CVPR 2024 Papers

Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionality of motion model can actually be leveraged to our advantage as sufficiently long trajectories identify the underlying motion uniquely in practice. Consequently we propose to learn a mapping from trajectories to embedding vectors that represent the generating motion. The obtained trajectory embeddings are useful for clustering multiple observed motions but are also trained to contain sufficient information to recover the parameters of the underlying motion by utilizing a geometric loss. We therefore are able to use only weak supervision from given motion segmentation to train this mapping. The entire algorithm consisting of trajectory embedding clustering and motion parameter estimation is highly efficient. We conduct experiments on the Hopkins155 Hopkins12 and KT3DMoSeg datasets and show state-of-the-art performance of our proposed method for trajectory-based motion segmentation on full sequences and its competitiveness on the occluded sequences. Project page: https://ylochman.github.io/trajectory-embedding.

Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However many VLMs rely on proprietary data and are not open-source which restricts the use of white-box approaches for fine-tuning. As such we aim to develop a black-box approach to optimize VLMs through natural language prompts thereby avoiding the need to access model parameters feature embeddings or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.

Autonomous driving (AD) has made significant strides in recent years. However existing frameworks struggle to interpret and execute spontaneous user instructions such as "overtake the car ahead." Large Language Models (LLMs) have demonstrated impressive reasoning capabilities showing potential to bridge this gap. In this paper we present LaMPilot a novel framework that integrates LLMs into AD systems enabling them to follow user instructions by generating code that leverages established functional primitives. We also introduce LaMPilot-Bench the first benchmark dataset specifically designed to quantitatively evaluate the efficacy of language model programs in AD. Adopting the LaMPilot framework we conduct extensive experiments to assess the performance of off-the-shelf LLMs on LaMPilot-Bench. Our results demonstrate the potential of LLMs in handling diverse driving scenarios and following user instructions in driving. To facilitate further research in this area we release our code and data at GitHub.com/PurdueDigitalTwin/LaMPilot.

In a recent strongly emergent literature on few-shot CLIP adaptation Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this work we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline in which the linear classifier weights are learnable functions of the text embedding with class-wise multipliers blending image and text knowledge. As our objective function depends on two types of variables i.e. the class visual prototypes and the learnable blending parameters we propose a computationally efficient block coordinate Majorize-Minimize (MM) descent algorithm. In our full-batch MM optimizer which we coin LP++ step sizes are implicit unlike standard gradient descent practices where learning rates are intensively searched over validation sets. By examining the mathematical properties of our loss (e.g. Lipschitz gradient continuity) we build majorizing functions yielding data-driven learning rates and derive approximations of the loss's minima which provide data-informed initialization of the variables. Our image-language objective function along with these non-trivial optimization insights and ingredients yields surprisingly highly competitive few-shot CLIP performances. Furthermore LP++ operates in black-box relaxes intensive validation searches for the optimization hyper-parameters and runs orders-of-magnitudes faster than state-of-the-art few-shot CLIP adaptation methods. Our code is available at: https://github.com/FereshteShakeri/FewShot-CLIP-Strong-Baseline.git.

Deep learning models particularly those based on transformers often employ numerous stacked structures which possess identical architectures and perform similar functions. While effective this stacking paradigm leads to a substantial increase in the number of parameters pos- ing challenges for practical applications. In today's land- scape of increasingly large models stacking depth can even reach dozens further exacerbating this issue. To miti- gate this problem we introduce LORS (LOw-rank Residual Structure). LORS allows stacked modules to share the majority of parameters requiring a much smaller num- ber of unique ones per module to match or even surpass the performance of using entirely distinct ones thereby significantly reducing parameter usage. We validate our method by applying it to the stacked decoders of a query- based object detector and conduct extensive experiments on the widely used MS COCO dataset. Experimental re- sults demonstrate the effectiveness of our method as even with a 70% reduction in the parameters of the decoder our method still enables the model to achieve comparable or even better performance than its original.

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

Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost we present LEOD the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example on Gen1 it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available reaching new state-of-the-art results. Finally we show that our method readily scales to improve larger detectors as well. Code is released at https://github.com/Wuziyi616/LEOD.

Visual odometry estimates the motion of a moving camera based on visual input. Existing methods mostly focusing on two-view point tracking often ignore the rich temporal context in the image sequence thereby overlooking the global motion patterns and providing no assessment of the full trajectory reliability. These shortcomings hinder performance in scenarios with occlusion dynamic objects and low-texture areas. To address these challenges we present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual inter-track and temporal cues with mindfully selected anchors for dynamic track estimation. Moreover LEAP's temporal probabilistic formulation integrates distribution updates into a learnable iterative refinement module to reason about point-wise uncertainty. Based on these traits we develop LEAP-VO a robust visual odometry system adept at handling occlusions and dynamic scenes. Our mindful integration showcases a novel practice by employing long-term point tracking as the front-end. Extensive experiments demonstrate that the proposed pipeline significantly outperforms existing baselines across various visual odometry benchmarks.

The remarkable success of "pretrain-then-finetune" paradigm has led to a proliferation of available pre-trained models for vision tasks. This surge presents a significant challenge in efficiently choosing the most suitable pre-trained models for downstream tasks. The critical aspect of this challenge lies in effectively predicting the model transferability by considering the underlying fine-tuning dynamics. Existing methods often model fine-tuning dynamics in feature space with linear transformations which do not precisely align with the fine-tuning objective and fail to grasp the essential nonlinearity from optimization. To this end we present LEAD a finetuning-aligned approach based on the network output of logits. LEAD proposes a theoretical framework to model the optimization process and derives an ordinary differential equation (ODE) to depict the nonlinear evolution toward the final logit state. Additionally we design a class-aware decomposition method to consider the varying evolution dynamics across classes and further ensure practical applicability. Integrating the closely aligned optimization objective and nonlinear modeling capabilities derived from the differential equation our method offers a concise solution to effectively bridge the optimization gap in a single step bypassing the lengthy fine-tuning process. The comprehensive experiments on 24 supervised and self-supervised pre-trained models across 10 downstream datasets demonstrate impressive performances and showcase its broad adaptability even in low-data scenarios.

Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution our approach augments expert states such that the augmented state is aware of learner state distribution. Our method applied to urban traffic simulation demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.

Removing noise from images a.k.a image denoising can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world datasets recent denoising networks struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a denoising network to new noise distribution. Instead in this work we shift our attention to the input noise itself for adaptation rather than adapting a network. Thus we keep a pretrained network frozen and adapt an input noise to capture the fine-grained deviations. As such we propose a new denoising algorithm dubbed Learning-to-Adapt-Noise (LAN) where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently the proposed framework exhibits performance improvement on images with unseen noise displaying the potential of the proposed research direction.

Due to the omnipresence of Neural Radiance Fields (NeRFs) the interest towards editable implicit 3D representations has surged over the last years. However editing implicit or hybrid representations as used for NeRFs is difficult due to the entanglement of appearance and geometry encoded in the model parameters. Despite these challenges recent research has shown first promising steps towards photorealistic and non-photorealistic appearance edits. The main open issues of related work include limited interactivity a lack of support for local edits and large memory requirements rendering them less useful in practice. We address these limitations with LAENeRF a unified framework for photorealistic and non-photorealistic appearance editing of NeRFs. To tackle local editing we leverage a voxel grid as starting point for region selection. We learn a mapping from expected ray terminations to final output color which can optionally be supervised by a style loss resulting in a framework which can perform photorealistic and non-photorealistic appearance editing of selected regions. Relying on a single point per ray for our mapping we limit memory requirements and enable fast optimization. To guarantee interactivity we compose the output color using a set of learned modifiable base colors composed with additive layer mixing. Compared to concurrent work LAENeRF enables recoloring and stylization while keeping processing time low. Furthermore we demonstrate that our approach surpasses baseline methods both quantitatively and qualitatively.

Deep neural networks have shown great success in representation learning. Deep neural networks have shown great success in representation learning. However when learning with noisy labels (LNL) they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective method named Learning to Bootstrap (L2B) which enables models to bootstrap themselves using their own predictions without being adversely affected by erroneous pseudo-labels. It achieves this by dynamically adjusting the importance weight between real observed and generated labels as well as between different samples through meta-learning. Unlike existing instance reweighting methods the key to our method lies in a new versatile objective that enables implicit relabeling concurrently leading to significant improvements without incurring additional costs. L2B offers several benefits over the baseline methods. It yields more robust models that are less susceptible to the impact of noisy labels by guiding the bootstrapping procedure more effectively. It better exploits the valuable information contained in corrupted instances by adapting the weights of both instances and labels. Furthermore L2B is compatible with existing LNL methods and delivers competitive results spanning natural and medical imaging tasks including classification and segmentation under both synthetic and real-world noise. Extensive experiments demonstrate that our method effectively mitigates the challenges of noisy labels often necessitating few to no validation samples and is well generalized to other tasks such as image segmentation. This not only positions it as a robust complement to existing LNL techniques but also underscores its practical applicability. The code and models are available at https://github.com/yuyinzhou/l2b.

Since its proposal Neural Radiance Fields (NeRF) has achieved great success in related tasks mainly adopting the hierarchical volume sampling (HVS) strategy for volume rendering. However the HVS of NeRF approximates distributions using piecewise constant functions which provides a relatively rough estimation. Based on the observation that a well-trained weight function w(t) and the L_0 distance between points and the surface have very high similarity we propose L_0-Sampler by incorporating the L_0 model into w(t) to guide the sampling process. Specifically we propose using piecewise exponential functions rather than piecewise constant functions for interpolation which can not only approximate quasi-L_0 weight distributions along rays quite well but can be easily implemented with a few lines of code change without additional computational burden. Stable performance improvements can be achieved by applying L_0-Sampler to NeRF and related tasks like 3D reconstruction. Code is available at https://ustc3dv.github.io/L0-Sampler/.

In this paper we address the challenge of making ViT models more robust to unseen affine transformations. Such robustness becomes useful in various recognition tasks such as face recognition when image alignment failures occur. We propose a novel method called KP-RPE which leverages key points (e.g.facial landmarks) to make ViT more resilient to scale translation and pose variations. We begin with the observation that Relative Position Encoding (RPE) is a good way to bring affine transform generalization to ViTs. RPE however can only inject the model with prior knowledge that nearby pixels are more important than far pixels. Keypoint RPE (KP-RPE) is an extension of this principle where the significance of pixels is not solely dictated by their proximity but also by their relative positions to specific keypoints within the image. By anchoring the significance of pixels around keypoints the model can more effectively retain spatial relationships even when those relationships are disrupted by affine transformations. We show the merit of KP-RPE in face and gait recognition. The experimental results demonstrate the effectiveness in improving face recognition performance from low-quality images particularly where alignment is prone to failure. Code and pre-trained models are available.

Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.

End-to-end autonomous driving recently emerged as a promising research direction to target autonomy from a full-stack perspective. Along this line many of the latest works follow an open-loop evaluation setting on nuScenes to study the planning behavior. In this paper we delve deeper into the problem by conducting thorough analyses and demystifying more devils in the details. We initially observed that the nuScenes dataset characterized by relatively simple driving scenarios leads to an under-utilization of perception information in end-to-end models incorporating ego status such as the ego vehicle's velocity. These models tend to rely predominantly on the ego vehicle's status for future path planning. Beyond the limitations of the dataset we also note that current metrics do not comprehensively assess the planning quality leading to potentially biased conclusions drawn from existing benchmarks. To address this issue we introduce a new metric to evaluate whether the predicted trajectories adhere to the road. We further propose a simple baseline able to achieve competitive results without relying on perception annotations. Given the current limitations on the benchmark and metrics we suggest the community reassess relevant prevailing research and be cautious about whether the continued pursuit of state-of-the-art would yield convincing and universal conclusions. Code and models are available at https://github.com/NVlabs/BEV-Planner.

We present Intrinsic Image Diffusion a generative model for appearance decomposition of indoor scenes. Given a single input view we sample multiple possible material explanations represented as albedo roughness and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue we advocate for a probabilistic formulation where instead of attempting to directly predict the true material properties we employ a conditional generative model to sample from the solution space. Furthermore we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper more consistent and more detailed materials outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

Spike cameras a novel neuromorphic visual sensor can capture full-time spatial information through spike stream offering ultra-high temporal resolution and an extensive dynamic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless due to disparities in data modality and information characteristics compared to frame stream and event stream the current lack of efficient AC methods has made it challenging for spike cameras to adapt to intricate real-world conditions. To address this challenge we introduce a spike-based autofocus framework that includes a spike-specific focus measure called spike dispersion (SD) which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally the framework integrates a fast search strategy called spike-based golden fast search (SGFS) allowing rapid focal positioning without the need for a complete focus range traversal. To validate the performance of our method we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.