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09/07/2023
ImageBind-LLM: Multi-modality Instruction Tuning
We present ImageBind-LLM, a multi-modality instruction tuning method of ...
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09/01/2023
Point-Bind Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds...
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08/15/2023
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
With the continuous increase of users and items, conventional recommende...
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07/24/2023
Revisiting Event-based Video Frame Interpolation
Dynamic vision sensors or event cameras provide rich complementary infor...
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07/03/2023
JourneyDB: A Benchmark for Generative Image Understanding
While recent advancements in vision-language models have revolutionized ...
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06/15/2023
Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models
Video Question Answering (VideoQA) has been significantly advanced from ...
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05/25/2023
Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
Recently, video object segmentation (VOS) referred by multi-modal signal...
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05/04/2023
Personalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been...
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04/28/2023
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
How to efficiently transform large language models (LLMs) into instructi...
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04/03/2023
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has pro...
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03/29/2023
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
Understanding 3D scenes from multi-view inputs has been proven to allevi...
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03/28/2023
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
We present LLaMA-Adapter, a lightweight adaption method to efficiently f...
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03/14/2023
Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
We present a Non-parametric Network for 3D point cloud analysis, Point-N...
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03/14/2023
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
Masked Autoencoders learn strong visual representations and achieve stat...
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03/09/2023
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
Masked Autoencoders (MAE) have been popular paradigms for large-scale vi...
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03/03/2023
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Visual recognition in low-data regimes requires deep neural networks to ...
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03/01/2023
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis
Performances on standard 3D point cloud benchmarks have plateaued, resul...
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12/28/2022
TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning
To achieve accurate and low-cost 3D object detection, existing methods p...
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12/13/2022
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Pre-training by numerous image data has become de-facto for robust 2D re...
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12/08/2022
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Continual Test-Time Adaptation (CTTA) aims to adapt the source model to ...
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12/07/2022
iQuery: Instruments as Queries for Audio-Visual Sound Separation
Current audio-visual separation methods share a standard architecture de...
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11/21/2022
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning
Contrastive Language-Image Pre-training (CLIP) has shown promising open-...
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09/29/2022
EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual and Language Learning
3D visual grounding aims to find the objects within point clouds mention...
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09/28/2022
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Contrastive Language-Image Pre-training (CLIP) has been shown to learn v...
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09/25/2022
Collaboration of Pre-trained Models Makes Better Few-shot Learner
Few-shot classification requires deep neural networks to learn generaliz...
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08/06/2022
Frozen CLIP Models are Efficient Video Learners
Video recognition has been dominated by the end-to-end learning paradigm...
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08/05/2022
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Image restoration algorithms such as super resolution (SR) are indispens...
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07/19/2022
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
Contrastive Vision-Language Pre-training, known as CLIP, has provided a ...
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07/03/2022
Can Language Understand Depth?
Besides image classification, Contrastive Language-Image Pre-training (C...
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05/28/2022
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
Masked Autoencoders (MAE) have shown great potentials in self-supervised...
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04/03/2022
POS-BERT: Point Cloud One-Stage BERT Pre-Training
Recently, the pre-training paradigm combining Transformer and masked lan...
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03/24/2022
MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection
Monocular 3D object detection has long been a challenging task in autono...
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02/09/2022
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning
In this paper, we propose a simple and general framework for self-superv...
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12/04/2021
PointCLIP: Point Cloud Understanding by CLIP
Recently, zero-shot and few-shot learning via Contrastive Vision-Languag...
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12/04/2021
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Vision-Language Pre-training (CLIP) has drown increasing att...
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11/19/2021
DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion
Point cloud processing is a challenging task due to its sparsity and irr...
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11/06/2021
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
Contrastive Vision-Language Pre-training, known as CLIP, has provided a ...
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05/31/2021