1 citations · 1 across the 9 of their papers we have counts for
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CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation
Tingyu Song, Mingxin Li, Yanzhao Zhang +5
MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different attribute-object bindings. Yet…
UEmbed: Unified Sparse and Dense Multimodal Embeddings
Tingyu Song, Mingxin Li, Yanzhao Zhang +5
Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exac…
An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models
Xinxin Lin, Guangxin Dai, Yi Zhong +25
Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requires substantial data and expert a…
Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing
Tingyu Song, Yanzhao Zhang, Mingxin Li +6
Composed Image Retrieval (CIR) is a pivotal and complex task in multimodal understanding. Current CIR benchmarks typically feature limited query categories and fail to capture the…