang +4
While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominantly focus on models without safe…
MoAlign: Motion-Centric Representation Alignment for Video Diffusion Models
Aritra Bhowmik, Denis Korzhenkov, Cees G. M. Snoek +2
Text-to-video diffusion models have enabled high-quality video synthesis, yet often fail to generate temporally coherent and physically plausible motion. A key reason is the models…
Elastic ViTs from Pretrained Models without Retraining
Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort +2
Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deployment choices under real-world con…
What Layers When: Learning to Skip Compute in LLMs with Residual Gates
Filipe Laitenberger, Dawid Kopiczko, Cees G. M. Snoek +1
We introduce GateSkip, a simple residual-stream gating mechanism that enables token-wise layer skipping in decoder-only LMs. Each Attention/MLP branch is equipped with a sigmoid-li…
Monocular Visual Odometry without Calibration or Test-time Optimization
Vladimir Yugay, Vlardimir Yugay, Duy-Kien Nguyen +3
The most accurate monocular visual odometry systems require known camera intrinsics, refine their estimates with test-time optimization, and recover trajectories only up to an unkn…
Purrception: Variational Flow Matching for Vector-Quantized Image Generation
Răzvan-Andrei Matişan, Vincent Tao Hu, Grigory Bartosh +6
We introduce Purrception, a variational flow matching approach for vector-quantized image generation that provides explicit categorical supervision while maintaining continuous tra…
Dual Guidance Semi-Supervised Action Detection
Ankit Singh, Efstratios Gavves, Cees G. M. Snoek +1
Semi-Supervised Learning (SSL) has shown tremendous potential to improve the predictive performance of deep learning models when annotations are hard to obtain. However, the applic…
MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning
Mohammadreza Salehi, Shashanka Venkataramanan, Ioana Simion +3
Dense self-supervised learning has shown great promise for learning pixel- and patch-level representations, but extending it to videos remains challenging due to the complexity of…