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Found 10 papers, 6 papers with code
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment
While compositional approaches that combine separate language and image models show a 111% improvement over unified models at the holistic level, their performance remains suboptimal at both block and image levels.
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model
Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics.
Efficient Inference of Vision Instruction-Following Models with Elastic Cache
Surrounding less important caches are then merged with these anchors, enhancing the preservation of contextual information in the KV caches while yielding an arbitrary acceleration ratio.
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.
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.
