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Found 22 papers, 11 papers with code
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision
In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.
SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens
While current one-stage methods, which follow a DETR-style pipeline, achieve state-of-the-art (SOTA) performance with high-resolution inputs, we observe that this particularly benefits the estimation of individuals in smaller scales of the image (e. g., those far from the camera), but at the cost of significantly increased computation overhead.
Dissecting Multiplication in Transformers: Insights into LLMs
This work underscores the importance of explainable AI, helping to build trust in large language models and promoting their adoption in critical applications.
XPose: eXplainable Human Pose Estimation
In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation.
General Greedy De-bias Learning
Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios.
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.
Self-Regulated Learning for Egocentric Video Activity Anticipation
Future activity anticipation is a challenging problem in egocentric vision.
Greedy Gradient Ensemble for Robust Visual Question Answering
Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information.
Ranked #2 on
Visual Question Answering (VQA)
on VQA-CP
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation
To date, learning weakly supervised panoptic segmentation (WSPS) with only image-level labels remains unexplored.
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation
Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention.
Neural Architecture Search for Joint Human Parsing and Pose Estimation
With the spirit of NAS, we propose to search for an efficient network architecture (NPPNet) to tackle two tasks at the same time.
Label Decoupling Framework for Salient Object Detection
Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution.
Ranked #1 on
Saliency Detection
on HKU-IS
Correlating Edge, Pose with Parsing
Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.
Gradually Vanishing Bridge for Adversarial Domain Adaptation
On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.
SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.
Snapshot Distillation: Teacher-Student Optimization in One Generation
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Pose-driven Deep Convolutional Model for Person Re-identification
Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
Ranked #105 on
Person Re-Identification
on Market-1501
Deep Attributes Driven Multi-Camera Person Re-identification
And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data.
Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification
Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people.
