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Found 19 papers, 11 papers with code
Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning
Pretrained masked language models (PLMs) were shown to be inheriting a considerable amount of relational knowledge from the source corpora.
DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
Furthermore, we propose a learnable deformation representation based on the learnable control points and blending weights, which can deform the template surface non-rigidly while maintaining the consistency of the local shape.
Dynamic 3D Point Cloud Sequences as 2D Videos
The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences.
On the Efficacy of Eviction Policy for Key-Value Constrained Generative Language Model Inference
Despite the recent success associated with Large Language Models (LLMs), they are notably cost-prohibitive to deploy in resource-constrained environments due to their excessive memory and computational demands.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.
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.
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e. g., chemical molecular formula).
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue.
Context Compression for Auto-regressive Transformers with Sentinel Tokens
The quadratic complexity of the attention module makes it gradually become the bulk of compute in Transformer-based LLMs during generation.
EMO: Earth Mover Distance Optimization for Auto-Regressive Language Modeling
In this paper, we propose Earth Mover Distance Optimization (EMO) for auto-regressive language modeling.
Low-Rank Prune-And-Factorize for Language Model Compression
The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy.
Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric
By associating each reference point with two given point clouds through computing its directional distances to them, the difference in directional distances of an identical reference point characterizes the geometric difference between a typical local region of the two point clouds.
Pruning Pre-trained Language Models with Principled Importance and Self-regularization
Iterative pruning is one of the most effective compression methods for pre-trained language models.
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.
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions
Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors.
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
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval
Current text-image approaches (e. g., CLIP) typically adopt dual-encoder architecture us- ing pre-trained vision-language representation.
Multi-turn Response Selection using Dialogue Dependency Relations
In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations.
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions, which incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable.
