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Found 5 papers, 2 papers with code
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
Sparse-Tuning efficiently fine-tunes the pre-trained ViT by sparsely preserving the informative tokens and merging redundant ones, enabling the ViT to focus on the foreground while reducing computational costs on background regions in the images.
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment
In this letter, we attempt to evaluate the perceptual quality and reconstruction fidelity of SR images considering LR images and scale factors.
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding
Specifically, we propose \textbf{DARA}, a novel PETL method comprising \underline{\textbf{D}}omain-aware \underline{\textbf{A}}dapters (DA Adapters) and \underline{\textbf{R}}elation-aware \underline{\textbf{A}}dapters (RA Adapters) for VG.
VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders
Large-scale text-to-image diffusion models have shown impressive capabilities for generative tasks by leveraging strong vision-language alignment from pre-training.
Study of charmonium-like and fully-charm tetraquark spectroscopy
The masses of tetraquark states of all $qc\bar q \bar c$ and $cc\bar c \bar c$ quark configurations are evaluated in a constituent quark model, where the Cornell-like potential and one-gluon exchange spin-spin coupling are employed.
High Energy Physics - Phenomenology
