44 citations · 76 across the 13 of their papers we have counts for
13 papers
LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning
Yifan Dai, Zhenhua Wu, Bohan Zeng +18
Joint audio-visual reasoning is essential for omnimodal understanding, yet current multimodal large language models (MLLMs) still struggle when reasoning requires fine-grained evid…
Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers
Bozhou Li, Yushuo Guan, Haolin Li +7
Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pro…
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Yue Ding, Yiyan Ji, Jungang Li +12
Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences le…
The Unseen Bias: How Norm Discrepancy in Pre-Norm MLLMs Leads to Visual Information Loss
Bozhou Li, Xinda Xue, Sihan Yang +5
Multimodal Large Language Models (MLLMs), which couple pre-trained vision encoders and language models, have shown remarkable capabilities. However, their reliance on the ubiquitou…
GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models
Bozhou Li, Sihan Yang, Yushuo Guan +6
The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the generated content. However, its…
VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks
Xinlong Chen, Yuanxing Zhang, Yushuo Guan +9
The "Reason-Then-Respond" paradigm, enhanced by Reinforcement Learning, has shown great promise in advancing Multimodal Large Language Models. However, its application to the video…
Mavors: Multi-granularity Video Representation for Multimodal Large Language Model
Yang Shi, Jiaheng Liu, Yushuo Guan +12
Long-context video understanding in multimodal large language models (MLLMs) faces a critical challenge: balancing computational efficiency with the retention of fine-grained spati…
VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation
Xinlong Chen, Yuanxing Zhang, Chongling Rao +7
The training of controllable text-to-video (T2V) models relies heavily on the alignment between videos and captions, yet little existing research connects video caption evaluation…
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
Yushuo Guan, Ning Liu, Pengyu Zhao +4
The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Structured (channel…
Differentiable Feature Aggregation Search for Knowledge Distillation
Yushuo Guan, Pengyu Zhao, Bingxuan Wang +4
Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distri…
A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling
Shangbang Long, Yushuo Guan, Kaigui Bian +1
Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene. However, recent methods…
Symmetry-constrained Rectification Network for Scene Text Recognition
MingKun Yang, Yushuo Guan, Minghui Liao +5
Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes. Recently, the community has paid increasing attenti…
Rethinking Irregular Scene Text Recognition
Shangbang Long, Yushuo Guan, Bingxuan Wang +2
Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial a…