CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore to holistically address the modality gap we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques our comprehensive approach culminates in MagNet (Mask-grounded Network) an architecture that significantly outperforms prior arts on three key benchmarks (RefCOCO RefCOCO+ and G-Ref) demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released.
The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods still rely on single-view images as input leading to potential limitations e.g. limited field-of-view and ambiguity in depth. To address these problems adding another camera to better capture the shape of hands is a practical direction. However existing multi-view hand pose estimation methods suffer from two main drawbacks: 1) Requiring multi-view annotations for training which are expensive. 2) During testing the model becomes inapplicable if camera parameters/layout are not the same as those used in training. In this paper we propose a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pre-trained single-view estimator to dual views. Compared with existing multi-view training methods 1) our adaptation process is unsupervised eliminating the need for multi-view annotation. 2) Moreover our method can handle arbitrary dual-view pairs with unknown camera parameters making the model applicable to diverse camera settings. Specifically S2DHand is built on certain stereo constraints including pair-wise cross-view consensus and invariance of transformation between both views.
2) Moreover our method can handle arbitrary dual-view pairs with unknown camera parameters making the model applicable to diverse camera settings. Specifically S2DHand is built on certain stereo constraints including pair-wise cross-view consensus and invariance of transformation between both views. These two stereo constraints are used in a complementary manner to generate pseudo-labels allowing reliable adaptation. Evaluation results reveal that S2DHand achieves significant improvements on arbitrary camera pairs under both in-dataset and cross-dataset settings and outperforms existing adaptation methods with leading performance. Project page: https://github.com/ut-vision/S2DHand.
We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information which is related to the differences in coding patterns is recorded as events. The image frame and events all of which are measured in a single exposure are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website.
Event-based Vision Sensors (EVS) gain popularity in enhancing CMOS Image Sensor (CIS) video capture. Nonidealities of EVS such as pixel or readout latency can significantly influence the quality of the enhanced images and warrant dedicated consideration in the design of fusion algorithms. A novel approach for jointly computing deblurred rolling-shutter artifact corrected high-speed videos with frame rates up to 10000 FPS using inherently blurry rolling shutter CIS frames of 120 FPS to 150 FPS in conjunction with EVS data from a hybrid CIS-EVS sensor is presented. EVS pixel latency readout latency and the sensor's refractory period are explicitly incorporated into the measurement model. This inverse function problem is solved on a per-pixel manner using an optimization-based framework. The interpolated images are subsequently processed by a novel refinement network. The proposed method is evaluated using simulated and measured datasets under natural and controlled environments. Extensive experiments show reduced shadowing effect a 4 dB increment in PSNR and a 12% improvement in LPIPS score compared to state-of-the-art methods.
Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels Multi-Instance Learning (MIL) is prevailing in wVAD. However MIL suffers from insufficiency of binary supervision to model diverse abnormal patterns. Besides the coupling between abnormality and its context hinders the learning of clear abnormal event boundary. In this paper we propose prompt-enhanced MIL to detect various abnormal events while ensuring clear event boundaries. Concretely we design the abnormal-aware prompts by using abnormal class annotations together with learnable prompt which can incorporate semantic priors into video features dynamically. The detector can utilize the semantic-rich features to capture diverse abnormal patterns. In addition normal context prompt is introduced to amplify the distinction between abnormality and its context facilitating the generation of clear boundary. With the mutual enhancement of abnormal-aware and normal context prompt the model can construct discriminative representations to detect divergent anomalies without ambiguous event boundaries. Extensive experiments demonstrate our method achieves SOTA performance on three public benchmarks. The code is available at https://github.com/Junxi-Chen/PE-MIL.
Character Animation aims to generating character videos from still images through driving signals. Currently diffusion models have become the mainstream in visual generation research owing to their robust generative capabilities. However challenges persist in the realm of image-to-video especially in character animation where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data our approach can animate arbitrary characters yielding superior results in character animation compared to other image-to-video methods. Furthermore we evaluate our method on image animation benchmarks achieving state-of-the-art results.
Benefiting from large-scale pre-trained text-to-image (T2I) generative models impressive progress has been achieved in customized image generation which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combining multiple concepts. These approaches often require retraining/fine-tuning using a few images leading to time-consuming training processes and impeding their swift implementation. Furthermore the reliance on multiple images to represent a singular concept increases the difficulty of customization. To this end we propose FreeCustom a novel tuning-free method to generate customized images of multi-concept composition based on reference concepts using only one image per concept as input. Specifically we introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy that enables the generated image to access and focus more on the reference concepts. In addition MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text.
In addition MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization but is simpler. Codes can be found \href https://github.com/aim-uofa/FreeCustom  here .
Sequence-to-sequence vision-language models are showing promise but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model trained with a Query-CTC loss that marginalizes over multiple inference paths in the decoder. This allows us to model the joint distribution of tokens rather than restricting to conditional distribution as in an autoregressive model. The resulting model NARVL achieves performance on-par with its state-of-the-art autoregressive counterpart but is faster at inference time reducing from the linear complexity associated with the sequential generation of tokens to a paradigm of constant time joint inference.
Recent advances in generative AI have significantly enhanced image and video editing particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However the computational demands of diffusion-based methods are substantial often necessitating large-scale paired datasets for training and therefore challenging the deployment in real applications. To address these issues this paper breaks down the text-based video editing task into two stages. First we leverage an pre-trained text-to-image diffusion model to simultaneously edit few keyframes in an zero-shot way. Second we introduce an efficient model called MaskINT which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the edited keyframes using the structural guidance from intermediate frames. Experimental results suggest that our MaskINT achieves comparable performance with diffusion-based methodologies while significantly improve the inference time. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.
Pre-trained Vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks such as classification and retrieval. Despite their performance because improving performance on new tasks requires task-specific knowledge their adaptation is essential. While labels are needed for the adaptation acquiring them is typically expensive. To overcome this challenge active learning a method of achieving a high performance by obtaining labels for a small number of samples from experts has been studied. Active learning primarily focuses on selecting unlabeled samples for labeling and leveraging them to train models. In this study we pose the question "how can the pre-trained VLMs be adapted under the active learning framework?" In response to this inquiry we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling. Based on these observations we devise a novel active learning framework for VLMs denoted as PCB. To assess the effectiveness of our approach we conduct experiments on seven different real-world datasets and the results demonstrate that PCB surpasses conventional active learning and random sampling methods.
Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images they reduce the rich tapestry of human preference to a single overall score. However the preference results vary when humans evaluate images with different aspects. Therefore to learn the multi-dimensional human preferences we propose the Multi-dimensional Preference Score (MPS) the first multi-dimensional preference scoring model for the evaluation of text-to-image models. The MPS introduces the preference condition module upon CLIP model to learn these diverse preferences. It is trained based on our Multi-dimensional Human Preference (MHP) Dataset which comprises 918315 human preference choices across four dimensions (i.e. aesthetics semantic alignment detail quality and overall assessment) on 607541 images. The images are generated by a wide range of latest text-to-image models. The MPS outperforms existing scoring methods across 3 datasets in 4 dimensions enabling it a promising metric for evaluating and improving text-to-image generation. The model and dataset will be made publicly available to facilitate future research.
Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work we demonstrate a strikingly simple method where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest---a scanning video---which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus to perform novel-view synthesis we create a smooth camera trajectory to the target view that we wish to render and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so we obtain a highly consistent novel view synthesis outperforming the state of the art.
Accurately detecting active objects undergoing state changes is essential for comprehending human interactions and facilitating decision-making. The existing methods for active object detection (AOD) primarily rely on visual appearance of the objects within input such as changes in size shape and relationship with hands. However these visual changes can be subtle posing challenges particularly in scenarios with multiple distracting no-change instances of the same category. We observe that the state changes are often the result of an interaction being performed upon the object thus propose to use informed priors about object related plausible interactions (including semantics and visual appearance) to provide more reliable cues for AOD. Specifically we propose a knowledge aggregation procedure to integrate the aforementioned informed priors into oracle queries within the teacher decoder offering more object affordance commonsense to locate the active object. To streamline the inference process and reduce extra knowledge inputs we propose a knowledge distillation approach that encourages the student decoder to mimic the detection capabilities of the teacher decoder using the oracle query by replicating its predictions and attention. Our proposed framework achieves state-of-the-art performance on four datasets namely Ego4D Epic-Kitchens MECCANO and 100DOH which demonstrates the effectiveness of our approach in improving AOD.
Our proposed framework achieves state-of-the-art performance on four datasets namely Ego4D Epic-Kitchens MECCANO and 100DOH which demonstrates the effectiveness of our approach in improving AOD. The code and models are available at https://github.com/idejie/KAD.git.
Deep neural networks (DNNs) struggle to learn in dynamic settings because they mainly rely on static datasets. Continual learning (CL) aims to overcome this limitation by enabling DNNs to incrementally accumulate knowledge. A widely adopted scenario in CL is class-incremental learning (CIL) where DNNs are required to sequentially learn more classes. Among the various strategies in CL replay methods which revisit previous classes stand out as the only effective ones in CIL. Other strategies such as architectural modifications to segregate information across weights and protect them from change are ineffective in CIL. This is because they need additional information during testing to select the correct network parts to use. In this paper we propose NICE Neurogenesis Inspired Contextual Encoding a replay-free architectural method inspired by adult neurogenesis in the hippocampus. NICE groups neurons in the DNN based on different maturation stages and infers which neurons to use during testing without any additional signal. Through extensive experiments across 6 datasets and 3 architectures we show that NICE performs on par with or often outperforms replay methods.
NICE groups neurons in the DNN based on different maturation stages and infers which neurons to use during testing without any additional signal. Through extensive experiments across 6 datasets and 3 architectures we show that NICE performs on par with or often outperforms replay methods. We also make the case that neurons exhibit highly distinctive activation patterns for the classes in which they specialize enabling us to determine when they should be used. The code is available at https://github.com/BurakGurbuz97/NICE.
Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However only a few works consider human-scene interactions together with text conditions which is crucial for visual and physical realism. This paper focuses on the task of generating human motions in 3D indoor scenes given text descriptions of the human-scene interactions. This task presents challenges due to the multimodality nature of text scene and motion as well as the need for spatial reasoning. To address these challenges we propose a new approach that decomposes the complex problem into two more manageable sub-problems: (1) language grounding of the target object and (2) object-centric motion generation. For language grounding of the target object we leverage the power of large language models. For motion generation we design an object-centric scene representation for the generative model to focus on the target object thereby reducing the scene complexity and facilitating the modeling of the relationship between human motions and the object. Experiments demonstrate the better motion quality of our approach compared to baselines and validate our design choices. Code will be available at https://zju3dv.github.io/text_scene_motion.
This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs potentially limiting their applicability to new and evolving architectures. To our knowledge we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework X-Ray Distillation with Object-Complete Frames suitable for both supervised and semi-supervised settings that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames creating Object-Complete frames that represent objects from multiple viewpoints thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher which processes simple and informative Object-Complete frames effectively offering a comprehensive view of objects as if seen through X-ray vision.
This technique encourages the strong Student model to emulate the behavior of the weaker Teacher which processes simple and informative Object-Complete frames effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets even with default hyperparameters. Code for Object-Complete frames is available here: https://github.com/sakharok13/X-Ray-Teacher-Patching-Tools.
Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore we introduce a global-to-local quantization mechanism which distills knowledge from stable global (clip-level) features into local (frame-level) ones to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance eg +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone.
Active recognition which allows intelligent agents to explore observations for better recognition performance serves as a prerequisite for various embodied AI tasks such as grasping navigation and room arrangements. Given the evolving environment and the multitude of object classes it is impractical to include all possible classes during the training stage. In this paper we aim at advancing active open-vocabulary recognition empowering embodied agents to actively perceive and classify arbitrary objects. However directly adopting recent open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) poses its unique challenges. Specifically we observe that CLIP's performance is heavily affected by the viewpoint and occlusions compromising its reliability in unconstrained embodied perception scenarios. Further the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features without relying on class-specific knowledge.