1 citations · 1 across the 11 of their papers we have counts for
13 papers
Toward Comprehensive 3D Grounding: Orientation Grounding through Vision-Language Models
Tuo Liang, Disheng Liu, Nengbo Wang +2
Grounding is a core capability of spatial vision-language models, yet most existing work focuses only on where a referred object is. Many 3D tasks also require knowing how it is or…
Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting
Yunlai Zhou, Yiren Lu, Tuo Liang +3
3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exhibiting holes in unobserved r…
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
Tuo Liang, Zhe Hu, Disheng Liu +2
Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communic…
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting
Disheng Liu, Tuo Liang, Chaoda Song +1
Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approaches to exploiting this potentia…
CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG
Nengbo Wang, Tuo Liang, Vikash Singh +6
Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structure…
Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing
Mengying Wang, Chenhui Ma, Ao Jiao +6
Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predict…
NEBULA: Do We Evaluate Vision-Language-Action Agents Correctly?
Jierui Peng, Yanyan Zhang, Yicheng Duan +3
The evaluation of Vision-Language-Action (VLA) agents is hindered by the coarse, end-task success metric that fails to provide precise skill diagnosis or measure robustness to real…
ERC-SVD: Error-Controlled SVD for Large Language Model Compression
Haolei Bai, Siyong Jian, Tuo Liang +2
Large language models (LLMs) have demonstrated impressive capabilities in a wide range of downstream natural language processing tasks. Nevertheless, their considerable sizes and m…
BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
Yiren Lu, Yunlai Zhou, Disheng Liu +2
3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the q…
When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?
Tuo Liang, Zhe Hu, Jing Li +8
Understanding humor-particularly when it involves complex, contradictory narratives that require comparative reasoning-remains a significant challenge for large vision-language mod…
Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting
Yiren Lu, Yunlai Zhou, Yiran Qiao +5
Open-vocabulary querying in 3D space is crucial for enabling more intelligent perception in applications such as robotics, autonomous systems, and augmented reality. However, most…
CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data
Disheng Liu, Yiran Qiao, Wuche Liu +5
True intelligence hinges on the ability to uncover and leverage hidden causal relations. Despite significant progress in AI and computer vision (CV), there remains a lack of benchm…
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Zhe Hu, Tuo Liang, Jing Li +5
Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding t…