Search Results for author:
Found 131 papers, 59 papers with code
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices.
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance.
Answer Complex Questions: Path Ranker Is All You Need
Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.
Detect Everything with Few Examples
DE-ViT establishes new state-of-the-art results on all benchmarks.
Ranked #1 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation.
Ranked #15 on
Semi-Supervised Video Object Segmentation
on VOT2020
Semi-Supervised Video Object Segmentation
Visual Object Tracking
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
Ranked #3 on
3D Object Detection
on Rope3D
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world.
FastPillars: A Deployment-friendly Pillar-based 3D Detector
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals
Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving.
Lenna: Language Enhanced Reasoning Detection Assistant
To evaluate the reasoning capability of Lenna, we construct a ReasonDet dataset to measure its performance on reasoning-based detection.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations.
Transforming the Latent Space of StyleGAN for Real Face Editing
But more importantly, the proposed $W$++ space achieves superior performance in both reconstruction quality and editing quality.
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline
ELUE is dedicated to depict the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
To our knowledge, for the very first time in lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the same as the FP32 model while enjoying $3\times$ inference speedup.
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection
Besides, by leveraging full training set and the additional 48K raw images of KITTI, it can further improve the MonoFlex by +4. 65% improvement on AP@0. 7 for car detection, reaching 18. 54% AP@0. 7, which ranks the 1st place among all monocular based methods on KITTI test leaderboard.
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image.
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance.
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines.
Lite-FPN for Keypoint-based Monocular 3D Object Detection
3D object detection with a single image is an essential and challenging task for autonomous driving.
HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution
In this paper, we introduce a new dataset, HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset) for building end-to-end generative information-seeking models that are capable of retrieving candidate quotes and generating attributed explanations.
Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification
In this way, the pre-training task and the T2I-ReID task are made consistent with each other on both data and training levels.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh their classification performance on WSIs.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
VRP-SAM: SAM with Visual Reference Prompt
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model.
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration
We discuss how Pyserini - a widely used toolkit for reproducible IR research can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking.
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection
3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control.
NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation
We measure LLM robustness using two metrics: (i) hallucination rate, measuring model tendency to hallucinate an answer, when the answer is not present in passages in the non-relevant subset, and (ii) error rate, measuring model inaccuracy to recognize relevant passages in the relevant subset.
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection.
Optical Flow boosts Unsupervised Localization and Segmentation
Our fine-tuning procedure outperforms state-of-the-art techniques for unsupervised semantic segmentation through linear probing, without the use of any labeled data.
EFLNet: Enhancing Feature Learning for Infrared Small Target Detection
To address this problem, we propose a new adaptive threshold focal loss (ATFL) function that decouples the target and the background, and utilizes the adaptive mechanism to adjust the loss weight to force the model to allocate more attention to target features.
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
Rethinking Label Smoothing on Multi-hop Question Answering
Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence prediction, and answer span extraction.
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
In this paper, we propose a novel active imitation learning framework based on a teacher-student interaction model, in which the teacher's goal is to identify the best teaching behavior and actively affect the student's learning process.
Neural Image Re-Exposure
In this work, we aim to re-expose the captured photo in post-processing to provide a more flexible way of addressing those issues within a unified framework.
Ranked #4 on
Deblurring
on GoPro
(using extra training data)
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
In this paper, we first conduct a comprehensive study on prior FL attacks and detection methods.
Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM
How can we construct an automated debate judge to evaluate an extensive, vibrant, multi-turn debate?
ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion
In this paper, we propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios.
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
To address this issue, we propose a novel graph convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly perform multi-information fusion for denoising purpose.
MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy Severity Prediction
The novelty of this project is to utilize the ordinal information among labels and add a new regression task, which can help the model learn more discriminative feature embedding for fine-grained classification tasks.
Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
A standard convex SPCA-based model with PSD constraint for unsupervised feature selection is proposed.
What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations
We propose a logistic Bradley-Terry probe which predicts word pair preferences of LLMs from the words' hidden vectors.
Peer attention enhances student learning
Human visual attention is susceptible to social influences.
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks.
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking
For example, on MS MARCO Passage v1, our method yields an average candidate set size of 27 out of 1, 000 which increases the reranking speed by about 37 times, while the MRR@10 is greater than a pre-specified value of 0. 38 with about 90% empirical coverage and the empirical baselines fail to provide such guarantee.
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving
With the increasing complexity of the traffic environment, the importance of safety perception in intelligent driving is growing.
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
In this paper, we propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments.
EduAgent: Generative Student Agents in Learning
Student simulation in online education is important to address dynamic learning behaviors of students with diverse backgrounds.
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Object detection methods are widely adopted for computer-aided diagnosis using medical images.
Peer-to-Peer Localization for Single-Antenna Devices
In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy.
Diverse Knowledge Distillation for End-to-End Person Search
Person search aims to localize and identify a specific person from a gallery of images.
A novel multimodal fusion network based on a joint coding model for lane line segmentation
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.
Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator
Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.
Early Exiting with Ensemble Internal Classifiers
In this paper, we show that a novel objective function for the training of the ensemble internal classifiers can be naturally induced from the perspective of ensemble learning and information theory.
IPS300+: a Challenging Multimodal Dataset for Intersection Perception System
Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task.
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.
Tuning Confidence Bound for Stochastic Bandits with Bandit Distance
We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others.
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
Bag-of-Words Baselines for Semantic Code Search
The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language.
A Little Bit Is Worse Than None: Ranking with Limited Training Data
Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search.
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models
However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs.
Multi-channel deep convolutional neural networks for multi-classifying thyroid disease
Therefore, this study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease.
Towards Best Practices for Training Multilingual Dense Retrieval Models
Dense retrieval models using a transformer-based bi-encoder design have emerged as an active area of research.
Generative Pre-Trained Transformers for Biologically Inspired Design
Biological systems in nature have evolved for millions of years to adapt and survive the environment.
Replacing Labeled Real-image Datasets with Auto-generated Contours
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs).
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games
First, there is inaccuracy when analysing the simplified payoff table.
BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network
Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way.
Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer
The second limitation comes from a common sense that these sensors can only pick up a narrow band (85-100Hz) of speech signals due to a sampling ceiling of 200Hz.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection.
Ambiguity Function Shaping based on Alternating Direction Riemannian Optimal Algorithm
In this paper, we proposed a novel method to design a waveform to synthesize the STAF based on suppressing the interference power.
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives.
A Hmong Corpus with Elaborate Expression Annotations
This paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas.
Better Than Whitespace: Information Retrieval for Languages without Custom Tokenizers
However, only a handful of the 7000+ languages on the planet benefit from specialized, custom-built tokenization algorithms, while the other languages are stuck with a "default" whitespace tokenizer, which cannot capture the intricacies of different languages.
RF-CHORD: Towards Deployable RFID Localization System for Logistics Network
RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network.
3DFill:Reference-guided Image Inpainting by Self-supervised 3D Image Alignment
We then fill the hole in the target image with the contents of the aligned image.
A classification performance evaluation measure considering data separability
Machine learning and deep learning classification models are data-driven, and the model and the data jointly determine their classification performance.
CAE v2: Context Autoencoder with CLIP Target
That is to say, the smaller the model, the lower the mask ratio needs to be.
Asymptotic Properties of the Synthetic Control Method
This paper provides new insights into the asymptotic properties of the synthetic control method (SCM).
Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language.
Modelling human logical reasoning process in dynamic environmental stress with cognitive agents
Overall, this work demonstrates a powerful, data-driven methodology to simulate and understand the vagaries of human logical reasoning process in dynamic contexts.
Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction
Recent progress in information retrieval finds that embedding query and document representation into multi-vector yields a robust bi-encoder retriever on out-of-distribution datasets.
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Our work significantly outperforms the state-of-the-art for three-finger robotic hands.
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one language with queries from another.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation.
Zero-Shot Listwise Document Reranking with a Large Language Model
Supervised ranking methods based on bi-encoder or cross-encoder architectures have shown success in multi-stage text ranking tasks, but they require large amounts of relevance judgments as training data.
Evaluating Embedding APIs for Information Retrieval
The ever-increasing size of language models curtails their widespread availability to the community, thereby galvanizing many companies into offering access to large language models through APIs.
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment.
Optimal Weighted Random Forests
The random forest (RF) algorithm has become a very popular prediction method for its great flexibility and promising accuracy.
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity
Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain.
Towards Optimal Neural Networks: the Role of Sample Splitting in Hyperparameter Selection
When artificial neural networks have demonstrated exceptional practical success in a variety of domains, investigations into their theoretical characteristics, such as their approximation power, statistical properties, and generalization performance, have concurrently made significant strides.
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
The language models, especially the basic text classification models, have been shown to be susceptible to textual adversarial attacks such as synonym substitution and word insertion attacks.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction.
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection
Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e. g., entity mismatch) of irrelevant references.
Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs
In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases, aiming to mitigate the problem of lacking domain-specific knowledge.
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain.
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization
In this work, we revisit this key challenge through the lens of gradient conflicts on the server side.
Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing
To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP).
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network
Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized.
Sequential Texts Driven Cohesive Motions Synthesis with Natural Transitions
At the technical level, we explore the local-to-global semantic features of previous and current texts to extract relevant information.
BEVHeight++: Toward Robust Visual Centric 3D Object Detection
In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods.
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
ProGO: Probabilistic Global Optimizer
To address these challenges, we develop a sequence of multidimensional integration-based methods that we show to converge to the global optima under some mild regularity conditions.
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer
However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy.
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS
The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process.
Leveraging generative artificial intelligence to simulate student learning behavior
Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy.
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer components, and wireless domain constraints.
Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement
In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities.
Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing
We propose DiffSBR, a differentiable framework for mmWave-based 3D reconstruction.
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning.
Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models
However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility.
Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics, but also manually generate low-resolution pairs of images that do not have sufficient confidence.
From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research
Thyroid cancer, the most prevalent endocrine cancer, has gained significant global attention due to its impact on public health.
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely.
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy.
LDSF: Lightweight Dual-Stream Framework for SAR Target Recognition by Coupling Local Electromagnetic Scattering Features and Global Visual Features
We extract the EM scattering (EMS) information from the complex SAR data and integrate the physical properties of the target into the network through a dual-stream framework to guide the network to learn physically meaningful and discriminative features.
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model
To this end, we explore the concept of test-time prompt tuning (TTPT), which enables the adaptation of the CLIP model to novel downstream tasks through only one step of optimization on an unsupervised objective that involves the test sample.
Stimulate the Potential of Robots via Competition
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents.
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models.
Worst-Case Riemannian Optimization with Uncertain Target Steering Vector for Slow-Time Transmit Sequence of Cognitive Radar
In order to solve this problem, we propose a new optimization method for slow-time transmit sequence design.
