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Found 7 papers, 5 papers with code
GMValuator: Similarity-based Data Valuation for Generative Models
Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to provide data valuation for generation tasks.
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA).
Unleashing Text-to-Image Diffusion Models for Visual Perception
In this paper, we propose VPD (Visual Perception with a pre-trained Diffusion model), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
Ranked #7 on
Referring Expression Segmentation
on RefCoCo val
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection
In particular, we propose to generate random layouts of a scene by making use of the objects in the synthetic CAD dataset and learn the 3D scene representation by applying object-level contrastive learning on two random scenes generated from the same set of synthetic objects.
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input.
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
Knowledge Distillation (KD) has been one of the most popu-lar methods to learn a compact model.
