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Found 16 papers, 4 papers with code
Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language
Temporal Activity Localization via Language (TALL) in video is a recently proposed challenging vision task, and tackling it requires fine-grained understanding of the video content, however, this is overlooked by most of the existing works.
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
This adaptation leads to convenient development of such LMMs with minimal modifications, however, it overlooks the intrinsic characteristics of diverse visual tasks and hinders the learning of perception capabilities.
LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs
To address this issue, we propose to apply an efficient Mixture of Experts (MoE) design, which is a sparse Mixture of LoRA Experts (MoLE) for instruction finetuning MLLMs.
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning
Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field.
Prompting Large Language Models to Reformulate Queries for Moment Localization
The task of moment localization is to localize a temporal moment in an untrimmed video for a given natural language query.
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection
Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques.
MT-Net Submission to the Waymo 3D Detection Leaderboard
In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard.
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart.
Self-supervised Learning for Semi-supervised Temporal Language Grounding
Given a text description, Temporal Language Grounding (TLG) aims to localize temporal boundaries of the segments that contain the specified semantics in an untrimmed video.
FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting
Blind face inpainting refers to the task of reconstructing visual contents without explicitly indicating the corrupted regions in a face image.
Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning
Dense Event Captioning (DEC) aims to jointly localize and describe multiple events of interest in untrimmed videos, which is an advancement of the conventional video captioning task (generating a single sentence description for a trimmed video).
Motion Guided Region Message Passing for Video Captioning
In this paper, we aim at designing a spatial information extraction and aggregation method for video captioning without the need of external object detectors.
Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos
Inspired by the fact that there exist cross-modal interactions in the human brain, we propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos and thus improve performances on both tasks.
Black-box Adversarial Attacks on Video Recognition Models
Using three benchmark video datasets, we demonstrate that V-BAD can craft both untargeted and targeted attacks to fool two state-of-the-art deep video recognition models.
Non-local NetVLAD Encoding for Video Classification
This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.
Aggregating Frame-level Features for Large-Scale Video Classification
This paper introduces the system we developed for the Google Cloud & YouTube-8M Video Understanding Challenge, which can be considered as a multi-label classification problem defined on top of the large scale YouTube-8M Dataset.
