Search Results for author:
Found 43 papers, 26 papers with code
Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
Large language models (LLMs) demonstrate remarkable text comprehension and generation capabilities but often lack the ability to utilize up-to-date or domain-specific knowledge not included in their training data.
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem.
A computational transition for detecting correlated stochastic block models by low-degree polynomials
For the detection problem of distinguishing this model from a pair of independent Erd\H{o}s-R\'enyi graphs with the same edge density $\mathcal{G}(n,\tfrac{\lambda s}{n})$, we focus on tests based on \emph{low-degree polynomials} of the entries of the adjacency matrices, and we determine the threshold that separates the easy and hard regimes.
Goldfish: Vision-Language Understanding of Arbitrarily Long Videos
This design of the retrieval mechanism enables the Goldfish to efficiently process arbitrarily long video sequences, facilitating its application in contexts such as movies or television series.
InfiniBench: A Comprehensive Benchmark for Large Multimodal Models in Very Long Video Understanding
To address this gap, we introduce InfiniBench a comprehensive benchmark for very long video understanding which presents 1)The longest video duration, averaging 52. 59 minutes per video 2) The largest number of question-answer pairs, 108. 2K 3) Diversity in questions that examine nine different skills and include both multiple-choice questions and open-ended questions 4) Human-centric, as the video sources come from movies and daily TV shows, with specific human-level question designs such as Movie Spoiler Questions that require critical thinking and comprehensive understanding.
VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images.
iMotion-LLM: Motion Prediction Instruction Tuning
We introduce iMotion-LLM: a Multimodal Large Language Models (LLMs) with trajectory prediction, tailored to guide interactive multi-agent scenarios.
Kestrel: Point Grounding Multimodal LLM for Part-Aware 3D Vision-Language Understanding
Therefore, we propose two novel tasks: (1) Part-Aware Point Grounding, the model is tasked with directly predicting a part-level segmentation mask based on user instructions, and (2) Part-Aware Point Grounded Captioning, the model provides a detailed caption that includes part-level descriptions and their corresponding masks.
Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue Generation
Integrating multimodal knowledge into large language models (LLMs) represents a significant advancement in dialogue generation capabilities.
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world.
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding.
Ranked #3 on
Zero-Shot Video Question Answer
on TVQA
Uni3DL: Unified Model for 3D and Language Understanding
In this work, we present Uni3DL, a unified model for 3D and Language understanding.
Efficiently matching random inhomogeneous graphs via degree profiles
In this paper, we study the problem of recovering the latent vertex correspondence between two correlated random graphs with vastly inhomogeneous and unknown edge probabilities between different pairs of vertices.
On the Robustness of Object Detection Models on Aerial Images
The benchmarks we propose and our comprehensive experimental analyses can facilitate research on robust object detection on aerial images.
Towards Generic and Controllable Attacks Against Object Detection
To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations.
A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation
We propose an efficient algorithm for matching two correlated Erd\H{o}s--R\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence.
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation
This work studies semantic segmentation under the domain generalization setting, where a model is trained only on the source domain and tested on the unseen target domain.
FreePoint: Unsupervised Point Cloud Instance Segmentation
Based on the point features, we perform a bottom-up multicut algorithm to segment point clouds into coarse instance masks as pseudo labels, which are used to train a point cloud instance segmentation model.
Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues.
Ranked #4 on
Oriented Object Detection
on DOTA 2.0
Few-Shot Object Detection via Variational Feature Aggregation
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples.
Detecting Building Changes with Off-Nadir Aerial Images
The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades.
Building change detection for remote sensing images
Change Detection
A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation
Motivated by the problem of matching vertices in two correlated Erd\H{o}s-R\'enyi graphs, we study the problem of matching two correlated Gaussian Wigner matrices.
Matching recovery threshold for correlated random graphs
For two correlated graphs which are independently sub-sampled from a common Erd\H{o}s-R\'enyi graph $\mathbf{G}(n, p)$, we wish to recover their \emph{latent} vertex matching from the observation of these two graphs \emph{without labels}.
Detection threshold for correlated Erdős-Rényi graphs via densest subgraphs
The problem of detecting edge correlation between two Erd\H{o}s-R\'enyi random graphs on $n$ unlabeled nodes can be formulated as a hypothesis testing problem: under the null hypothesis, the two graphs are sampled independently; under the alternative, the two graphs are independently sub-sampled from a parent graph which is Erd\H{o}s-R\'enyi $\mathbf{G}(n, p)$ (so that their marginal distributions are the same as the null).
Expanding Low-Density Latent Regions for Open-Set Object Detection
Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability.
Decoupling Zero-Shot Semantic Segmentation
2) a zero-shot classification task on segments.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
The planted matching problem: Sharp threshold and infinite-order phase transition
Conversely, if $\sqrt{d} B(\mathcal{P},\mathcal{Q}) \ge 1+\epsilon$ for an arbitrarily small constant $\epsilon>0$, the reconstruction error for any estimator is shown to be bounded away from $0$ under both the sparse and dense model, resolving the conjecture in [Moharrami et al. 2019, Semerjian et al. 2020].
ReDet: A Rotation-equivariant Detector for Aerial Object Detection
More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size.
Ranked #21 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
DetCo: Unsupervised Contrastive Learning for Object Detection
Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.
Align Deep Features for Oriented Object Detection
However most of existing methods rely on heuristically defined anchors with different scales, angles and aspect ratios and usually suffer from severe misalignment between anchor boxes and axis-aligned convolutional features, which leads to the common inconsistency between the classification score and localization accuracy.
Ranked #26 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Modeling the Control of COVID-19: Impact of Policy Interventions and Meteorological Factors
Home quarantine is the most important one to prevent the spread of COVID-19.
Consistent recovery threshold of hidden nearest neighbor graphs
Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$-nearest neighbor (NN) graph in an $n$-vertex complete graph, whose edge weights are independent and distributed according to $P_n$ for edges in the hidden $2k$-NN graph and $Q_n$ otherwise.
Learning RoI Transformer for Oriented Object Detection in Aerial Images
Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects.
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects.
Ranked #54 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Efficient random graph matching via degree profiles
This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges.
Hidden Hamiltonian Cycle Recovery via Linear Programming
Represented as bicolored multi-graphs, these extreme points are further decomposed into simpler "blossom-type" structures for the large deviation analysis and counting arguments.
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)
Ranked #58 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Online Learning with Composite Loss Functions
This class includes problems where the algorithm's loss is the minimum over the recent adversarial values, the maximum over the recent values, or a linear combination of the recent values.
Bandits with Switching Costs: T^{2/3} Regret
We prove that the player's $T$-round minimax regret in this setting is $\widetilde{\Theta}(T^{2/3})$, thereby closing a fundamental gap in our understanding of learning with bandit feedback.
