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Found 14 papers, 9 papers with code
StreamChat: Chatting with Streaming Video
This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content.
PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
This work represents a significant step towards a truly unified MLLM capable of adapting to the granularity demands of various visual tasks.
FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis
In this study, we delve into the generation of high-resolution images from pre-trained diffusion models, addressing persistent challenges, such as repetitive patterns and structural distortions, that emerge when models are applied beyond their trained resolutions.
InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation
In this work, we introduce InstructSeq, an instruction-conditioned multi-modal modeling framework that unifies diverse vision tasks through flexible natural language control and handling of both visual and textual data.
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
To alleviate this, previous methods simply replace the pixel reconstruction targets of 75% masked tokens by encoded features from pre-trained image-image (DINO) or image-language (CLIP) contrastive learning.
FeatAug-DETR: Enriching One-to-Many Matching for DETRs with Feature Augmentation
The first method is One-to-many Matching via Data Augmentation (denoted as DataAug-DETR).
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
On top of that, the performance of Tip-Adapter can be further boosted to be state-of-the-art on ImageNet by fine-tuning the cache model for 10$\times$ fewer epochs than existing methods, which is both effective and efficient.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
RBGNet: Ray-based Grouping for 3D Object Detection
In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers.
Ranked #13 on
3D Object Detection
on SUN-RGBD val
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
To further enhance CLIP's few-shot capability, CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and significantly improves the performance for few-shot classification.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.
Learning Longterm Representations for Person Re-Identification Using Radio Signals
RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape.
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable.
Adversarial Attack and Defense on Point Sets
A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment.
