3 citations · 7 across the 26 of their papers we have counts for
12 papers · 1 filter
LoSA: Near-Lossless Sparse Attention for Training-Free Video Diffusion Acceleration
Enhuai Liu, Yunke Wang, Yutong Wang +2
Video diffusion transformers are costly to sample: every denoising step applies self-attention over a long 3D token sequence, a quadratic cost that dominates as resolution and dura…
PARE: Pruning and Adaptive Routing for Efficient Video Generation
Yutong Wang, Yunke Wang, Tianfan Xue +4
Video Diffusion Transformers (DiTs) generate high-quality videos but demand substantial compute due to wide blocks, deep architectures, and iterative sampling. Recent methods reduc…
BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation
Yutong Wang, Yunke Wang, Xinyuan Chen +1
Automatic movie trailer generation must select shots from a full-length film and synchronize them with background music. Existing methods either relegate music alignment to post-pr…
Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation
Jingxuan He, Xiyu Wang, Yunke Wang +2
Instruction-based image editing (IIE) models have recently demonstrated strong capability in modifying specific image regions according to natural language instructions, which impl…
Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
Chenyu Hui, Xiaodi Huang, Siyu Xu +5
Vision-language-action (VLA) models typically rely on large-scale real-world videos, whereas simulated data, despite being inexpensive and highly parallelizable to collect, often s…
Rethinking Where to Edit: Task-Aware Localization for Instruction-Based Image Editing
Jingxuan He, Xiyu Wang, Mengyu Zheng +3
Instruction-based image editing (IIE) aims to modify an image according to a natural language instruction. Despite recent advances in diffusion transformers, existing methods often…
MambaScope: Coarse-to-Fine Scoping for Efficient Vision Mamba
Shanhui Liu, Rui Xu, Yunke Wang
Vision Mamba has emerged as a promising and efficient alternative to Vision Transformers, yet its efficiency remains fundamentally constrained by the number of input tokens. Existi…
Rethinking Visual Token Reduction in LVLMs Under Cross-Modal Misalignment
Rui Xu, Yunke Wang, Yong Luo +1
Large Vision-Language Models (LVLMs) encode visual inputs as dense sequences of patch-level tokens to capture fine-grained semantics. These visual tokens often outnumber their text…
Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation
Laibin Chang, Yunke Wang, JiaXing Huang +3
Marine Saliency Segmentation (MSS) plays a pivotal role in various vision-based marine exploration tasks. However, existing marine segmentation techniques face the dilemma of objec…
FusionSAM: Visual Multi-Modal Learning with Segment Anything
Daixun Li, Weiying Xie, Mingxiang Cao +5
Multimodal image fusion and semantic segmentation are critical for autonomous driving. Despite advancements, current models often struggle with segmenting densely packed elements d…
CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V
Siyu Xu, Yunke Wang, Daochang Liu +2
Recent advancements in generative AI have suggested that by taking visual prompts, GPT-4V can demonstrate significant proficiency in visual recognition tasks. Despite its impressiv…
Multi-Tailed Vision Transformer for Efficient Inference
Yunke Wang, Bo Du, Wenyuan Wang +1
Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the seque…