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Found 4 papers, 2 papers with code
SemanticMIM: Marring Masked Image Modeling with Semantics Compression for General Visual Representation
This paper represents a neat yet effective framework, named SemanticMIM, to integrate the advantages of masked image modeling (MIM) and contrastive learning (CL) for general visual representation.
MMBench: Is Your Multi-modal Model an All-around Player?
In response to these challenges, we propose MMBench, a bilingual benchmark for assessing the multi-modal capabilities of VLMs.
DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency
In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations.
Disassembling Object Representations without Labels
In this paper, we study a new representation-learning task, which we termed as disassembling object representations.
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