15 citations · 38 across the 5 of their papers we have counts for
6 papers
AMBER: Multi-View Adaptive Budget Allocation for Listwise Vision-Language Reranking
Wenteng Chen, Jiachen Zhu, Rong Shan +10
Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local candidate views. Existing m…
Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering
Jiachen Zhu, Zhuoying Ou, Congmin Zheng +9
Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly trea…
Variance-Covariance Regularization Improves Representation Learning
Jiachen Zhu, Katrina Evtimova, Yubei Chen +2
Transfer learning plays a key role in advancing machine learning models, yet conventional supervised pretraining often undermines feature transferability by prioritizing features t…
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment
Shraman Pramanick, Li Jing, Sayan Nag +4
Vision-language pre-training (VLP) has recently proven highly effective for various uni- and multi-modal downstream applications. However, most existing end-to-end VLP methods use…
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning
Jiachen Zhu, Rafael M. Moraes, Serkan Karakulak +3
We present Transformation Invariance and Covariance Contrast (TiCo) for self-supervised visual representation learning. Similar to other recent self-supervised learning methods, ou…
Masked Siamese ConvNets
Li Jing, Jiachen Zhu, Yann LeCun
Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to d…