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Found 229 papers, 95 papers with code
C^3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
This paper addresses two primary challenges: the significant redundancy of information across different scales, and the confusion caused by combining features from these scales, which may vary widely in quality.
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively.
Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM
Our main contribution is that the speech input and output modalities can be easily connected to a textual LLM while keeping the LLM's parameters frozen throughout the training process.
Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection
Furthermore, we present VisTA, a simple yet effective baseline method that unifies the tasks of question answering and grounding by delivering both visual and textual answers.
Frozen Large Language Models Can Perceive Paralinguistic Aspects of Speech
As speech becomes an increasingly common modality for interacting with large language models (LLMs), it is becoming desirable to develop systems where LLMs can take into account users' emotions or speaking styles when providing their responses.
OmniGenBench: Automating Large-scale in-silico Benchmarking for Genomic Foundation Models
Recent breakthroughs in GFMs, such as Evo, have attracted significant investment and attention to genomic modeling, as they address long-standing challenges and transform in-silico genomic studies into automated, reliable, and efficient paradigms.
Implicit Euler Discrete-Time Set-Valued Admittance Control for Impact-Contact Force Control
This paper introduces a novel admittance controller that ensures stable force control after impacting unknown stiffness environments by leveraging the differentiability of impact-contact forces.
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
An emerging area of research aims to learn deep generative models with limited training data.
Test-time Training for Hyperspectral Image Super-resolution
In order to better support our test-time training method, we also propose a new network architecture to learn HSI SR without modeling spectral band interaction and propose a new data augmentation method Spectral Mixup to increase the diversity of the training data at test time.
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process.
Improving Factuality in Large Language Models via Decoding-Time Hallucinatory and Truthful Comparators
In this paper, we propose a Comparator-driven Decoding-Time (CDT) framework to alleviate the response hallucination.
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #2 on
Multi-task Language Understanding
on MMLU
Towards scalable efficient on-device ASR with transfer learning
Multilingual pretraining for transfer learning significantly boosts the robustness of low-resource monolingual ASR models.
OmniGenome: Aligning RNA Sequences with Secondary Structures in Genomic Foundation Models
The alignment between RNA sequences and structures in foundation models (FMs) has yet to be thoroughly investigated.
Image-to-Text Logic Jailbreak: Your Imagination can Help You Do Anything
However, no researchers evaluate whether logic understanding capabilities of VLMs in flowchart can influence jailbreak.
Intrinsic PAPR for Point-level 3D Scene Albedo and Shading Editing
Recent advancements in neural rendering have excelled at novel view synthesis from multi-view RGB images.
Local Manifold Learning for No-Reference Image Quality Assessment
This crop is then used to cluster other crops from the same image as the positive class, while crops from different images are treated as negative classes to increase inter-class distance.
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance.
CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report Generation
Automatic medical report generation (MRG), which possesses significant research value as it can aid radiologists in clinical diagnosis and report composition, has garnered increasing attention.
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement
Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25% to 40% compared to the Whisper large-v3 model, with merely 10% model parameters.
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
To bridge this gap, we introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation within the medical multimodal domain.
PAPR in Motion: Seamless Point-level 3D Scene Interpolation
We propose the problem of point-level 3D scene interpolation, which aims to simultaneously reconstruct a 3D scene in two states from multiple views, synthesize smooth point-level interpolations between them, and render the scene from novel viewpoints, all without any supervision between the states.
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1. 5 Pro, as well as open-source image models like InternVL-Chat-V1. 5 and video models like LLaVA-NeXT-Video.
PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery.
IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
One limitation of existing Transformer-based models is that they cannot handle very long sequences as input since their self-attention operations exhibit quadratic time and space complexity.
Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting
To enhance Mamba's ability to preserve historical information in a longer range, we design a novel Mamba+ block by adding a forget gate inside Mamba to selectively combine the new features with the historical features in a complementary manner.
Ranked #38 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective
This paper presents the second part of the two-part survey series on decomposition-based evolutionary multi-objective optimization where we mainly focus on discussing the literature related to multi-objective evolutionary algorithms based on decomposition (MOEA/D).
A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part I-Past and Future
In the first part, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches.
Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
To address this challenge, a sophisticated large language model system named as Xiwu has been developed, allowing you switch between the most advanced foundation models and quickly teach the model domain knowledge.
A General and Efficient Training for Transformer via Token Expansion
The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost.
Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models
We conduct GenAP on three popular code intelligence PLMs with three canonical code intelligence tasks including defect prediction, code summarization, and code translation.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks.
Towards Multimodal Sentiment Analysis Debiasing via Bias Purification
In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases.
Towards Multimodal Human Intention Understanding Debiasing via Subject-Deconfounding
Multimodal intention understanding (MIU) is an indispensable component of human expression analysis (e. g., sentiment or humor) from heterogeneous modalities, including visual postures, linguistic contents, and acoustic behaviors.
Sinkhorn Distance Minimization for Knowledge Distillation
We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
Is the System Message Really Important to Jailbreaks in Large Language Models?
Furthermore, we propose the System Messages Evolutionary Algorithm (SMEA) to generate system messages that are more resistant to jailbreak prompts, even with minor changes.
Multi-Fidelity Methods for Optimization: A Survey
Real-world black-box optimization often involves time-consuming or costly experiments and simulations.
Feature Denoising Diffusion Model for Blind Image Quality Assessment
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks.
ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field
Neural radiance fields (NeRFs) have gained popularity with multiple works showing promising results across various applications.
Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning
The learned preference information is used to progressively guide policy optimization towards policies of interest.
Evolutionary Alternating Direction Method of Multipliers for Constrained Multi-Objective Optimization with Unknown Constraints
Constrained multi-objective optimization problems (CMOPs) pervade real-world applications in science, engineering, and design.
Solving the Catastrophic Forgetting Problem in Generalized Category Discovery
The LER optimizes the distribution of potential known class samples in unlabeled data thus ensuring the preservation of knowledge related to known categories while learning novel classes.
Unleashing Channel Potential: Space-Frequency Selection Convolution for SAR Object Detection
In this paper we propose an efficient convolution module for SAR object detection called SFS-Conv which increases feature diversity within each convolutional layer through a shunt-perceive-select strategy.
How Good Are Deep Generative Models for Solving Inverse Problems?
We assess the validity of these models' outputs as solutions to the inverse problems and conduct a thorough analysis of the reliability of the models' estimates of uncertainty over the solution.
Weakly Supervised Open-Vocabulary Object Detection
Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset.
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks.
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space
On the other hand, the model unconditionally learns the probability distribution of the data $p(X)$ and generates samples that conform to this distribution.
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks.
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation.
Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence
We endeavor to design an efficient and provable method for expensive POCOPs under the framework of constrained Bayesian optimization.
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes
However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios.
Aligning and Prompting Everything All at Once for Universal Visual Perception
However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality interaction, which is not effective in prompting object detection and visual grounding.
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study
Previous efforts on hyperparameter optimization (HPO) of machine learning (ML) models predominately focus on algorithmic advances, yet little is known about the topography of the underlying hyperparameter (HP) loss landscape, which plays a fundamental role in governing the search process of HPO.
Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit
Finally, we deploy our method in a practical problem, specifically in protein structure prediction (PSP).
Towards Robust Text Retrieval with Progressive Learning
However, existing embedding models for text retrieval usually have three non-negligible limitations.
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs
In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data.
NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
Volume rendering requires evaluating an integral along each ray, which is numerically approximated with a finite sum that corresponds to the exact integral along the ray under piecewise constant volume density.
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators
Instruction-based language modeling has received significant attention in pretrained language models.
Woodpecker: Hallucination Correction for Multimodal Large Language Models
Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content.
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have shown promise in solving expensive optimization problems in static environments, the attempts to develop such approaches in dynamic environments remain rarely unexplored.
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model
In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Masked Autoencoders are Efficient Class Incremental Learners
Moreover, MAEs can reliably reconstruct original input images from randomly selected patches, which we use to store exemplars from past tasks more efficiently for CIL.
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
To the best of our knowledge, this work is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception.
LiDAR Meta Depth Completion
While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns.
Prompting Large Language Models with Speech Recognition Abilities
Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings.
Abstractive Text Summarization
Automatic Speech Recognition
+3
PAPR: Proximity Attention Point Rendering
PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry.
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer.
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks.
A Survey on Multimodal Large Language Models
Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks.
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image.
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.
Multi-modal Queried Object Detection in the Wild
To address the learning inertia problem brought by the frozen detector, a vision conditioned masked language prediction strategy is proposed.
Ranked #2 on
Few-Shot Object Detection
on ODinW-35
READ: Recurrent Adaptation of Large Transformers
However, fine-tuning all pre-trained model parameters becomes impractical as the model size and number of tasks increase.
Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images
Firstly, a multi-branch segmentation network is built to learn an expert for each source RSI.
Compressing neural network by tensor network with exponentially fewer variational parameters
In this work, we propose a general compression scheme that significantly reduces the variational parameters of NN by encoding them to deep automatically-differentiable tensor network (ADTN) that contains exponentially-fewer free parameters.
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples
Recent studies have revealed the vulnerability of pre-trained language models to adversarial attacks.
DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion
We propose a factorization that models independent part style and part configuration distributions and presents a novel cross-diffusion network that enables us to generate coherent and plausible shapes under our proposed factorization.
SketchXAI: A First Look at Explainability for Human Sketches
Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Diffusion models with location-scale noise
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it.
HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion
First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process.
RFAConv: Innovating Spatial Attention and Standard Convolutional Operation
In this paper, we propose a new perspective on the effectiveness of spatial attention, which is that the spatial attention mechanism essentially solves the problem of convolutional kernel parameter sharing.
SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates
To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes.
Practical Cross-System Shilling Attacks with Limited Access to Data
With the idea of Cross-system Attack, we design a Practical Cross-system Shilling Attack (PC-Attack) framework that requires little information about the victim RS model and the target RS data for conducting attacks.
Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point.
Photo Pre-Training, but for Sketch
This lack of sketch data has imposed on the community a few "peculiar" design choices -- the most representative of them all is perhaps the coerced utilisation of photo-based pre-training (i. e., no sketch), for many core tasks that otherwise dictates specific sketch understanding.
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation
However, existing methods often struggle to simultaneously minimize the number of modified pixels and the size of the modifications, often requiring a large number of queries and assuming unrestricted access to the targeted DNN.
Fewer is More: Efficient Object Detection in Large Aerial Images
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not.
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.
Ranked #13 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning
EFCIL is of interest because it mitigates concerns about privacy and long-term storage of data, while at the same time alleviating the problem of catastrophic forgetting in incremental learning.
Improving Fast-slow Encoder based Transducer with Streaming Deliberation
Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
A persistent challenge in conditional image synthesis has been to generate diverse output images from the same input image despite only one output image being observed per input image.
Immersive Neural Graphics Primitives
In this paper, we present and evaluate a NeRF-based framework that is capable of rendering scenes in immersive VR allowing users to freely move their heads to explore complex real-world scenes.
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective functions evolve over time.
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition
In this work, we present our Joint Audio/Text training method for Transformer Rescorer, to leverage unpaired text-only data which is relatively cheaper than paired audio-text data.
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs.
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework
Our experimental results on three classification tasks and nine public datasets show that BootAug addresses the performance drop problem and outperforms state-of-the-art text augmentation methods.
Long-Tailed Class Incremental Learning
However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world.
Defend Data Poisoning Attacks on Voice Authentication
With the advances in deep learning, speaker verification has achieved very high accuracy and is gaining popularity as a type of biometric authentication option in many scenes of our daily life, especially the growing market of web services.
LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal
Finally, the LSA modules are further developed to fully use the prior information in non-shadow regions to cleanse the shadow regions.
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+5
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization
Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary.
Ranked #17 on
Open Vocabulary Object Detection
on LVIS v1.0
Learning Best Combination for Efficient N:M Sparsity
In this paper, we show that the N:M learning can be naturally characterized as a combinatorial problem which searches for the best combination candidate within a finite collection.
Efficient Decoder-free Object Detection with Transformers
A natural usage of ViTs in detection is to replace the CNN-based backbone with a transformer-based backbone, which is straightforward and effective, with the price of bringing considerable computation burden for inference.
Evolutionary Multi-Task Injection Testing on Web Application Firewalls
In this paper, we propose DaNuoYi, an automatic injection testing tool that simultaneously generates test inputs for multiple types of injection attacks on a WAF.
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
U-Nets have achieved tremendous success in medical image segmentation.
Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?
Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems.
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
C3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank
Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner.
Streaming parallel transducer beam search with fast-slow cascaded encoders
Streaming ASR with strict latency constraints is required in many speech recognition applications.
Training-free Transformer Architecture Search
Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks.
ARM: Any-Time Super-Resolution Method
To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets.
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
However, these methods suffer from severe performance degradation when quantizing the SR models to ultra-low precision (e. g., 2-bit and 3-bit) with the low-cost layer-wise quantizer.
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer
Our proposed CF-ViT is motivated by two important observations in modern ViT models: (1) The coarse-grained patch splitting can locate informative regions of an input image.
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art
To evaluate the effectiveness of our proposed method, we attack three state-of-the-art image classification models trained on the CIFAR-10 dataset in a targeted manner.
Automated Few-Shot Time Series Forecasting based on Bi-level Programming
One of the critical challenges of time series renewable energy forecasting is the lack of historical data to train an adequate predictive model.
Variational Model Inversion Attacks
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application.
Unlocking the Secrets of Software Configuration Landscapes-Ruggedness, Accessibility, Escapability, and Transferability
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders.
RMNet: Equivalently Removing Residual Connection from Networks
In this paper, we aim to remedy this problem and propose to remove the residual connection in a vanilla ResNet equivalently by a reserving and merging (RM) operation on ResBlock.
LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation
Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification.
Adversarial Defense
Aspect-Based Sentiment Analysis (ABSA)
+2
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution.
Anchor-free Oriented Proposal Generator for Object Detection
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
A Two-Stage Framework to Generate Video Chapter
On top of this dataset, we propose a two-stage framework to perform chapter localization and chapter title generation.