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Found 16 papers, 1 papers with code
Delving into the Trajectory Long-tail Distribution for Muti-object Tracking
In this study, we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets.
Small Language Model Meets with Reinforced Vision Vocabulary
In Vary-toy, we introduce an improved vision vocabulary, allowing the model to not only possess all features of Vary but also gather more generality.
Ranked #76 on
Visual Question Answering
on MM-Vet
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy.
Merlin:Empowering Multimodal LLMs with Foresight Minds
Then, FIT requires MLLMs to first predict trajectories of related objects and then reason about potential future events based on them.
Ranked #61 on
Visual Question Answering
on MM-Vet
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience.
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Besides, GroupLane with ResNet18 still surpasses PersFormer by 4. 9% F1 score, while the inference speed is nearly 7x faster and the FLOPs is only 13. 3% of it.
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisfactory convergence dynamics.
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes.
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
The point cloud based 3D single object tracking has drawn increasing attention.
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Recent advanced works generally employ a series of object attributes, e. g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT.
Delving into the Pre-training Paradigm of Monocular 3D Object Detection
(2) Combining depth estimation and 2D object detection is a promising M3OD pre-training baseline.
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking
To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector.
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation
Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID).
MAT: Motion-Aware Multi-Object Tracking
Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections.
Refinements in Motion and Appearance for Online Multi-Object Tracking
Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association.
Fusion-supervised Deep Cross-modal Hashing
Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages.
