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Found 13 papers, 5 papers with code
Compressing Large Language Models by Streamlining the Unimportant Layer
Based on this phenomenon, we propose LLM-Streamline, which consists of two parts: layer pruning, where we remove a set of consecutive layers with the lowest importance in the model according to the target sparsity; and layer replacement, where we train a lightweight model to substitute the pruned layers, thereby mitigating the performance degradation caused by pruning.
Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion
In practice, users often desire the ability to control object motion and camera movement independently for customized video creation.
Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields
We then transfer the discrepancy between two 3D geometric models as the discrepancy between their DDFs defined on an identical domain, naturally establishing model correspondence.
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion Model
Recently, text-to-image denoising diffusion probabilistic models (DDPMs) have demonstrated impressive image generation capabilities and have also been successfully applied to image inpainting.
$\rm SP^3$: Enhancing Structured Pruning via PCA Projection
Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency.
Enhancing Dynamic Image Advertising with Vision-Language Pre-training
The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling.
GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation
We present a learning-based method, namely GeoUDF, to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent planes of neighboring points on the surface.
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
In DestFormer, the spatial and temporal dimensions of the 4D point cloud videos are decoupled to achieve efficient self-attention for learning both long-term and short-term features.
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists of three modules, i. e., feature embedding, symmetric overlapping region detection, and pose estimation through the established correspondence.
Ranked #1 on
Image to Point Cloud Registration
on KITTI
Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework
Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video.
A Baseline Framework for Part-level Action Parsing and Action Recognition
This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021.
Explainable Person Re-Identification with Attribute-guided Metric Distillation
In this paper, we propose a post-hoc method, named Attribute-guided Metric Distillation (AMD), to explain existing ReID models.
Ranked #44 on
Person Re-Identification
on DukeMTMC-reID
MLPerf Mobile Inference Benchmark
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks.
