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Found 44 papers, 14 papers with code
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
These methods further deteriorate on small, imbalanced data that has a large degree of class overlap.
Unsupervised Learning of Discriminative Attributes and Visual Representations
Attributes offer useful mid-level features to interpret visual data.
Learning Deep Representation for Imbalanced Classification
We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both inter-cluster and inter-class margins.
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking.
Local Similarity-Aware Deep Feature Embedding
Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric.
Ranked #27 on
Metric Learning
on CUB-200-2011
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking.
Learning to Disambiguate by Asking Discriminative Questions
In particular, each tuple consists of a pair of images and 4. 6 discriminative questions (as positive samples) and 5. 9 non-discriminative questions (as negative samples) on average.
Learning Policies for Adaptive Tracking with Deep Feature Cascades
Our fundamental insight is to take an adaptive approach, where easy frames are processed with cheap features (such as pixel values), while challenging frames are processed with invariant but expensive deep features.
CNNs are Globally Optimal Given Multi-Layer Support
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs.
Mask-aware Photorealistic Face Attribute Manipulation
The task of face attribute manipulation has found increasing applications, but still remains challeng- ing with the requirement of editing the attributes of a face image while preserving its unique details.
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Data for face analysis often exhibit highly-skewed class distribution, i. e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances.
Pose Guided Human Video Generation
Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance.
Dense Intrinsic Appearance Flow for Human Pose Transfer
Unlike existing methods, we propose to estimate dense and intrinsic 3D appearance flow to better guide the transfer of pixels between poses.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric.
Neural-DINF: A Neural Network based Framework for Measuring Document Influence
Measuring the scholarly impact of a document without citations is an important and challenging problem.
FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
We propose a simple yet effective method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the data scarcity issue by augmenting the feature space especially for rare classes.
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics
This leads to consistent improvements since the value function provides effective metric supervision during finetuning, and helps to correct the potential bias of loss-only supervision.
Uniform Inference on High-dimensional Spatial Panel Networks
We propose employing a debiased-regularized, high-dimensional generalized method of moments (GMM) framework to perform inference on large-scale spatial panel networks.
An Attention Free Transformer
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention.
A Dot Product Attention Free Transformer
We introduce Dot Product Attention Free Transformer (DAFT), an efficient variant of Transformers \citep{transformer} that eliminates the query-key dot product in self attention.
Ranked #623 on
Image Classification
on ImageNet
A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios
We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements.
Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling
This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels.
Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization
To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence techniques.
Open-Vocabulary DETR with Conditional Matching
To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image.
Ranked #21 on
Open Vocabulary Object Detection
on MSCOCO
An Empirical Study of Language Model Integration for Transducer based Speech Recognition
Based on the DR method, we propose a low-order density ratio method (LODR) by replacing the estimation with a low-order weak language model.
Efficient Representation Learning via Adaptive Context Pooling
Inspired by the success of ConvNets that are combined with pooling to capture long-range dependencies, we learn to pool neighboring features for each token before computing attention in a given attention layer.
Position Prediction as an Effective Pretraining Strategy
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec models in Speech and, recently, in MAE models in Vision, forces the model to learn about relationships between the content in different parts of the input using autoencoding related objectives.
A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications
Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios.
Pre-Training Representations of Binary Code Using Contrastive Learning
To the best of our knowledge, COMBO is the first language representation model that incorporates source code, binary code, and comments into contrastive code representation learning and unifies multiple tasks for binary code analysis.
Unified Vision and Language Prompt Learning
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision community since the emergence of large vision-language models like CLIP.
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors
We do so by Masked Augmentation Subspace Training (or MAST) to encode in the single feature space the priors from different data augmentations in a factorized way.
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch
This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature.
DUET: 2D Structured and Approximately Equivariant Representations
We propose 2D strUctured and EquivarianT representations (coined DUET), which are 2d representations organized in a matrix structure, and equivariant with respect to transformations acting on the input data.
Cryo-Electron Ptychography: Applications and Potential in Biological Characterisation
There is a clear need for developments in characterisation techniques that provide detailed information about structure-function relationships in biology.
Adaptivity and Modularity for Efficient Generalization Over Task Complexity
We investigate how the use of a mechanism for adaptive and modular computation in transformers facilitates the learning of tasks that demand generalization over the number of sequential computation steps (i. e., the depth of the computation graph).
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures.
Towards Equipping Transformer with the Ability of Systematic Compositionality
One of the key factors in language productivity and human cognition is the ability of systematic compositionality, which refers to understanding composed unseen examples of seen primitives.
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model
Motivated by this, we develop a time-efficient approach called DREditor to edit the matching rule of an off-the-shelf dense retrieval model to suit a specific domain.
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
To this end, various methods have been proposed to create an adaptive filter by incorporating an extra filter (e. g., a high-pass filter) extracted from the graph topology.
Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks.
Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
However, the estimator is severely biased when the data's time series dimension $T$ is long due to the large degree of overidentification.
Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents that must engage in tailored strategic planning for diverse users to secure a favorable agreement.
Concept -- An Evaluation Protocol on Conversation Recommender Systems with System-centric and User-centric Factors
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences.
