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Found 33 papers, 12 papers with code
Towards Understanding How Knowledge Evolves in Large Vision-Language Models
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications.
SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
To utilize these slice features, we propose SliceOcc, an RGB camera-based model specifically tailored for indoor 3D semantic occupancy prediction.
EventGPT: Event Stream Understanding with Multimodal Large Language Models
Our EventGPT comprises an event encoder, followed by a spatio-temporal aggregator, a linear projector, an event-language adapter, and an LLM.
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
To address these challenges, we propose HDI-Former, a Hybrid Dynamic Interaction ANN-SNN Transformer, marking the first trial to design a directly trained hybrid ANN-SNN architecture for high-accuracy and energy-efficient object detection using frames and events.
Event Stream based Sign Language Translation: A High-Definition Benchmark Dataset and A New Algorithm
Based on this dataset and several other large-scale datasets, we propose a novel baseline method that fully leverages the Mamba model's ability to integrate temporal information of CNN features, resulting in improved sign language translation outcomes.
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information
Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks.
3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
Human pose estimation remains a multifaceted challenge in computer vision, pivotal across diverse domains such as behavior recognition, human-computer interaction, and pedestrian tracking.
Dissecting Multiplication in Transformers: Insights into LLMs
This work underscores the importance of explainable AI, helping to build trust in large language models and promoting their adoption in critical applications.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
XPose: eXplainable Human Pose Estimation
In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation.
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis
To overcome this limitation in MLLMs, we introduce Proximity Question Answering (Proximity QA), a novel framework designed to enable MLLMs to infer the proximity relationship between objects in images.
Residual Alignment: Uncovering the Mechanisms of Residual Networks
Our measurements reveal a process called Residual Alignment (RA) characterized by four properties: (RA1) intermediate representations of a given input are equispaced on a line, embedded in high dimensional space, as observed by Gai and Zhang [2021]; (RA2) top left and right singular vectors of Residual Jacobians align with each other and across different depths; (RA3) Residual Jacobians are at most rank C for fully-connected ResNets, where C is the number of classes; and (RA4) top singular values of Residual Jacobians scale inversely with depth.
SODFormer: Streaming Object Detection with Transformer Using Events and Frames
Then, we design a spatiotemporal Transformer architecture to detect objects via an end-to-end sequence prediction problem, where the novel temporal Transformer module leverages rich temporal cues from two visual streams to improve the detection performance.
Deep Directly-Trained Spiking Neural Networks for Object Detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.
Full Resolution Repetition Counting
Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions.
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e. g., motion blur and low light) in monocular depth estimation.
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
In this paper, we propose a unified framework named BEV-LGKD to transfer the knowledge in the teacher-student manner.
Uncertainty Guided Depth Fusion for Spike Camera
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in autonomous driving by mitigating the challenges posed by high-velocity motion blur.
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
Computational Efficiency
Event-Based Video Reconstruction
+2
1000x Faster Camera and Machine Vision with Ordinary Devices
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
Ranked #1 on
Deblurring
on .
Concerted Rolling and Membrane Penetration Revealed by Atomistic Simulations of Antimicrobial Peptides
Short peptides with antimicrobial activity have therapeutic potential for treating bacterial infections.
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.
Ranked #1 on
Object Tracking
on VisEvent
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
To this end, we propose Differentiable AIS (DAIS), a variant of AIS which ensures differentiability by abandoning the Metropolis-Hastings corrections.
NeuSpike-Net: High Speed Video Reconstruction via Bio-Inspired Neuromorphic Cameras
In this paper, we propose a NeuSpike-Net to learn both the high dynamic range and high motion sensitivity of DVS and the full texture sampling of spike camera to achieve high-speed and high dynamic image reconstruction.
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification
This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification.
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution.
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.
Global-Local Temporal Representations For Video Person Re-Identification
The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences.
Multi-scale 3D Convolution Network for Video Based Person Re-Identification
A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network.
LVreID: Person Re-Identification with Long Sequence Videos
This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID).
Pose-driven Deep Convolutional Model for Person Re-identification
Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
Ranked #107 on
Person Re-Identification
on Market-1501
