Search Results for author:
Found 15 papers, 11 papers with code
Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization
In this paper, we explore the idea that CV adopts discrete and terminological task definitions (\eg, ``image segmentation''), which may be a key barrier to zero-shot task generalization.
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Using CoMCTS, we construct Mulberry-260k, a multimodal dataset with a tree of rich, explicit and well-defined reasoning nodes for each question.
DistinctAD: Distinctive Audio Description Generation in Contexts
Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment.
Dense Connector for MLLMs
We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs.
Automated Multi-level Preference for MLLMs
To verify our viewpoint, we present the Automated Multi-level Preference (AMP) framework for MLLMs.
Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey
To aid researchers in understanding the application of traffic analysis tools for studying CIoT security and privacy risks, this survey reviews 303 publications on traffic analysis within the CIoT security and privacy domain from January 2018 to June 2024, focusing on three research questions.
Multi-level graph learning for audio event classification and human-perceived annoyance rating prediction
Specifically, this paper proposes a lightweight multi-level graph learning (MLGL) based on local and global semantic graphs to simultaneously perform audio event classification (AEC) and human annoyance rating prediction (ARP).
GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?
Our study centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks: Firstly, we explore the potential of its generated rich textual descriptions across various categories to enhance recognition performance without any training.
What Can Simple Arithmetic Operations Do for Temporal Modeling?
We conduct comprehensive ablation studies on the instantiation of ATMs and demonstrate that this module provides powerful temporal modeling capability at a low computational cost.
Ranked #4 on
Action Recognition
on Something-Something V1
UATVR: Uncertainty-Adaptive Text-Video Retrieval
In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation.
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification
Experiments on a polyphonic acoustic scene dataset show that the proposed ERGL achieves competitive performance on ASC by using only a limited number of embeddings of audio events without any data augmentations.
Acoustic Scene Classification
Graph Representation Learning
+1
It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training
In order to guide the encoder to fully excavate spatial-temporal features, two separate decoders are used for two pretext tasks of disentangled appearance and motion prediction.
DALG: Deep Attentive Local and Global Modeling for Image Retrieval
Deeply learned representations have achieved superior image retrieval performance in a retrieve-then-rerank manner.
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR.
