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Found 39 papers, 13 papers with code
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering
On the other hand, trilinear models such as the CTI model efficiently utilize the inter-modality information between answers, questions, and images, while ignoring intra-modality information.
VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing
Visual entailment (VE) is a multimodal reasoning task consisting of image-sentence pairs whereby a promise is defined by an image, and a hypothesis is described by a sentence.
Adversarial Testing for Visual Grounding via Image-Aware Property Reduction
To this end, we propose PEELING, a text perturbation approach via image-aware property reduction for adversarial testing of the VG model.
Evaluating Decision Optimality of Autonomous Driving via Metamorphic Testing
In this paper, we focus on evaluating the decision-making quality of an ADS and propose the first method for detecting non-optimal decision scenarios (NoDSs), where the ADS does not compute optimal paths for AVs.
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Code-LLaMA architecture, ranging from 7B to 34B parameters.
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues
With the development of LLMs, the security threats of LLMs are getting more and more attention.
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue.
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation
In the second stage, we distill the knowledge from the existing teacher adapters into the student adapter to help its inference.
A Survey on Query-based API Recommendation
We also investigate the typical data extraction procedures and collection approaches employed by the existing approaches.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians.
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
To address this need, we have developed the ConferenceQA dataset for 7 diverse academic conferences with human annotations.
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
In recent times, the generation of 3D assets from text prompts has shown impressive results.
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval
Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conversational Information Retrieval (CIR).
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective
We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis.
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP).
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Solving Math Word Problems via Cooperative Reasoning induced Language Models
This inspires us to develop a cooperative reasoning-induced PLM for solving MWPs, called Cooperative Reasoning (CoRe), resulting in a human-like reasoning architecture with system 1 as the generator and system 2 as the verifier.
Ranked #103 on
Arithmetic Reasoning
on GSM8K
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
The noise is due to a fatal bug in the backend of our relevance assessment interface.
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
We propose a new paradigm for zero-shot learners that is format agnostic, i. e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis.
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge
This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets.
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene
In our experiments, the performance gain brought by GridMask is stronger than spectrum augmentation in ASCs.
Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling
Comparison with existing state-of-the-art oil painting techniques shows that our results have higher fidelity and more realistic textures.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss
This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No. 1 in CLUE Semantic Matching Challenge.
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network
We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption.
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue.
Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis.
Low-Latency Online Speaker Diarization with Graph-Based Label Generation
To ensure the low latency in the online setting, we introduce a variant of AHC, namely chkpt-AHC, to cluster the speakers.
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning
We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios.
The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge
This report describes the submission of the DKU-DukeECE-Lenovo team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2021 track 4.
Sparsely Overlapped Speech Training in the Time Domain: Joint Learning of Target Speech Separation and Personal VAD Benefits
This paper proposes the weighted SI-SNR loss, together with the joint learning of target speech separation and personal VAD.
Change Detection from SAR Images Based on Deformable Residual Convolutional Neural Networks
Convolutional neural networks (CNN) have made great progress for synthetic aperture radar (SAR) images change detection.
TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling
Also, the TransfoRNN model was applied on the LibriSpeech speech recognition task and has shown comparable results with the Transformer models.
Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery
The challenges of the latter task are two folds: (1) pose failure (i. e., pose mismatching -- different skeleton definitions in dataset and SMPL , and pose ambiguity -- endpoints have arbitrary joint angle configurations for the same 3D joint coordinates).
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation
Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively.
Monocular 3D Human Pose Estimation
Unsupervised 3D Human Pose Estimation
Training Wake Word Detection with Synthesized Speech Data on Confusion Words
Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of words that sound similar to the predefined keywords.
