SPA unlocks patch-level features in CLIP for class-incremental learning via semantic-guided selection and optimal transport alignment with class descriptions, plus projectors and pseudo-feature replay to reduce forgetting.
Qwen-vl: A versatile vision-language model for understanding, localization.Text Reading, and Beyond, 2(1):1, 2023
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
FIKA-Bench is a leakage-aware benchmark of 311 instances showing that even the best large multimodal models and tool-equipped agents reach only 25.1% accuracy on fine-grained recognition questions that require external evidence search and verification.
TransVLM formalizes Shot Transition Detection as identifying full temporal transition segments rather than single cut points and introduces a VLM that injects optical flow as a motion prior via simple feature fusion, plus a synthetic data engine and benchmark.
citing papers explorer
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Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
SPA unlocks patch-level features in CLIP for class-incremental learning via semantic-guided selection and optimal transport alignment with class descriptions, plus projectors and pseudo-feature replay to reduce forgetting.
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FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition
FIKA-Bench is a leakage-aware benchmark of 311 instances showing that even the best large multimodal models and tool-equipped agents reach only 25.1% accuracy on fine-grained recognition questions that require external evidence search and verification.
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TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions
TransVLM formalizes Shot Transition Detection as identifying full temporal transition segments rather than single cut points and introduces a VLM that injects optical flow as a motion prior via simple feature fusion, plus a synthetic data engine and benchmark.