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20222026
most citedSegment Anything Model for Medical Images?

502 citations · 502 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2024

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…

cs.CV2023

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…

cs.CV2022

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…