311 citations · 332 across the 10 of their papers we have counts for
10 papers
Counting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language Models
Liwei Che, Zhiyu Xue, Yihao Quan +7
Counting serves as a simple but powerful test of a Large Vision-Language Model's (LVLM's) reasoning; it forces the model to identify each individual object and then add them all up…
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model
Benlin Liu, Yuhao Dong, Yiqin Wang +6
Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. C…
Efficient Inference of Vision Instruction-Following Models with Elastic Cache
Zuyan Liu, Benlin Liu, Jiahui Wang +5
In the field of instruction-following large vision-language models (LVLMs), the efficient deployment of these models faces challenges, notably due to the high memory demands of the…
GMValuator: Similarity-based Data Valuation for Generative Models
Jiaxi Yang, Wenglong Deng, Benlin Liu +3
Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained atten…
Unleashing Text-to-Image Diffusion Models for Visual Perception
Wenliang Zhao, Yongming Rao, Zuyan Liu +3
Diffusion models (DMs) have become the new trend of generative models and have demonstrated a powerful ability of conditional synthesis. Among those, text-to-image diffusion models…
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
Yushi Hu, Benlin Liu, Jungo Kasai +4
Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align w…
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection
Yongming Rao, Benlin Liu, Yi Wei +3
3D point cloud understanding has made great progress in recent years. However, one major bottleneck is the scarcity of annotated real datasets, especially compared to 2D object det…
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu +3
Attention is sparse in vision transformers. We observe the final prediction in vision transformers is only based on a subset of most informative tokens, which is sufficient for acc…
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Xin Wang, Thomas E. Huang, Benlin Liu +4
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.…
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
Benlin Liu, Yongming Rao, Jiwen Lu +2
Knowledge Distillation (KD) has been one of the most popu-lar methods to learn a compact model. However, it still suffers from highdemand in time and computational resources caused…