Auto-FlexSwitch achieves efficient dynamic model merging by decomposing task vectors into sparse masks, signs, and scalars, then making the compression learnable via gating and adaptive bit selection with KNN-based retrieval.
End-to-end object detection with transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.
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Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression
Auto-FlexSwitch achieves efficient dynamic model merging by decomposing task vectors into sparse masks, signs, and scalars, then making the compression learnable via gating and adaptive bit selection with KNN-based retrieval.
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TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection
TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.