502 citations · 502 across the 7 of their papers we have counts for
8 papers
Enhancing Long-Video VLM Embeddings with Query-Aware Streaming Latent Reasoning
Haozhe Chi, Song Jin, Yang Jin +1
Long-video embedding requires capturing sparse query-relevant evidence under a limited visual-token budget. Uniform sampling can miss brief events in videos spanning minutes or hou…
Allocate Before You Embed: Adaptive Visual Input Allocation for Video Embeddings
Song Jin, Zhongtao Jiang, Chenglei Shen +5
Large-scale video retrieval requires embedding models to encode long and diverse videos under tight visual-input and inference budgets. Existing methods typically sample a small, f…
Balancing Image Compression and Generation with Bootstrapped Tokenization
Haozhe Chi, Jinghan Li, Hao Jiang +4
Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens. The mi…
GOPAgen: Codec-Aware Agentic Long-Video Understanding with Structured Memory
Haozhe Chi, Yang Jin, Yadong Mu
Agentic long-video question answering often relies on sparse RGB sampling and caption retrieval. This reliance can lead to missed brief motion events and repeated processing of irr…
RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
Zhicheng Sun, Zhenhao Yang, Yang Jin +7
Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require tra…
Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification
Yuhao Huang, Xin Yang, Xiaoqiong Huang +7
Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep…
Segment Anything Model for Medical Images?
Yuhao Huang, Xin Yang, Lian Liu +16
The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. Howev…
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality
Haozhe Chi, Minghua Yang, Junhao Zhu +2
Multimodal sentiment analysis (MSA) is an important way of observing mental activities with the help of data captured from multiple modalities. However, due to the recording or tra…