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Found 27 papers, 12 papers with code
Object-Shot Enhanced Grounding Network for Egocentric Video
To address these limitations, we propose OSGNet, an Object-Shot enhanced Grounding Network for egocentric video.
Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin
Adapting vision-language models (VLMs) to downstream tasks with pseudolabels has gained increasing attention.
BMRL: Bi-Modal Guided Multi-Perspective Representation Learning for Zero-Shot Deepfake Attribution
Besides, they tend to fail to assess the generalization performance of deepfake attributors to unseen generators in a fine-grained manner.
Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation
Recent works on dataset distillation demonstrate that combining distilled and real data can mitigate the effectiveness decay.
Embodied Crowd Counting
However, most existing methods are designed for indoor navigation, showing unknown performance in analyzing complex object distribution in large scale scenes, such as crowds.
MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-Resolution
Pioneering text-to-image (T2I) diffusion models have ushered in a new era of real-world image super-resolution (Real-ISR), significantly enhancing the visual perception of reconstructed images.
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
To explore the real limit of PTQ, we propose an extremely low-bit PTQ method called PTQ1. 61, which enables weight quantization to 1. 61-bit for the first time.
FALCON: Resolving Visual Redundancy and Fragmentation in High-resolution Multimodal Large Language Models via Visual Registers
To directly address the visual redundancy present in the output of vision encoder, we propose a Register-based Representation Compacting (ReCompact) mechanism.
Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification
Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task.
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes.
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image Editing
Despite their advances, existing methods still encounter three key issues: 1) limited capacity of the text prompt in guiding target image generation, 2) insufficient mining of word-to-patch and patch-to-patch relationships for grounding editing areas, and 3) unified editing strength for all regions during each denoising step.
Ranked #3 on
Text-based Image Editing
on PIE-Bench
Token-level Correlation-guided Compression for Efficient Multimodal Document Understanding
Secondly, we present a token-level sampling method that efficiently captures the most informative tokens by delving into the correlation between the [CLS] token and patch tokens.
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
In this paper, we propose a mixture of multimodal experts (MoME) to mitigate task interference and obtain a generalist MLLM.
MMGRec: Multimodal Generative Recommendation with Transformer Model
Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information.
UniAV: Unified Audio-Visual Perception for Multi-Task Video Event Localization
Video localization tasks aim to temporally locate specific instances in videos, including temporal action localization (TAL), sound event detection (SED) and audio-visual event localization (AVEL).
Prompt-based Multi-interest Learning Method for Sequential Recommendation
Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions.
Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog
In response to these challenges, we present an iterative search and reasoning framework, which consists of a textual encoder, a visual encoder, and a generator.
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Pre-trained vision-language models, e. g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning.
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation
The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image.
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks.
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification
Moreover, human semantic information and pedestrian identity information are not fully explored.
A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification
In addition, a visual clothes shielding module (VCS) is also designed to extract a more robust feature representation for the cloth-changing task by covering the clothing regions and focusing the model on the visual semantic information unrelated to the clothes.
Cloth-Changing Person Re-Identification
Semantic Segmentation
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval
Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images.
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification
To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes.
TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization
In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework.
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding
Distantly-supervised relation extraction has proven to be effective to find relational facts from texts.
