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Found 99 papers, 51 papers with code
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining
We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic.
Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy
Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down.
SVIP: Towards Verifiable Inference of Open-source Large Language Models
Open-source Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language understanding and generation, leading to widespread adoption across various domains.
Iterative Methods via Locally Evolving Set Process
We propose to use the \textit{locally evolving set process}, a novel framework to characterize the algorithm locality, and demonstrate that many standard solvers can be effectively localized.
Independently-Normalized SGD for Generalized-Smooth Nonconvex Optimization
Recent studies have shown that many nonconvex machine learning problems meet a so-called generalized-smooth condition that extends beyond traditional smooth nonconvex optimization.
Does RoBERTa Perform Better than BERT in Continual Learning: An Attention Sink Perspective
Such attention sinks may lead to models' over-smoothing in single-task learning and interference in sequential tasks' learning, which may compromise the models' CL performance despite their high pre-trained capabilities.
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Our insight is that although it is intractable to guarantee hard state-wise constraints in a model-free setting, we can enforce state-wise safety with high probability while excluding strong assumptions.
Image Copy Detection for Diffusion Models
Existing Image Copy Detection (ICD) models, though accurate in detecting hand-crafted replicas, overlook the challenge from diffusion models.
MonoFormer: One Transformer for Both Diffusion and Autoregression
Most existing multimodality methods use separate backbones for autoregression-based discrete text generation and diffusion-based continuous visual generation, or the same backbone by discretizing the visual data to use autoregression for both text and visual generation.
Active Reconfigurable Intelligent Surface Empowered Synthetic Aperture Radar Imaging
In this paper, we first present a range-Doppler (RD) imaging algorithm to obtain imaging results for the proposed ARIS-empowered SAR system.
Knowledge Distillation via Query Selection for Detection Transformer
Transformers have revolutionized the object detection landscape by introducing DETRs, acclaimed for their simplicity and efficacy.
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
Data valuation has emerged as a powerful framework for quantifying each datum's contribution to the training of a machine learning model.
AutoScale: Automatic Prediction of Compute-optimal Data Composition for Training LLMs
Domain reweighting is an emerging research area aimed at adjusting the relative weights of different data sources to improve the effectiveness and efficiency of language model pre-training.
LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction
Existing methods enhance open-vocabulary object detection by leveraging the robust open-vocabulary recognition capabilities of Vision-Language Models (VLMs), such as CLIP. However, two main challenges emerge:(1) A deficiency in concept representation, where the category names in CLIP's text space lack textual and visual knowledge.
Ranked #1 on
Open Vocabulary Object Detection
on LVIS v1.0
Replication in Visual Diffusion Models: A Survey and Outlook
In this survey, we provide the first comprehensive review of replication in visual diffusion models, marking a novel contribution to the field by systematically categorizing the existing studies into unveiling, understanding, and mitigating this phenomenon.
Reachability Analysis for Linear Systems with Uncertain Parameters using Polynomial Zonotopes
We present a reachability algorithm for linear systems with uncertain parameters and inputs using set propagation of polynomial zonotopes.
Assessing Model Generalization in Vicinity
This paper evaluates the generalization ability of classification models on out-of-distribution test sets without depending on ground truth labels.
Dense Connector for MLLMs
We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs.
Automated Multi-level Preference for MLLMs
To verify our viewpoint, we present the Automated Multi-level Preference (AMP) framework for MLLMs.
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels.
AnyPattern: Towards In-context Image Copy Detection
To accommodate the "seen $\rightarrow$ unseen" generalization scenario, we construct the first large-scale pattern dataset named AnyPattern, which has the largest number of tamper patterns ($90$ for training and $10$ for testing) among all the existing ones.
EASE-DETR: Easing the Competition among Object Queries
This paper views the DETR's non-duplicate detection ability as a competition result among object queries.
Representation of forward performance criteria with random endowment via FBSDE and application to forward optimized certainty equivalent
We, also, consider representative examples for both forward performance criteria with random endowment and forward OCE, and for the case of exponential criteria, we investigate the connection between forward OCE and forward entropic risk measures.
Understanding News Creation Intents: Frame, Dataset, and Method
As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest.
On Mask-based Image Set Desensitization with Recognition Support
In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images.
Real-time Estimation of DoS Duration and Frequency for Security Control
In this paper, we develop a new denial-of-service (DoS) estimator, enabling defenders to identify duration and frequency parameters of any DoS attacker, except for three edge cases, exclusively using real-time data.
Data Acquisition: A New Frontier in Data-centric AI
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative.
Absolute Policy Optimization
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios.
A Lightweight and Transferable Design for Robust LEGO Manipulation
Experiments demonstrate that the EOAT can reliably manipulate Lego bricks and the learning framework can effectively and safely improve the manipulation performance to a 100% success rate.
Large-Scale Person Detection and Localization using Overhead Fisheye Cameras
Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras.
State-wise Constrained Policy Optimization
In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO.
Jigsaw: Learning to Assemble Multiple Fractured Objects
Our framework consists of four components: (1) front-end point feature extractor with attention layers, (2) surface segmentation to separate fracture and original parts, (3) multi-parts matching to find correspondences among fracture surface points, and (4) robust global alignment to recover the global poses of the pieces.
Whitening-based Contrastive Learning of Sentence Embeddings
Consequently, using multiple positive samples with enhanced diversity further improves contrastive learning due to better alignment.
Fast Online Node Labeling for Very Large Graphs
This paper studies the online node classification problem under a transductive learning setting.
GUARD: A Safe Reinforcement Learning Benchmark
Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms.
On the Difficulty of Intersection Checking with Polynomial Zonotopes
The standard method for intersection checking with polynomial zonotopes is a two-part algorithm that overapproximates a polynomial zonotope with a regular zonotope and then, if the overapproximation error is deemed too large, splits the set and recursively tries again.
Feature-compatible Progressive Learning for Video Copy Detection
Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content.
DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection
Second, through our design, the object queries and the foreground query in the decoder share consensus on the class semantics, therefore making the strong and weak supervision mutually benefit each other for domain alignment.
TransHP: Image Classification with Hierarchical Prompting
This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task.
Reducing Discretization Error in the Frank-Wolfe Method
The Frank-Wolfe algorithm is a popular method in structurally constrained machine learning applications, due to its fast per-iteration complexity.
Exploring Expression-related Self-supervised Learning for Affective Behaviour Analysis
This paper explores an expression-related self-supervised learning (SSL) method (ContraWarping) to perform expression classification in the 5th Affective Behavior Analysis in-the-wild (ABAW) competition.
A Unified Continual Learning Framework with General Parameter-Efficient Tuning
1) Learning: the pre-trained model adapts to the new task by tuning an online PET module, along with our adaptation speed calibration to align different PET modules, 2) Accumulation: the task-specific knowledge learned by the online PET module is accumulated into an offline PET module through momentum update, 3) Ensemble: During inference, we respectively construct two experts with online/offline PET modules (which are favored by the novel/historical tasks) for prediction ensemble.
Unsupervised Facial Expression Representation Learning with Contrastive Local Warping
Therefore, given a facial image, ContraWarping employs some global transformations and local warping to generate its positive and negative samples and sets up a novel contrastive learning framework.
MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation
The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically.
Pose Synchronization Under Multiple Pair-Wise Relative Poses
The first step performs diffusion and clustering to compute the candidate poses of the input objects.
Context-Aware Pretraining for Efficient Blind Image Decomposition
(2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task.
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification
Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature.
Semisoft Task Clustering for Multi-Task Learning
Motivated by the idea of semisoft clustering of data, we propose a semisoft task clustering approach, which can simultaneously reveal the task cluster structure for both pure and mixed tasks as well as select the relevant features.
An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression
High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients.
Clustered Federated Learning based on Nonconvex Pairwise Fusion
We propose a clustered FL framework that incorporates a nonconvex penalty to pairwise differences of parameters.
Generalizable Re-Identification from Videos with Cycle Association
In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos.
Domain Generalization
Generalizable Person Re-identification
+1
A Multistep Frank-Wolfe Method
The Frank-Wolfe algorithm has regained much interest in its use in structurally constrained machine learning applications.
Feature-Proxy Transformer for Few-Shot Segmentation
With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intricate decoder to perform sophisticated pixel-wise matching, while the supervised segmentation methods use a simple linear classification head.
V$^2$L: Leveraging Vision and Vision-language Models into Large-scale Product Retrieval
While model ensemble is common, we show that combining the vision models and vision-language models brings particular benefits from their complementarity and is a key factor to our superiority.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
Our novel sparse feature sampling module only utilizes local 2D region of interest (RoI) features calculated by the projection of 3D query boxes for further box refinement, leading to a fully-convolutional and deployment-friendly pipeline.
Ranked #8 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Accelerating Frank-Wolfe via Averaging Step Directions
The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity.
Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation Drift
Continual learning (CL) aims to learn a sequence of tasks over time, with data distributions shifting from one task to another.
A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection
Moreover, this paper further reveals a unique difficulty for solving the hard negative problem in ICD, i. e., there is a fundamental conflict between current metric learning and ICD.
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains
Instead of directly aligning the source and target domains against each other, we propose to align the source and target domains against their intermediate domains for a smooth knowledge transfer.
Results and findings of the 2021 Image Similarity Challenge
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
Action Keypoint Network for Efficient Video Recognition
An effective approach is to select informative content from the holistic video, yielding a popular family of dynamic video recognition methods.
Ranked #43 on
Action Recognition
on Something-Something V1
Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification
These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association.
Cross-Modality Person Re-identification
Person Re-Identification
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection
How to align the source and target domains is critical to the CDWSOD accuracy.
Ranked #1 on
Weakly Supervised Object Detection
on Clipart1k
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval
By combining two fundamental learning approaches in DML, e. g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR.
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.
D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection
In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21.
Bag of Tricks and A Strong baseline for Image Copy Detection
In this paper, a bag of tricks and a strong baseline are proposed for image copy detection.
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Model smoothing is of central importance for obtaining a reliable teacher model in the student-teacher framework, where the teacher generates surrogate supervision signals to train the student.
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification
We hold a hypothesis, i. e., if a deep model is capable to fast generalize itself to different domains (using very few samples) during training, it will maintain such domain generalization capacity for testing.
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors.
Screening for a Reweighted Penalized Conditional Gradient Method
We couple this with a screening rule which is safe in the convex case, converging to the true support at a rate $O(1/(\delta^2))$ where $\delta \geq 0$ measures how close the problem is to degeneracy.
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
In this paper, we consider approximate Frank-Wolfe (FW) algorithms to solve convex optimization problems over graph-structured support sets where the linear minimization oracle (LMO) cannot be efficiently obtained in general.
Learning View Selection for 3D Scenes
Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond.
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
Ranked #2 on
Metric Learning
on DyML-Animal
Latent Group Structured Multi-task Learning
In multi-task learning (MTL), we improve the performance of key machine learning algorithms by training various tasks jointly.
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory
With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i. e.}, deep image classification and deep metric learning (on long-tailed data).
Ranked #46 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.
Embodied Self-supervised Learning by Coordinated Sampling and Training
The proposed approach works in an analysis-by-synthesis manner to learn an inference network by iteratively sampling and training.
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Safe Screening for the Generalized Conditional Gradient Method
The conditional gradient method (CGM) has been widely used for fast sparse approximation, having a low per iteration computational cost for structured sparse regularizers.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
K-Best Transformation Synchronization
In this paper, we introduce the problem of K-best transformation synchronization for the purpose of multiple scan matching.
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
We propose a neural network based approach for extracting models from dynamic data using ordinary and partial differential equations.
Ranked #11 on
Image Classification
on Fashion-MNIST
Learning low-dimensional state embeddings and metastable clusters from time series data
This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank.
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images).
Ranked #12 on
Person Re-Identification
on Market-1501-C
Joint Map and Symmetry Synchronization
Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation.
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
Deep Unsupervised Clustering Using Mixture of Autoencoders
Unsupervised clustering is one of the most fundamental challenges in machine learning.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.
Ranked #3 on
Person Re-Identification
on UAV-Human
Straggler Mitigation in Distributed Optimization Through Data Encoding
Slow running or straggler tasks can significantly reduce computation speed in distributed computation.
A Simple Approach to Learn Polysemous Word Embeddings
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
SVDNet for Pedestrian Retrieval
This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (re-ID).
Ranked #14 on
Person Re-Identification
on CUHK03 detected
Dynamic Word Embeddings for Evolving Semantic Discovery
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices
We further develop the asymptotic analysis of the corresponding phase diagrams with and without measurement noise, for different distribution of signals, and discuss the best possible reconstruction performances regardless of the algorithm.
Statistical Mechanics Information Theory Information Theory
Statistical physics-based reconstruction in compressed sensing
Compressed sensing is triggering a major evolution in signal acquisition.
Statistical Mechanics Information Theory Information Theory
