Despite significant recent progress in the field of autonomous driving modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand large language models (LLM) have shown impressive reasoning capabilities that approach "Artificial General Intelligence". On the other hand previous autonomous driving methods tend to rely on limited-format inputs (e.g. sensor data and navigation waypoints) restricting the vehicle's ability to understand language information and interact with humans. To this end this paper introduces LMDrive a novel language-guided end-to-end closed-loop autonomous driving framework. LMDrive uniquely processes and integrates multi-modal sensor data with natural language instructions enabling interaction with humans and navigation software in realistic instructional settings. To facilitate further research in language-based closed-loop autonomous driving we also publicly release the corresponding dataset which includes approximately 64K instruction-following data clips and the LangAuto benchmark that tests the system's ability to handle complex instructions and challenging driving scenarios. Extensive closed-loop experiments are conducted to demonstrate LMDrive's effectiveness. To the best of our knowledge we're the very first work to leverage LLMs for closed-loop end-to-end autonomous driving. Code is available at https://github.com/opendilab/LMDrive
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Annotating datasets for object detection is an expensive and time-consuming endeavor. To minimize this burden active learning (AL) techniques are employed to select the most informative samples for annotation within a constrained "annotation budget". Traditional AL strategies typically rely on model uncertainty or sample diversity for query sampling while more advanced methods have focused on developing AL-specific object detector architectures to enhance performance. However these specialized approaches are not readily adaptable to different object detectors due to the significant engineering effort required for integration. To overcome this challenge we introduce Plug and Play Active Learning (PPAL) a simple and effective AL strategy for object detection. PPAL is a two-stage method comprising uncertainty-based and diversity-based sampling phases. In the first stage our Difficulty Calibrated Uncertainty Sampling leverage a category-wise difficulty coefficient that combines both classification and localisation difficulties to re-weight instance uncertainties from which we sample a candidate pool for the subsequent diversity-based sampling. In the second stage we propose Category Conditioned Matching Similarity to better compute the similarities of multi-instance images as ensembles of their instance similarities which is used by the k-Means++ algorithm to sample the final AL queries. PPAL makes no change to model architectures or detector training pipelines; hence it can be easily generalized to different object detectors. We benchmark PPAL on the MS-COCO and Pascal VOC datasets using different detector architectures and show that our method outperforms prior work by a large margin. Code is available at https://github.com/ChenhongyiYang/PPAL
We introduce the new setting of open-vocabulary object 6D pose estimation in which a textual prompt is used to specify the object of interest. In contrast to existing approaches in our setting (i) the object of interest is specified solely through the textual prompt (ii) no object model (e.g. CAD or video sequence) is required at inference and (iii) the object is imaged from two RGBD viewpoints of different scenes. To operate in this setting we introduce a novel approach that leverages a Vision-Language Model to segment the object of interest from the scenes and to estimate its relative 6D pose. The key of our approach is a carefully devised strategy to fuse object-level information provided by the prompt with local image features resulting in a feature space that can generalize to novel concepts. We validate our approach on a new benchmark based on two popular datasets REAL275 and Toyota-Light which collectively encompass 34 object instances appearing in four thousand image pairs. The results demonstrate that our approach outperforms both a well-established hand-crafted method and a recent deep learning-based baseline in estimating the relative 6D pose of objects in different scenes. Code and dataset are available at https://jcorsetti.github.io/oryon.
We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured room impulse response data paired with multi-view images and precise 6DoF pose tracking data for sound emitters and listeners in the rooms. We used this dataset to evaluate existing methods for novel-view acoustic synthesis and impulse response generation which previously relied on synthetic data. In our evaluation we thoroughly assessed existing audio and audio-visual models against multiple criteria and proposed settings to enhance their performance on real-world data. We also conducted experiments to investigate the impact of incorporating visual data (i.e. images and depth) into neural acoustic field models. Additionally we demonstrated the effectiveness of a simple sim2real approach where a model is pre-trained with simulated data and fine-tuned with sparse real-world data resulting in significant improvements in the few-shot learning approach. RAF is the first dataset to provide densely captured room acoustic data making it an ideal resource for researchers working on audio and audio-visual neural acoustic field modeling techniques.
We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory exemplified by agents on smart glasses or by actively exploring the environment as in the case of mobile robots. We accompany our formulation with OpenEQA -- the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol we evaluate several state-of-the-art foundation models including GPT-4V and find that they significantly lag behind human-level performance. Consequently OpenEQA stands out as a straightforward measurable and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI conversational agents and world models.
Surveillance videos are important for public security. However current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding although they have obtained considerable performance. To address this issue we propose a new research direction of surveillance video-and-language understanding(VALU) and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset UCA (UCF-Crime Annotation) contains 23542 sentences with an average length of 20 words and its annotated videos are as long as 110.7 hours. Furthermore we benchmark SOTA models for four multimodal tasks on this newly created dataset which serve as new baselines for surveillance VALU. Through experiments we find that mainstream models used in previously public datasets perform poorly on surveillance video demonstrating new challenges in surveillance VALU. We also conducted experiments on multimodal anomaly detection. These results demonstrate that our multimodal surveillance learning can improve the performance of anomaly detection. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI.
We propose a structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step which is difficult for texture-poor scenes and poor keypoint detection may break down the whole SfM system. We propose a detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints while solving the multi-view inconsistency issue of detector-free matchers. Specifically our framework first reconstructs a coarse SfM model from quantized detector-free matches. Then it refines the model by a novel iterative refinement pipeline which iterates between an attention-based multi-view matching module to refine feature tracks and a geometry refinement module to improve the reconstruction accuracy. Experiments demonstrate that the proposed framework outperforms existing detector-based SfM systems on common benchmark datasets. We also collect a texture-poor SfM dataset to demonstrate the capability of our framework to reconstruct texture-poor scenes. Based on this framework we take first place in Image Matching Challenge 2023.
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
PDF ↗In this paper we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples. Existing methods though somewhat effective encounter several limitations which we alleviate through two significant improvements. First we introduce a lightweight parameter-efficient adaptation strategy to address overfitting associated with fine-tuning a large number of parameters on small datasets. This strategy employs a linear transformation of pre-trained features significantly reducing the trainable parameter count. Second we replace the traditional nearest centroid classifier with a discriminative sample-aware loss function enhancing the model's sensitivity to the inter- and intra-class variances within the training set for improved clustering in feature space. Empirical evaluations on the Meta-Dataset benchmark showcase that our approach not only improves accuracy up to 7.7% and 5.3% on previously seen and unseen datasets respectively but also achieves the above performance while being at least 3x more parameter-efficient than existing methods establishing a new state-of-the-art in cross-domain few-shot learning. Our code is available at https://github.com/rashindrie/DIPA.
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks Methods and Applications
PDF ↗We consider the task of animating 3D facial geometry from speech signal. Existing works are primarily deterministic focusing on learning a one-to-one mapping from speech signal to 3D face meshes on small datasets with limited speakers. While these models can achieve high-quality lip articulation for speakers in the training set they are unable to capture the full and diverse distribution of 3D facial motions that accompany speech in the real world. Importantly the relationship between speech and facial motion is one-to-many containing both inter-speaker and intra-speaker variations and necessitating a probabilistic approach. In this paper we identify and address key challenges that have so far limited the development of probabilistic models: lack of datasets and metrics that are suitable for training and evaluating them as well as the difficulty of designing a model that generates diverse results while remaining faithful to a strong conditioning signal as speech. We first propose large-scale benchmark datasets and metrics suitable for probabilistic modeling. Then we demonstrate a probabilistic model that achieves both diversity and fidelity to speech outperforming other methods across the proposed benchmarks. Finally we showcase useful applications of probabilistic models trained on these large-scale datasets: we can generate diverse speech-driven 3D facial motion that matches unseen speaker styles extracted from reference clips; and our synthetic meshes can be used to improve the performance of downstream audio-visual models.
Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains allowing users to hold a dialogue about given visual content. However such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example to handle high-resolution RS imagery with diverse scale changes across categories and many small objects region-level reasoning is necessary alongside holistic scene interpretation. Furthermore the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries. To address these limitations we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Furthermore it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. Leveraging this rich dataset we fine-tune our remote sensing VLM based on the LLaVA-1.5 architecture. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various remote sensing tasks e.g. image and region captioning visual question answering scene classification visually grounded conversations and referring object detection. Our codes will be open-sourced.
Limited training data is a long-standing problem for video emotion analysis (VEA). Existing works leverage the power of large-scale image datasets for transferring while failing to extract the temporal correlation of affective cues in the video. Inspired by psychology research and empirical theory we verify that the degree of emotion may vary in different segments of the video thus introducing the sentiment complementary and emotion intrinsic among temporal segments. We propose an MAE-style method for learning robust affective representation of videos via masking termed MART. First we extract the affective cues of the lexicon and verify the extracted one by computing its matching score with video content in terms of sentiment and emotion scores alongside the temporal dimension. Then with the verified cues we propose masked affective modeling to recover temporal emotion distribution. We present temporal affective complementary learning that pulls the complementary part and pushes the intrinsic one of masked multimodal features where the constraint is set with cross-modal attention among features to mask the video and recover the degree of emotion among segments. Extensive experiments on five benchmarks show the superiority of our method in video sentiment analysis video emotion recognition multimodal sentiment analysis and multimodal emotion recognition.
Curation methods for massive vision-language datasets trade off between dataset size and quality. However even the highest quality of available curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs we collect the Densely Captioned Images (DCI) dataset containing 8012 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset we hope to enable the development of new benchmarks or fine-tuning recipes for the next generation of VLMs to come.
Achieving the optimal form of Visual Question Answering mandates a profound grasp of understanding grounding and reasoning within the intersecting domains of vision and language. Traditional VQA benchmarks have predominantly focused on simplistic tasks such as counting visual attributes and object detection which do not necessitate intricate cross-modal information understanding and inference. Motivated by the need for a more comprehensive evaluation we introduce a novel dataset comprising 23781 questions derived from 10124 image-text pairs. Specifically the task of this dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. Furthermore we evaluate this VTQA dataset comparing the performance of both state-of-the-art VQA models and our proposed baseline model the Key Entity Cross-Media Reasoning Network (KECMRN). The VTQA task poses formidable challenges for traditional VQA models underscoring its intrinsic complexity. Conversely KECMRN exhibits a modest improvement signifying its potential in multimedia entity alignment and multi-step reasoning. Our analysis underscores the diversity difficulty and scale of the VTQA task compared to previous multimodal QA datasets. In conclusion we anticipate that this dataset will serve as a pivotal resource for advancing and evaluating models proficient in multimedia entity alignment multi-step reasoning and open-ended answer generation. Our dataset and code is available at https://visual-text-qa.github.io/.
Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast for untrimmed videos the sound duration start- and end-sounding time positions and visual deformation of audible objects vary significantly. Therefore we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS) which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes we also provide the bounding boxes to indicate the sounding regions. Specifically LU-AVS contains 10M mask annotations across 6.6K videos and 11M bounding box annotations across 7K videos. Compared with the existing datasets LU-AVS videos are on average 4 8 times longer with the silent duration being 3 15 times greater. Furthermore we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://yenanliu.github.io/LU-AVS/.
The capacity of existing human keypoint localization models is limited by keypoint priors provided by the training data. To alleviate this restriction and pursue more general model this work studies keypoint localization from a different perspective by reasoning locations based on keypiont clues in text descriptions. We propose LocLLM the first Large-Language Model (LLM) based keypoint localization model that takes images and text instructions as inputs and outputs the desired keypoint coordinates. LocLLM leverages the strong reasoning capability of LLM and clues of keypoint type location and relationship in textual descriptions for keypoint localization. To effectively tune LocLLM we construct localization-based instruction conversations to connect keypoint description with corresponding coordinates in input image and fine-tune the whole model in a parameter-efficient training pipeline. LocLLM shows remarkable performance on standard 2D/3D keypoint localization benchmarks. Moreover incorporating language clues into the localization makes LocLLM show superior flexibility and generalizable capability in cross dataset keypoint localization and even detecting novel type of keypoints unseen during training.
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision graphics and machine learning; it saves the designer significant time when iterating on in-the-wild objects. Recent advancements in this direction achieve relatively reliable semantic segmentation but still struggle to produce an adequate topology of the CAD model. In this work we analyze the current state of the art for that ill-posed task and identify shortcomings of existing methods. We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models and can be readily combined with different segmentation backbones. Moreover to power the surface fitting stage we propose a novel implicit neural representation of freeform surfaces driving up the performance of our overall CAD reconstruction scheme. We extensively evaluate our method on the popular ABC benchmark of CAD models and set a new state-of-the-art for that dataset. Code is available at https://github.com/YujiaLiu76/point2cad.
We present NARUTO a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning enabling high-fidelity surface reconstruction. Our approach leverages a multi-resolution hash-grid as the mapping backbone chosen for its exceptional convergence speed and capacity to capture high-frequency local features. The centerpiece of our work is the incorporation of an uncertainty learning module that dynamically quantifies reconstruction uncertainty while actively reconstructing the environment. By harnessing learned uncertainty we propose a novel uncertainty aggregation strategy for goal searching and efficient path planning. Our system autonomously explores by targeting uncertain observations and reconstructs environments with remarkable completeness and fidelity. We also demonstrate the utility of this uncertainty-aware approach by enhancing SOTA neural SLAM systems through an active ray sampling strategy. Extensive evaluations of NARUTO in various environments using an indoor scene simulator confirm its superior performance and state-of-the-art status in active reconstruction as evidenced by its impressive results on benchmark datasets like Replica and MP3D.
Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However it often suffers inferior performance compared to the feature knowledge distillation. In this paper we argue that existing logit-based methods may be sub-optimal since they only leverage the global logit output that couples multiple semantic knowledge. This may transfer ambiguous knowledge to the student and mislead its learning. To this end we propose a simple but effective method i.e. Scale Decoupled Distillation (SDD) for logit knowledge distillation. SDD decouples the global logit output into multiple local logit outputs and establishes distillation pipelines for them. This helps the student to mine and inherit fine-grained and unambiguous logit knowledge. Moreover the decoupled knowledge can be further divided into consistent and complementary logit knowledge that transfers the semantic information and sample ambiguity respectively. By increasing the weight of complementary parts SDD can guide the student to focus more on ambiguous samples improving its discrimination ability. Extensive experiments on several benchmark datasets demonstrate the effectiveness of SDD for wide teacher-student pairs especially in the fine-grained classification task. Code is available at: \href https://github.com/shicaiwei123/SDD-CVPR2024 https://github.com/shicaiwei123/SDD-CVPR2024
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR a model that can learn this task without requiring any manual annotations. To this end we construct "Self-Collages" images with various pasted objects as training samples that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple baselines and generic models such as FasterRCNN and DETR but also matches the performance of supervised counting models in some domains.
Edge segmentation is well-known to be subjective due to personalized annotation styles and preferred granularity. However most existing deterministic edge detection methods produce only a single edge map for one input image. We argue that generating multiple edge maps is more reasonable than generating a single one considering the subjectivity and ambiguity of the edges. Thus motivated in this paper we propose multiple granularity edge detection called MuGE which can produce a wide range of edge maps from approximate object contours to fine texture edges. Specifically we first propose to design an edge granularity network to estimate the edge granularity from an individual edge annotation. Subsequently to guide the generation of diversified edge maps we integrate such edge granularity into the multi-scale feature maps in the spatial domain. Meanwhile we decompose the feature maps into low-frequency and high-frequency parts where the encoded edge granularity is further fused into the high-frequency part to achieve more precise control over the details of the produced edge maps. Compared to previous methods MuGE is able to not only generate multiple edge maps at different controllable granularities but also achieve a competitive performance on the BSDS500 and Multicue benchmark datasets.