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Found 10 papers, 6 papers with code
Agent Journey Beyond RGB: Unveiling Hybrid Semantic-Spatial Environmental Representations for Vision-and-Language Navigation
SUSA includes a Textual Semantic Understanding (TSU) module, which narrows the modality gap between instructions and environments by generating and associating the descriptions of environmental landmarks in the agent's immediate surroundings.
Ranked #1 on
Visual Navigation
on R2R
Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrieval
By replacing a single text query with a series of text proxies, TV-ProxyNet not only broadens the query scope but also achieves a more precise expansion.
Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data
Dynamic facial expression recognition (DFER) infers emotions from the temporal evolution of expressions, unlike static facial expression recognition (SFER), which relies solely on a single snapshot.
Ranked #2 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Seeing is Believing? Enhancing Vision-Language Navigation using Visual Perturbations
Autonomous navigation for an embodied agent guided by natural language instructions remains a formidable challenge in vision-and-language navigation (VLN).
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering
Unsupervised representation learning for image clustering is essential in computer vision.
Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning
The crucial issue in this task is to model the global and the local matching between the image and different languages.
Compact Bidirectional Transformer for Image Captioning
In this paper, we introduce a Compact Bidirectional Transformer model for image captioning that can leverage bidirectional context implicitly and explicitly while the decoder can be executed parallelly.
Semi-Autoregressive Transformer for Image Captioning
To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel.
More Grounded Image Captioning by Distilling Image-Text Matching Model
To improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong supervision.
Quality-aware Unpaired Image-to-Image Translation
To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level.
