Search Results for author:
Found 18 papers, 16 papers with code
SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches
This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and AcquisitionInspired Morphological Inflection Generation.
Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play
A voice AI agent that blends seamlessly into daily life would interact with humans in an autonomous, real-time, and emotionally expressive manner.
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
Spatial intelligence is a critical component of embodied AI, promoting robots to understand and interact with their environments.
Ranked #1 on
Spatial Reasoning
on EmbSpatial-Bench
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions
Recent advancements in multimodal large language models (MLLMs) have made significant strides in open-world understanding.
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github. com/uni-medical/IMIS-Bench.
Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR
Technically, instance labels are assigned to the unlabeled data in per batch and random augmentation and training are performed few times on these augmented data.
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
This long-context capability allows IXC-2. 5 to excel in tasks requiring extensive input and output contexts.
Ranked #4 on
Video Question Answering
on TVBench
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution.
Ranked #47 on
Visual Question Answering
on MM-Vet
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension.
Ranked #18 on
Visual Question Answering
on MM-Vet v2
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection
Explicitly our method focuses on enhancing labeling using synthetic scenes crafted from 3D shapes generated via random prompts.
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields.
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Ranked #9 on
Visual Question Answering (VQA)
on InfiMM-Eval
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs).
Word Segmentation and Morphological Parsing for Sanskrit
We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
