activity
20192026
most citedA CLIP-Enhanced Method for Video-Language Understanding

7 citations · 13 across the 4 of their papers we have counts for

collaborators

6 papers

cond-mat.str-el2026

Resolving Spectral Gaps of the Frustrated Spin-1/2 - Chain via Dual Spin-Sector Variational Quantum Eigensolver

Sk Saniur Rahaman

Finding excited states and resolving spectral gaps in frustrated quantum spin systems using Variational Quantum Eigensolvers is complicated by state-ordering inversions. In the fru…

cs.CV2026

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

Feng He, Zhenting Wang, Qifan Wang +4

Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses o…

cs.CV2023

Improving Video Retrieval by Adaptive Margin

Feng He, Qi Wang, Zhifan Feng +4

Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representatio…

cs.CV2022★ 1 cited

CLOP: Video-and-Language Pre-Training with Knowledge Regularizations

Guohao Li, Hu Yang, Feng He +4

Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner,…

cs.CV2021★ 7 cited

A CLIP-Enhanced Method for Video-Language Understanding

Guohao Li, Feng He, Zhifan Feng

This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark.github.io/challenge\_2021.html). We propo…

cs.CL2019★ 5 cited

mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation

Dayiheng Liu, Xu Yang, Feng He +2

It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem. The model w…