32 citations · 52 across the 13 of their papers we have counts for
10 papers · 1 filter
AHMAD: Adaptive Hybrid Multi-task Vision Learning with Assisted Distillation for Keypoint Detection
Mohammad Mahdi, Nedyalko Prisadnikov, Yuqian Fu +3
Generalist multitasking vision models aim to unify multiple vision tasks within a single framework, enabling more efficient and versatile learning. However, handling diverse vision…
LA4VLA: Learning to Act without Seeing via Language-Action Pretraining
Tao Lin, Yuxin Du, Yiran Mao +13
Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…
-Scene: Physically Grounded Image-to-3D Scene Reconstruction
Haodong Li, Lulu Shao, Haolin Lu +4
Recent image-to-3D scene methods recover high-fidelity 3D objects with plausible arrangements, but often leave floatings and interpenetrations that limit physical validity and down…
Afford-VLA: Action-Aligned Visual Planning via Internalized Affordance
Runze Wang, Yuqian Fu, Yu Li +7
Vision-language-action (VLA) models have shown strong potential for generalist robot manipulation, yet they remain limited by insufficient spatial reasoning, particularly in determ…
StepAudio 2.5 Technical Report
Bin Lin, Bo Zhao, Boyong Wu +98
Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…
Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
Carmelo Scribano, Mohammad Mahdi, Nedyalko Prisadnikov +5
Pretrained vision foundation models deliver strong performance across tasks with limited fine-tuning. However, their Vision Transformer (ViT) backbones impose high inference costs,…
CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation
Linan ZHU, Linan Zhu, Zihao Zhai +7
In video calls and livestreamed discussions, recognizing speakers' emotions helps identify emotionally salient segments and supports fine-grained video content analysis. Multimodal…
Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Tao Lin, Yuxin Du, Jiting Liu +14
Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…
Focusable Monocular Depth Estimation
Yuxin Du, Tao Lin, Zile Zhong +7
Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not distinguish user-specified or task-…
Self-supervised pretraining for an iterative image size agnostic vision transformer
Nedyalko Prisadnikov, Danda Pani Paudel, Yuqian Fu +1
Vision Transformers (ViTs) dominate self-supervised learning (SSL). While they have proven highly effective for large-scale pretraining, they are computationally inefficient and sc…