Search Results for author:
Found 58 papers, 22 papers with code
Deep Hashing with Active Pairwise Supervision
n this paper, we propose a Deep Hashing method with Active Pairwise Supervision(DH-APS).
Q-VLM: Post-training Quantization for Large Vision-Language Models
On the contrary, we mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy searching with low search cost.
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning
However, these studies primarily focus on perturbing accuracy, leaving a critical question unexplored: Can an attacker bypass the group fairness mechanisms in FL and manipulate the global model to be biased?
EmbodiedSAM: Online Segment Any 3D Thing in Real Time
In this paper, we aim to leverage Segment Anything Model (SAM) for real-time 3D instance segmentation in an online setting.
Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods
This work addresses the certification of the local robustness of vision-based two-stage 6D object pose estimation.
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
This effect is especially visible for open questions and questions of high difficulty or novelty.
Embodied Instruction Following in Unknown Environments
For the task planner, we generate the feasible step-by-step plans for human goal accomplishment according to the task completion process and the known visual clues.
ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
To model this process, we design a consistency distillation technique to predict the action sample directly instead of predicting the noise within the vision community for fast convergence in the low-dimensional action manifold.
ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
Performing language-conditioned robotic manipulation tasks in unstructured environments is highly demanded for general intelligent robots.
Memory-based Adapters for Online 3D Scene Perception
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
Online Training of Large Language Models: Learn while chatting
Large Language Models(LLMs) have dramatically revolutionized the field of Natural Language Processing(NLP), offering remarkable capabilities that have garnered widespread usage.
LowRankOcc: Tensor Decomposition and Low-Rank Recovery for Vision-based 3D Semantic Occupancy Prediction
In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction.
ThinkBot: Embodied Instruction Following with Thought Chain Reasoning
Embodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments.
Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions.
Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting.
MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow
Previous work only focused on data attribution from the training data perspective, which is unsuitable for tracing and quantifying copyright infringement in practice because of the following reasons: (1) the training datasets are not always available in public; (2) the model provider is the responsible party, not the image.
Anyview: Generalizable Indoor 3D Object Detection with Variable Frames
Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images.
Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
Regulatory compliance auditing across diverse industrial domains requires heightened quality assurance and traceability.
Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition
In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy.
An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras
We also demonstrate the integration of image convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an application of our architecture.
Self-Enforced Job Matching
The classic two-sided many-to-one job matching model assumes that firms treat workers as substitutes and workers ignore colleagues when choosing where to work.
Asynchronous Blob Tracker for Event Cameras
Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range.
Embodied Task Planning with Large Language Models
Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments.
Towards Accurate Post-training Quantization for Diffusion Models
On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead.
3D Small Object Detection with Dynamic Spatial Pruning
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.
Partner Choice and Morality: Preference Evolution under Stable Matching
We present a model that investigates preference evolution with endogenous matching.
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression
On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation.
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
Category-level Shape Estimation for Densely Cluttered Objects
Then we fuse the feature maps representing the visual information of multi-view RGB images and the pixel affinity learned from the clutter point cloud, where the acquired instance segmentation masks of multi-view RGB images are projected to partition the clutter point cloud.
Planning Irregular Object Packing via Hierarchical Reinforcement Learning
We compare our approach with existing robotic packing methods for irregular objects in a physics simulator.
Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections.
Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds
Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving.
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee
After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee.
Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value
Explaining deep convolutional neural networks has been recently drawing increasing attention since it helps to understand the networks' internal operations and why they make certain decisions.
Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data
However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search
Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost.
A Linear Comb Filter for Event Flicker Removal
Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor.
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data
Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages.
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception
We provide a SHEF dataset targeted at evaluating disparity estimation algorithms and introduce a stereo disparity estimation algorithm that uses edge information extracted from the event stream correlated with the edge detected in the frame data.
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
On the contrary, our GMPQ searches the mixed-quantization policy that can be generalized to largescale datasets with only a small amount of data, so that the search cost is significantly reduced without performance degradation.
Instance Similarity Learning for Unsupervised Feature Representation
In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation.
Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN
Therefore, this paper considers optimising the computational resource consumption by reducing the size and number of kernels in convolutional layers.
Enhanced Modality Transition for Image Captioning
In this work, we explicitly build a Modality Transition Module (MTM) to transfer visual features into semantic representations before forwarding them to the language model.
Reanalyses and a high-resolution model fail to capture the `high tail' of CAPE distributions
Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail ($>$ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer.
Atmospheric and Oceanic Physics
Event Camera Calibration of Per-pixel Biased Contrast Threshold
Currently, most of the existing works use a single contrast threshold to estimate the intensity change of all pixels.
An Asynchronous Kalman Filter for Hybrid Event Cameras
Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes.
ORD: Object Relationship Discovery for Visual Dialogue Generation
With the rapid advancement of image captioning and visual question answering at single-round level, the question of how to generate multi-round dialogue about visual content has not yet been well explored. Existing visual dialogue methods encode the image into a fixed feature vector directly, concatenated with the question and history embeddings to predict the response. Some recent methods tackle the co-reference resolution problem using co-attention mechanism to cross-refer relevant elements from the image, history, and the target question. However, it remains challenging to reason visual relationships, since the fine-grained object-level information is omitted before co-attentive reasoning.
BiDet: An Efficient Binarized Object Detector
Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly.
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data.
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way.
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks
In this paper, we propose a channel-wise interaction based binary convolutional neural network learning method (CI-BCNN) for efficient inference.
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-scale product databases to find their desired items.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.
Look Deeper See Richer: Depth-aware Image Paragraph Captioning
Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming.
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning
Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, reducing the uncertainty of binary codes by maximizing the mutual information with inputs and related bits, so that the ambiguous bits receive additional instruction from the graph for confident binarization.
Learning Deep Binary Descriptor With Multi-Quantization
In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching.
